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  • Data Quality and Master Data Management Resources

    - by Dejan Sarka
    Many companies or organizations do regular data cleansing. When you cleanse the data, the data quality goes up to some higher level. The data quality level is determined by the amount of work invested in the cleansing. As time passes, the data quality deteriorates, and you need to repeat the cleansing process. If you spend an equal amount of effort as you did with the previous cleansing, you can expect the same level of data quality as you had after the previous cleansing. And then the data quality deteriorates over time again, and the cleansing process starts over and over again. The idea of Data Quality Services is to mitigate the cleansing process. While the amount of time you need to spend on cleansing decreases, you will achieve higher and higher levels of data quality. While cleansing, you learn what types of errors to expect, discover error patterns, find domains of correct values, etc. You don’t throw away this knowledge. You store it and use it to find and correct the same issues automatically during your next cleansing process. The following figure shows this graphically. The idea of master data management, which you can perform with Master Data Services (MDS), is to prevent data quality from deteriorating. Once you reach a particular quality level, the MDS application—together with the defined policies, people, and master data management processes—allow you to maintain this level permanently. This idea is shown in the following picture. OK, now you know what DQS and MDS are about. You can imagine the importance on maintaining the data quality. Here are some resources that help you preparing and executing the data quality (DQ) and master data management (MDM) activities. Books Dejan Sarka and Davide Mauri: Data Quality and Master Data Management with Microsoft SQL Server 2008 R2 – a general introduction to MDM, MDS, and data profiling. Matching explained in depth. Dejan Sarka, Matija Lah and Grega Jerkic: MCTS Self-Paced Training Kit (Exam 70-463): Building Data Warehouses with Microsoft SQL Server 2012 – I wrote quite a few chapters about DQ and MDM, and introduced also SQL Server 2012 DQS. Thomas Redman: Data Quality: The Field Guide – you should start with this book. Thomas Redman is the father of DQ and MDM. Tyler Graham: Microsoft SQL Server 2012 Master Data Services – MDS in depth from a product team mate. Arkady Maydanchik: Data Quality Assessment – data profiling in depth. Tamraparni Dasu, Theodore Johnson: Exploratory Data Mining and Data Cleaning – advanced data profiling with data mining. Forthcoming presentations I am presenting a DQS and MDM seminar at PASS SQL Rally Amsterdam 2013: Wednesday, November 6th, 2013: Enterprise Information Management with SQL Server 2012 – a good kick start to your first DQ and / or MDM project. Courses Data Quality and Master Data Management with SQL Server 2012 – I wrote a 2-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Start improving the quality of your data now!

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  • How do i delete these files?

    - by user107277
    I ran this command sudo find / -type d -name '*Trash*' | sudo xargs du -h | sort This was the output: 100M /root/.local/share/Trash/files/recup_dir.2.30 100M /root/.local/share/Trash/files/recup_dir.2.72 100M /root/.local/share/Trash/files/recup_dir.32 101M /root/.local/share/Trash/files/recup_dir.2.27 101M /root/.local/share/Trash/files/recup_dir.29 103M /root/.local/share/Trash/files/recup_dir.2.7 103M /root/.local/share/Trash/files/recup_dir.9 103M /root/.local/share/Trash/files/recup_dir.93 106M /root/.local/share/Trash/files/recup_dir.187 106M /root/.local/share/Trash/files/recup_dir.71 107M /root/.local/share/Trash/files/recup_dir.131 107M /root/.local/share/Trash/files/recup_dir.136 107M /root/.local/share/Trash/files/recup_dir.2.46 107M /root/.local/share/Trash/files/recup_dir.51 108M /root/.local/share/Trash/files/recup_dir.106 108M /root/.local/share/Trash/files/recup_dir.2.78 108M /root/.local/share/Trash/files/recup_dir.52 109M /root/.local/share/Trash/files/recup_dir.2.32 109M /root/.local/share/Trash/files/recup_dir.34 110M /root/.local/share/Trash/files/recup_dir.2.28 110M /root/.local/share/Trash/files/recup_dir.2.53 110M /root/.local/share/Trash/files/recup_dir.30 110M /root/.local/share/Trash/files/recup_dir.55 110M /root/.local/share/Trash/files/recup_dir.89 112M /root/.local/share/Trash/files/recup_dir.2.31 112M /root/.local/share/Trash/files/recup_dir.33 114M /root/.local/share/Trash/files/recup_dir.2.29 114M /root/.local/share/Trash/files/recup_dir.2.74 114M /root/.local/share/Trash/files/recup_dir.31 115M /root/.local/share/Trash/files/recup_dir.125 117M /root/.local/share/Trash/files/recup_dir.83 118M /root/.local/share/Trash/files/recup_dir.105 118M /root/.local/share/Trash/files/recup_dir.2.70 119M /root/.local/share/Trash/files/recup_dir.133 1.1G /root/.local/share/Trash/files/recup_dir.148 11M /root/.local/share/Trash/files/recup_dir.179 1.1M /root/.local/share/Trash/info 122M /root/.local/share/Trash/files/recup_dir.80 124M /root/.local/share/Trash/files/recup_dir.137 125G /root/.local/share/Trash 125G /root/.local/share/Trash/files 125M /root/.local/share/Trash/files/recup_dir.2.49 129M /root/.local/share/Trash/files/recup_dir.153 1.2G /root/.local/share/Trash/files/recup_dir.165 1.2G /root/.local/share/Trash/files/recup_dir.166 12K /media/A80E1DE60E1DAE76/.Trash-1000/files 12M /root/.local/share/Trash/files/recup_dir.178 12M /root/.local/share/Trash/files/recup_dir.180 12M /root/.local/share/Trash/files/recup_dir.181 130M /root/.local/share/Trash/files/recup_dir.85 137M /root/.local/share/Trash/files/recup_dir.2.5 137M /root/.local/share/Trash/files/recup_dir.7 137M /root/.local/share/Trash/files/recup_dir.76 13M /root/.local/share/Trash/files/recup_dir.143 13M /root/.local/share/Trash/files/recup_dir.18 13M /root/.local/share/Trash/files/recup_dir.182 13M /root/.local/share/Trash/files/recup_dir.2.16 13M /root/.local/share/Trash/files/recup_dir.2.2 13M /root/.local/share/Trash/files/recup_dir.4 140M /root/.local/share/Trash/files/recup_dir.2.77 145M /root/.local/share/Trash/files/recup_dir.2.63 147M /root/.local/share/Trash/files/recup_dir.2.43 147M /root/.local/share/Trash/files/recup_dir.45 148M /root/.local/share/Trash/files/recup_dir.84 149M /root/.local/share/Trash/files/recup_dir.160 149M /root/.local/share/Trash/files/recup_dir.2.79 1.4G /root/.local/share/Trash/files/recup_dir.191 150M /root/.local/share/Trash/files/recup_dir.2.26 150M /root/.local/share/Trash/files/recup_dir.28 153M /root/.local/share/Trash/files/recup_dir.64 153M /root/.local/share/Trash/files/recup_dir.78 154M /root/.local/share/Trash/files/recup_dir.107 155M /root/.local/share/Trash/files/recup_dir.2.80 155M /root/.local/share/Trash/files/recup_dir.79 15M /root/.local/share/Trash/files/recup_dir.151 162M /root/.local/share/Trash/files/recup_dir.65 163M /root/.local/share/Trash/files/recup_dir.82 164M /root/.local/share/Trash/files/recup_dir.104 165M /root/.local/share/Trash/files/recup_dir.2.39 165M /root/.local/share/Trash/files/recup_dir.41 168M /root/.local/share/Trash/files/recup_dir.2.62 16M /root/.local/share/Trash/files/recup_dir.171 170M /root/.local/share/Trash/files/recup_dir.135 170M /root/.local/share/Trash/files/recup_dir.159 171M /root/.local/share/Trash/files/recup_dir.91 172M /root/.local/share/Trash/files/recup_dir.2.41 172M /root/.local/share/Trash/files/recup_dir.43 175M /root/.local/share/Trash/files/recup_dir.2.33 175M /root/.local/share/Trash/files/recup_dir.35 176M /root/.local/share/Trash/files/recup_dir.2.76 179M /root/.local/share/Trash/files/recup_dir.2.38 179M /root/.local/share/Trash/files/recup_dir.40 179M /root/.local/share/Trash/files/recup_dir.61 1.7G /root/.local/share/Trash/files/recup_dir.167 17M /root/.local/share/Trash/files/recup_dir.172 180M /root/.local/share/Trash/files/recup_dir.186 181M /root/.local/share/Trash/files/recup_dir.2.71 182M /root/.local/share/Trash/files/recup_dir.158 183M /root/.local/share/Trash/files/recup_dir.2.59 185M /root/.local/share/Trash/files/recup_dir.123 189M /root/.local/share/Trash/files/recup_dir.92 18M /root/.local/share/Trash/files/recup_dir.142 18M /root/.local/share/Trash/files/recup_dir.149 18M /root/.local/share/Trash/files/recup_dir.150 18M /root/.local/share/Trash/files/recup_dir.152 18M /root/.local/share/Trash/files/recup_dir.173 18M /root/.local/share/Trash/files/recup_dir.177 191M /root/.local/share/Trash/files/recup_dir.147 193M /root/.local/share/Trash/files/recup_dir.102 195M /root/.local/share/Trash/files/recup_dir.73 196M /root/.local/share/Trash/files/recup_dir.94 198M /root/.local/share/Trash/files/recup_dir.2.58 19M /root/.local/share/Trash/files/recup_dir.175 19M /root/.local/share/Trash/files/recup_dir.176 205M /root/.local/share/Trash/files/recup_dir.108 206M /root/.local/share/Trash/files/recup_dir.56 206M /root/.local/share/Trash/files/recup_dir.60 207M /root/.local/share/Trash/files/recup_dir.2.55 209M /root/.local/share/Trash/files/recup_dir.90 2.0G /root/.local/share/Trash/files/recup_dir.190 20K /media/A80E1DE60E1DAE76/.Trash-1000/info 20M /root/.local/share/Trash/files/recup_dir.17 20M /root/.local/share/Trash/files/recup_dir.2.15 210M /root/.local/share/Trash/files/recup_dir.121 211M /root/.local/share/Trash/files/recup_dir.134 212M /root/.local/share/Trash/files/recup_dir.57 21M /root/.local/share/Trash/files/recup_dir.174 223M /root/.local/share/Trash/files/recup_dir.88 225M /root/.local/share/Trash/files/recup_dir.118 230M /root/.local/share/Trash/files/recup_dir.87 232M /root/.local/share/Trash/files/recup_dir.66 235M /root/.local/share/Trash/files/recup_dir.139 236M /root/.local/share/Trash/files/recup_dir.97 238M /root/.local/share/Trash/files/recup_dir.2.54 240M /root/.local/share/Trash/files/recup_dir.163 241M /root/.local/share/Trash/files/recup_dir.126 242M /root/.local/share/Trash/files/recup_dir.2.81 243M /root/.local/share/Trash/files/recup_dir.156 244M 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/root/.local/share/Trash/files/recup_dir.26 37M /root/.local/share/Trash/files/recup_dir.112 390M /root/.local/share/Trash/files/recup_dir.162 398M /root/.local/share/Trash/files/recup_dir.2.67 39M /root/.local/share/Trash/files/recup_dir.145 401M /root/.local/share/Trash/files/recup_dir.2.52 402M /root/.local/share/Trash/files/recup_dir.54 408M /root/.local/share/Trash/files/recup_dir.2.40 408M /root/.local/share/Trash/files/recup_dir.42 4.0K /home/daniel/.local/share/Trash 40K /media/A80E1DE60E1DAE76/.Trash-1000 41M /root/.local/share/Trash/files/recup_dir.13 41M /root/.local/share/Trash/files/recup_dir.2.11 428M /root/.local/share/Trash/files/recup_dir.2.61 434M /root/.local/share/Trash/files/recup_dir.2.36 434M /root/.local/share/Trash/files/recup_dir.38 43M /root/.local/share/Trash/files/recup_dir.19 43M /root/.local/share/Trash/files/recup_dir.2.17 43M /root/.local/share/Trash/files/recup_dir.53 440M /root/.local/share/Trash/files/recup_dir.157 448M 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/root/.local/share/Trash/files/recup_dir.58 815M /root/.local/share/Trash/files/recup_dir.2.66 818M /root/.local/share/Trash/files/recup_dir.2.56 82M /root/.local/share/Trash/files/recup_dir.2.44 82M /root/.local/share/Trash/files/recup_dir.46 835M /root/.local/share/Trash/files/recup_dir.68 84M /root/.local/share/Trash/files/recup_dir.189 860M /root/.local/share/Trash/files/recup_dir.161 86M /root/.local/share/Trash/files/recup_dir.117 86M /root/.local/share/Trash/files/recup_dir.2.69 86M /root/.local/share/Trash/files/recup_dir.2.75 90M /root/.local/share/Trash/files/recup_dir.74 924M /root/.local/share/Trash/files/recup_dir.184 94M /root/.local/share/Trash/files/recup_dir.81 95M /root/.local/share/Trash/files/recup_dir.100 96M /root/.local/share/Trash/files/recup_dir.2.6 96M /root/.local/share/Trash/files/recup_dir.2.65 96M /root/.local/share/Trash/files/recup_dir.8 97M /root/.local/share/Trash/files/recup_dir.2.50 97M /root/.local/share/Trash/files/recup_dir.67 97M /root/.local/share/Trash/files/recup_dir.72 98M /root/.local/share/Trash/files/recup_dir.96 99M /root/.local/share/Trash/files/recup_dir.48 How do I delete these files?

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  • Zen and the Art of File and Folder Organization

    - by Mark Virtue
    Is your desk a paragon of neatness, or does it look like a paper-bomb has gone off? If you’ve been putting off getting organized because the task is too huge or daunting, or you don’t know where to start, we’ve got 40 tips to get you on the path to zen mastery of your filing system. For all those readers who would like to get their files and folders organized, or, if they’re already organized, better organized—we have compiled a complete guide to getting organized and staying organized, a comprehensive article that will hopefully cover every possible tip you could want. Signs that Your Computer is Poorly Organized If your computer is a mess, you’re probably already aware of it.  But just in case you’re not, here are some tell-tale signs: Your Desktop has over 40 icons on it “My Documents” contains over 300 files and 60 folders, including MP3s and digital photos You use the Windows’ built-in search facility whenever you need to find a file You can’t find programs in the out-of-control list of programs in your Start Menu You save all your Word documents in one folder, all your spreadsheets in a second folder, etc Any given file that you’re looking for may be in any one of four different sets of folders But before we start, here are some quick notes: We’re going to assume you know what files and folders are, and how to create, save, rename, copy and delete them The organization principles described in this article apply equally to all computer systems.  However, the screenshots here will reflect how things look on Windows (usually Windows 7).  We will also mention some useful features of Windows that can help you get organized. Everyone has their own favorite methodology of organizing and filing, and it’s all too easy to get into “My Way is Better than Your Way” arguments.  The reality is that there is no perfect way of getting things organized.  When I wrote this article, I tried to keep a generalist and objective viewpoint.  I consider myself to be unusually well organized (to the point of obsession, truth be told), and I’ve had 25 years experience in collecting and organizing files on computers.  So I’ve got a lot to say on the subject.  But the tips I have described here are only one way of doing it.  Hopefully some of these tips will work for you too, but please don’t read this as any sort of “right” way to do it. At the end of the article we’ll be asking you, the reader, for your own organization tips. Why Bother Organizing At All? For some, the answer to this question is self-evident. And yet, in this era of powerful desktop search software (the search capabilities built into the Windows Vista and Windows 7 Start Menus, and third-party programs like Google Desktop Search), the question does need to be asked, and answered. I have a friend who puts every file he ever creates, receives or downloads into his My Documents folder and doesn’t bother filing them into subfolders at all.  He relies on the search functionality built into his Windows operating system to help him find whatever he’s looking for.  And he always finds it.  He’s a Search Samurai.  For him, filing is a waste of valuable time that could be spent enjoying life! It’s tempting to follow suit.  On the face of it, why would anyone bother to take the time to organize their hard disk when such excellent search software is available?  Well, if all you ever want to do with the files you own is to locate and open them individually (for listening, editing, etc), then there’s no reason to ever bother doing one scrap of organization.  But consider these common tasks that are not achievable with desktop search software: Find files manually.  Often it’s not convenient, speedy or even possible to utilize your desktop search software to find what you want.  It doesn’t work 100% of the time, or you may not even have it installed.  Sometimes its just plain faster to go straight to the file you want, if you know it’s in a particular sub-folder, rather than trawling through hundreds of search results. Find groups of similar files (e.g. all your “work” files, all the photos of your Europe holiday in 2008, all your music videos, all the MP3s from Dark Side of the Moon, all your letters you wrote to your wife, all your tax returns).  Clever naming of the files will only get you so far.  Sometimes it’s the date the file was created that’s important, other times it’s the file format, and other times it’s the purpose of the file.  How do you name a collection of files so that they’re easy to isolate based on any of the above criteria?  Short answer, you can’t. Move files to a new computer.  It’s time to upgrade your computer.  How do you quickly grab all the files that are important to you?  Or you decide to have two computers now – one for home and one for work.  How do you quickly isolate only the work-related files to move them to the work computer? Synchronize files to other computers.  If you have more than one computer, and you need to mirror some of your files onto the other computer (e.g. your music collection), then you need a way to quickly determine which files are to be synced and which are not.  Surely you don’t want to synchronize everything? Choose which files to back up.  If your backup regime calls for multiple backups, or requires speedy backups, then you’ll need to be able to specify which files are to be backed up, and which are not.  This is not possible if they’re all in the same folder. Finally, if you’re simply someone who takes pleasure in being organized, tidy and ordered (me! me!), then you don’t even need a reason.  Being disorganized is simply unthinkable. Tips on Getting Organized Here we present our 40 best tips on how to get organized.  Or, if you’re already organized, to get better organized. Tip #1.  Choose Your Organization System Carefully The reason that most people are not organized is that it takes time.  And the first thing that takes time is deciding upon a system of organization.  This is always a matter of personal preference, and is not something that a geek on a website can tell you.  You should always choose your own system, based on how your own brain is organized (which makes the assumption that your brain is, in fact, organized). We can’t instruct you, but we can make suggestions: You may want to start off with a system based on the users of the computer.  i.e. “My Files”, “My Wife’s Files”, My Son’s Files”, etc.  Inside “My Files”, you might then break it down into “Personal” and “Business”.  You may then realize that there are overlaps.  For example, everyone may want to share access to the music library, or the photos from the school play.  So you may create another folder called “Family”, for the “common” files. You may decide that the highest-level breakdown of your files is based on the “source” of each file.  In other words, who created the files.  You could have “Files created by ME (business or personal)”, “Files created by people I know (family, friends, etc)”, and finally “Files created by the rest of the world (MP3 music files, downloaded or ripped movies or TV shows, software installation files, gorgeous desktop wallpaper images you’ve collected, etc).”  This system happens to be the one I use myself.  See below:  Mark is for files created by meVC is for files created by my company (Virtual Creations)Others is for files created by my friends and familyData is the rest of the worldAlso, Settings is where I store the configuration files and other program data files for my installed software (more on this in tip #34, below). Each folder will present its own particular set of requirements for further sub-organization.  For example, you may decide to organize your music collection into sub-folders based on the artist’s name, while your digital photos might get organized based on the date they were taken.  It can be different for every sub-folder! Another strategy would be based on “currentness”.  Files you have yet to open and look at live in one folder.  Ones that have been looked at but not yet filed live in another place.  Current, active projects live in yet another place.  All other files (your “archive”, if you like) would live in a fourth folder. (And of course, within that last folder you’d need to create a further sub-system based on one of the previous bullet points). Put some thought into this – changing it when it proves incomplete can be a big hassle!  Before you go to the trouble of implementing any system you come up with, examine a wide cross-section of the files you own and see if they will all be able to find a nice logical place to sit within your system. Tip #2.  When You Decide on Your System, Stick to It! There’s nothing more pointless than going to all the trouble of creating a system and filing all your files, and then whenever you create, receive or download a new file, you simply dump it onto your Desktop.  You need to be disciplined – forever!  Every new file you get, spend those extra few seconds to file it where it belongs!  Otherwise, in just a month or two, you’ll be worse off than before – half your files will be organized and half will be disorganized – and you won’t know which is which! Tip #3.  Choose the Root Folder of Your Structure Carefully Every data file (document, photo, music file, etc) that you create, own or is important to you, no matter where it came from, should be found within one single folder, and that one single folder should be located at the root of your C: drive (as a sub-folder of C:\).  In other words, do not base your folder structure in standard folders like “My Documents”.  If you do, then you’re leaving it up to the operating system engineers to decide what folder structure is best for you.  And every operating system has a different system!  In Windows 7 your files are found in C:\Users\YourName, whilst on Windows XP it was C:\Documents and Settings\YourName\My Documents.  In UNIX systems it’s often /home/YourName. These standard default folders tend to fill up with junk files and folders that are not at all important to you.  “My Documents” is the worst offender.  Every second piece of software you install, it seems, likes to create its own folder in the “My Documents” folder.  These folders usually don’t fit within your organizational structure, so don’t use them!  In fact, don’t even use the “My Documents” folder at all.  Allow it to fill up with junk, and then simply ignore it.  It sounds heretical, but: Don’t ever visit your “My Documents” folder!  Remove your icons/links to “My Documents” and replace them with links to the folders you created and you care about! Create your own file system from scratch!  Probably the best place to put it would be on your D: drive – if you have one.  This way, all your files live on one drive, while all the operating system and software component files live on the C: drive – simply and elegantly separated.  The benefits of that are profound.  Not only are there obvious organizational benefits (see tip #10, below), but when it comes to migrate your data to a new computer, you can (sometimes) simply unplug your D: drive and plug it in as the D: drive of your new computer (this implies that the D: drive is actually a separate physical disk, and not a partition on the same disk as C:).  You also get a slight speed improvement (again, only if your C: and D: drives are on separate physical disks). Warning:  From tip #12, below, you will see that it’s actually a good idea to have exactly the same file system structure – including the drive it’s filed on – on all of the computers you own.  So if you decide to use the D: drive as the storage system for your own files, make sure you are able to use the D: drive on all the computers you own.  If you can’t ensure that, then you can still use a clever geeky trick to store your files on the D: drive, but still access them all via the C: drive (see tip #17, below). If you only have one hard disk (C:), then create a dedicated folder that will contain all your files – something like C:\Files.  The name of the folder is not important, but make it a single, brief word. There are several reasons for this: When creating a backup regime, it’s easy to decide what files should be backed up – they’re all in the one folder! If you ever decide to trade in your computer for a new one, you know exactly which files to migrate You will always know where to begin a search for any file If you synchronize files with other computers, it makes your synchronization routines very simple.   It also causes all your shortcuts to continue to work on the other machines (more about this in tip #24, below). Once you’ve decided where your files should go, then put all your files in there – Everything!  Completely disregard the standard, default folders that are created for you by the operating system (“My Music”, “My Pictures”, etc).  In fact, you can actually relocate many of those folders into your own structure (more about that below, in tip #6). The more completely you get all your data files (documents, photos, music, etc) and all your configuration settings into that one folder, then the easier it will be to perform all of the above tasks. Once this has been done, and all your files live in one folder, all the other folders in C:\ can be thought of as “operating system” folders, and therefore of little day-to-day interest for us. Here’s a screenshot of a nicely organized C: drive, where all user files are located within the \Files folder:   Tip #4.  Use Sub-Folders This would be our simplest and most obvious tip.  It almost goes without saying.  Any organizational system you decide upon (see tip #1) will require that you create sub-folders for your files.  Get used to creating folders on a regular basis. Tip #5.  Don’t be Shy About Depth Create as many levels of sub-folders as you need.  Don’t be scared to do so.  Every time you notice an opportunity to group a set of related files into a sub-folder, do so.  Examples might include:  All the MP3s from one music CD, all the photos from one holiday, or all the documents from one client. It’s perfectly okay to put files into a folder called C:\Files\Me\From Others\Services\WestCo Bank\Statements\2009.  That’s only seven levels deep.  Ten levels is not uncommon.  Of course, it’s possible to take this too far.  If you notice yourself creating a sub-folder to hold only one file, then you’ve probably become a little over-zealous.  On the other hand, if you simply create a structure with only two levels (for example C:\Files\Work) then you really haven’t achieved any level of organization at all (unless you own only six files!).  Your “Work” folder will have become a dumping ground, just like your Desktop was, with most likely hundreds of files in it. Tip #6.  Move the Standard User Folders into Your Own Folder Structure Most operating systems, including Windows, create a set of standard folders for each of its users.  These folders then become the default location for files such as documents, music files, digital photos and downloaded Internet files.  In Windows 7, the full list is shown below: Some of these folders you may never use nor care about (for example, the Favorites folder, if you’re not using Internet Explorer as your browser).  Those ones you can leave where they are.  But you may be using some of the other folders to store files that are important to you.  Even if you’re not using them, Windows will still often treat them as the default storage location for many types of files.  When you go to save a standard file type, it can become annoying to be automatically prompted to save it in a folder that’s not part of your own file structure. But there’s a simple solution:  Move the folders you care about into your own folder structure!  If you do, then the next time you go to save a file of the corresponding type, Windows will prompt you to save it in the new, moved location. Moving the folders is easy.  Simply drag-and-drop them to the new location.  Here’s a screenshot of the default My Music folder being moved to my custom personal folder (Mark): Tip #7.  Name Files and Folders Intelligently This is another one that almost goes without saying, but we’ll say it anyway:  Do not allow files to be created that have meaningless names like Document1.doc, or folders called New Folder (2).  Take that extra 20 seconds and come up with a meaningful name for the file/folder – one that accurately divulges its contents without repeating the entire contents in the name. Tip #8.  Watch Out for Long Filenames Another way to tell if you have not yet created enough depth to your folder hierarchy is that your files often require really long names.  If you need to call a file Johnson Sales Figures March 2009.xls (which might happen to live in the same folder as Abercrombie Budget Report 2008.xls), then you might want to create some sub-folders so that the first file could be simply called March.xls, and living in the Clients\Johnson\Sales Figures\2009 folder. A well-placed file needs only a brief filename! Tip #9.  Use Shortcuts!  Everywhere! This is probably the single most useful and important tip we can offer.  A shortcut allows a file to be in two places at once. Why would you want that?  Well, the file and folder structure of every popular operating system on the market today is hierarchical.  This means that all objects (files and folders) always live within exactly one parent folder.  It’s a bit like a tree.  A tree has branches (folders) and leaves (files).  Each leaf, and each branch, is supported by exactly one parent branch, all the way back to the root of the tree (which, incidentally, is exactly why C:\ is called the “root folder” of the C: drive). That hard disks are structured this way may seem obvious and even necessary, but it’s only one way of organizing data.  There are others:  Relational databases, for example, organize structured data entirely differently.  The main limitation of hierarchical filing structures is that a file can only ever be in one branch of the tree – in only one folder – at a time.  Why is this a problem?  Well, there are two main reasons why this limitation is a problem for computer users: The “correct” place for a file, according to our organizational rationale, is very often a very inconvenient place for that file to be located.  Just because it’s correctly filed doesn’t mean it’s easy to get to.  Your file may be “correctly” buried six levels deep in your sub-folder structure, but you may need regular and speedy access to this file every day.  You could always move it to a more convenient location, but that would mean that you would need to re-file back to its “correct” location it every time you’d finished working on it.  Most unsatisfactory. A file may simply “belong” in two or more different locations within your file structure.  For example, say you’re an accountant and you have just completed the 2009 tax return for John Smith.  It might make sense to you to call this file 2009 Tax Return.doc and file it under Clients\John Smith.  But it may also be important to you to have the 2009 tax returns from all your clients together in the one place.  So you might also want to call the file John Smith.doc and file it under Tax Returns\2009.  The problem is, in a purely hierarchical filing system, you can’t put it in both places.  Grrrrr! Fortunately, Windows (and most other operating systems) offers a way for you to do exactly that:  It’s called a “shortcut” (also known as an “alias” on Macs and a “symbolic link” on UNIX systems).  Shortcuts allow a file to exist in one place, and an icon that represents the file to be created and put anywhere else you please.  In fact, you can create a dozen such icons and scatter them all over your hard disk.  Double-clicking on one of these icons/shortcuts opens up the original file, just as if you had double-clicked on the original file itself. Consider the following two icons: The one on the left is the actual Word document, while the one on the right is a shortcut that represents the Word document.  Double-clicking on either icon will open the same file.  There are two main visual differences between the icons: The shortcut will have a small arrow in the lower-left-hand corner (on Windows, anyway) The shortcut is allowed to have a name that does not include the file extension (the “.docx” part, in this case) You can delete the shortcut at any time without losing any actual data.  The original is still intact.  All you lose is the ability to get to that data from wherever the shortcut was. So why are shortcuts so great?  Because they allow us to easily overcome the main limitation of hierarchical file systems, and put a file in two (or more) places at the same time.  You will always have files that don’t play nice with your organizational rationale, and can’t be filed in only one place.  They demand to exist in two places.  Shortcuts allow this!  Furthermore, they allow you to collect your most often-opened files and folders together in one spot for convenient access.  The cool part is that the original files stay where they are, safe forever in their perfectly organized location. So your collection of most often-opened files can – and should – become a collection of shortcuts! If you’re still not convinced of the utility of shortcuts, consider the following well-known areas of a typical Windows computer: The Start Menu (and all the programs that live within it) The Quick Launch bar (or the Superbar in Windows 7) The “Favorite folders” area in the top-left corner of the Windows Explorer window (in Windows Vista or Windows 7) Your Internet Explorer Favorites or Firefox Bookmarks Each item in each of these areas is a shortcut!  Each of those areas exist for one purpose only:  For convenience – to provide you with a collection of the files and folders you access most often. It should be easy to see by now that shortcuts are designed for one single purpose:  To make accessing your files more convenient.  Each time you double-click on a shortcut, you are saved the hassle of locating the file (or folder, or program, or drive, or control panel icon) that it represents. Shortcuts allow us to invent a golden rule of file and folder organization: “Only ever have one copy of a file – never have two copies of the same file.  Use a shortcut instead” (this rule doesn’t apply to copies created for backup purposes, of course!) There are also lesser rules, like “don’t move a file into your work area – create a shortcut there instead”, and “any time you find yourself frustrated with how long it takes to locate a file, create a shortcut to it and place that shortcut in a convenient location.” So how to we create these massively useful shortcuts?  There are two main ways: “Copy” the original file or folder (click on it and type Ctrl-C, or right-click on it and select Copy):  Then right-click in an empty area of the destination folder (the place where you want the shortcut to go) and select Paste shortcut: Right-drag (drag with the right mouse button) the file from the source folder to the destination folder.  When you let go of the mouse button at the destination folder, a menu pops up: Select Create shortcuts here. Note that when shortcuts are created, they are often named something like Shortcut to Budget Detail.doc (windows XP) or Budget Detail – Shortcut.doc (Windows 7).   If you don’t like those extra words, you can easily rename the shortcuts after they’re created, or you can configure Windows to never insert the extra words in the first place (see our article on how to do this). And of course, you can create shortcuts to folders too, not just to files! Bottom line: Whenever you have a file that you’d like to access from somewhere else (whether it’s convenience you’re after, or because the file simply belongs in two places), create a shortcut to the original file in the new location. Tip #10.  Separate Application Files from Data Files Any digital organization guru will drum this rule into you.  Application files are the components of the software you’ve installed (e.g. Microsoft Word, Adobe Photoshop or Internet Explorer).  Data files are the files that you’ve created for yourself using that software (e.g. Word Documents, digital photos, emails or playlists). Software gets installed, uninstalled and upgraded all the time.  Hopefully you always have the original installation media (or downloaded set-up file) kept somewhere safe, and can thus reinstall your software at any time.  This means that the software component files are of little importance.  Whereas the files you have created with that software is, by definition, important.  It’s a good rule to always separate unimportant files from important files. So when your software prompts you to save a file you’ve just created, take a moment and check out where it’s suggesting that you save the file.  If it’s suggesting that you save the file into the same folder as the software itself, then definitely don’t follow that suggestion.  File it in your own folder!  In fact, see if you can find the program’s configuration option that determines where files are saved by default (if it has one), and change it. Tip #11.  Organize Files Based on Purpose, Not on File Type If you have, for example a folder called Work\Clients\Johnson, and within that folder you have two sub-folders, Word Documents and Spreadsheets (in other words, you’re separating “.doc” files from “.xls” files), then chances are that you’re not optimally organized.  It makes little sense to organize your files based on the program that created them.  Instead, create your sub-folders based on the purpose of the file.  For example, it would make more sense to create sub-folders called Correspondence and Financials.  It may well be that all the files in a given sub-folder are of the same file-type, but this should be more of a coincidence and less of a design feature of your organization system. Tip #12.  Maintain the Same Folder Structure on All Your Computers In other words, whatever organizational system you create, apply it to every computer that you can.  There are several benefits to this: There’s less to remember.  No matter where you are, you always know where to look for your files If you copy or synchronize files from one computer to another, then setting up the synchronization job becomes very simple Shortcuts can be copied or moved from one computer to another with ease (assuming the original files are also copied/moved).  There’s no need to find the target of the shortcut all over again on the second computer Ditto for linked files (e.g Word documents that link to data in a separate Excel file), playlists, and any files that reference the exact file locations of other files. This applies even to the drive that your files are stored on.  If your files are stored on C: on one computer, make sure they’re stored on C: on all your computers.  Otherwise all your shortcuts, playlists and linked files will stop working! Tip #13.  Create an “Inbox” Folder Create yourself a folder where you store all files that you’re currently working on, or that you haven’t gotten around to filing yet.  You can think of this folder as your “to-do” list.  You can call it “Inbox” (making it the same metaphor as your email system), or “Work”, or “To-Do”, or “Scratch”, or whatever name makes sense to you.  It doesn’t matter what you call it – just make sure you have one! Once you have finished working on a file, you then move it from the “Inbox” to its correct location within your organizational structure. You may want to use your Desktop as this “Inbox” folder.  Rightly or wrongly, most people do.  It’s not a bad place to put such files, but be careful:  If you do decide that your Desktop represents your “to-do” list, then make sure that no other files find their way there.  In other words, make sure that your “Inbox”, wherever it is, Desktop or otherwise, is kept free of junk – stray files that don’t belong there. So where should you put this folder, which, almost by definition, lives outside the structure of the rest of your filing system?  Well, first and foremost, it has to be somewhere handy.  This will be one of your most-visited folders, so convenience is key.  Putting it on the Desktop is a great option – especially if you don’t have any other folders on your Desktop:  the folder then becomes supremely easy to find in Windows Explorer: You would then create shortcuts to this folder in convenient spots all over your computer (“Favorite Links”, “Quick Launch”, etc). Tip #14.  Ensure You have Only One “Inbox” Folder Once you’ve created your “Inbox” folder, don’t use any other folder location as your “to-do list”.  Throw every incoming or created file into the Inbox folder as you create/receive it.  This keeps the rest of your computer pristine and free of randomly created or downloaded junk.  The last thing you want to be doing is checking multiple folders to see all your current tasks and projects.  Gather them all together into one folder. Here are some tips to help ensure you only have one Inbox: Set the default “save” location of all your programs to this folder. Set the default “download” location for your browser to this folder. If this folder is not your desktop (recommended) then also see if you can make a point of not putting “to-do” files on your desktop.  This keeps your desktop uncluttered and Zen-like: (the Inbox folder is in the bottom-right corner) Tip #15.  Be Vigilant about Clearing Your “Inbox” Folder This is one of the keys to staying organized.  If you let your “Inbox” overflow (i.e. allow there to be more than, say, 30 files or folders in there), then you’re probably going to start feeling like you’re overwhelmed:  You’re not keeping up with your to-do list.  Once your Inbox gets beyond a certain point (around 30 files, studies have shown), then you’ll simply start to avoid it.  You may continue to put files in there, but you’ll be scared to look at it, fearing the “out of control” feeling that all overworked, chaotic or just plain disorganized people regularly feel. So, here’s what you can do: Visit your Inbox/to-do folder regularly (at least five times per day). Scan the folder regularly for files that you have completed working on and are ready for filing.  File them immediately. Make it a source of pride to keep the number of files in this folder as small as possible.  If you value peace of mind, then make the emptiness of this folder one of your highest (computer) priorities If you know that a particular file has been in the folder for more than, say, six weeks, then admit that you’re not actually going to get around to processing it, and move it to its final resting place. Tip #16.  File Everything Immediately, and Use Shortcuts for Your Active Projects As soon as you create, receive or download a new file, store it away in its “correct” folder immediately.  Then, whenever you need to work on it (possibly straight away), create a shortcut to it in your “Inbox” (“to-do”) folder or your desktop.  That way, all your files are always in their “correct” locations, yet you still have immediate, convenient access to your current, active files.  When you finish working on a file, simply delete the shortcut. Ideally, your “Inbox” folder – and your Desktop – should contain no actual files or folders.  They should simply contain shortcuts. Tip #17.  Use Directory Symbolic Links (or Junctions) to Maintain One Unified Folder Structure Using this tip, we can get around a potential hiccup that we can run into when creating our organizational structure – the issue of having more than one drive on our computer (C:, D:, etc).  We might have files we need to store on the D: drive for space reasons, and yet want to base our organized folder structure on the C: drive (or vice-versa). Your chosen organizational structure may dictate that all your files must be accessed from the C: drive (for example, the root folder of all your files may be something like C:\Files).  And yet you may still have a D: drive and wish to take advantage of the hundreds of spare Gigabytes that it offers.  Did you know that it’s actually possible to store your files on the D: drive and yet access them as if they were on the C: drive?  And no, we’re not talking about shortcuts here (although the concept is very similar). By using the shell command mklink, you can essentially take a folder that lives on one drive and create an alias for it on a different drive (you can do lots more than that with mklink – for a full rundown on this programs capabilities, see our dedicated article).  These aliases are called directory symbolic links (and used to be known as junctions).  You can think of them as “virtual” folders.  They function exactly like regular folders, except they’re physically located somewhere else. For example, you may decide that your entire D: drive contains your complete organizational file structure, but that you need to reference all those files as if they were on the C: drive, under C:\Files.  If that was the case you could create C:\Files as a directory symbolic link – a link to D:, as follows: mklink /d c:\files d:\ Or it may be that the only files you wish to store on the D: drive are your movie collection.  You could locate all your movie files in the root of your D: drive, and then link it to C:\Files\Media\Movies, as follows: mklink /d c:\files\media\movies d:\ (Needless to say, you must run these commands from a command prompt – click the Start button, type cmd and press Enter) Tip #18. Customize Your Folder Icons This is not strictly speaking an organizational tip, but having unique icons for each folder does allow you to more quickly visually identify which folder is which, and thus saves you time when you’re finding files.  An example is below (from my folder that contains all files downloaded from the Internet): To learn how to change your folder icons, please refer to our dedicated article on the subject. Tip #19.  Tidy Your Start Menu The Windows Start Menu is usually one of the messiest parts of any Windows computer.  Every program you install seems to adopt a completely different approach to placing icons in this menu.  Some simply put a single program icon.  Others create a folder based on the name of the software.  And others create a folder based on the name of the software manufacturer.  It’s chaos, and can make it hard to find the software you want to run. Thankfully we can avoid this chaos with useful operating system features like Quick Launch, the Superbar or pinned start menu items. Even so, it would make a lot of sense to get into the guts of the Start Menu itself and give it a good once-over.  All you really need to decide is how you’re going to organize your applications.  A structure based on the purpose of the application is an obvious candidate.  Below is an example of one such structure: In this structure, Utilities means software whose job it is to keep the computer itself running smoothly (configuration tools, backup software, Zip programs, etc).  Applications refers to any productivity software that doesn’t fit under the headings Multimedia, Graphics, Internet, etc. In case you’re not aware, every icon in your Start Menu is a shortcut and can be manipulated like any other shortcut (copied, moved, deleted, etc). With the Windows Start Menu (all version of Windows), Microsoft has decided that there be two parallel folder structures to store your Start Menu shortcuts.  One for you (the logged-in user of the computer) and one for all users of the computer.  Having two parallel structures can often be redundant:  If you are the only user of the computer, then having two parallel structures is totally redundant.  Even if you have several users that regularly log into the computer, most of your installed software will need to be made available to all users, and should thus be moved out of the “just you” version of the Start Menu and into the “all users” area. To take control of your Start Menu, so you can start organizing it, you’ll need to know how to access the actual folders and shortcut files that make up the Start Menu (both versions of it).  To find these folders and files, click the Start button and then right-click on the All Programs text (Windows XP users should right-click on the Start button itself): The Open option refers to the “just you” version of the Start Menu, while the Open All Users option refers to the “all users” version.  Click on the one you want to organize. A Windows Explorer window then opens with your chosen version of the Start Menu selected.  From there it’s easy.  Double-click on the Programs folder and you’ll see all your folders and shortcuts.  Now you can delete/rename/move until it’s just the way you want it. Note:  When you’re reorganizing your Start Menu, you may want to have two Explorer windows open at the same time – one showing the “just you” version and one showing the “all users” version.  You can drag-and-drop between the windows. Tip #20.  Keep Your Start Menu Tidy Once you have a perfectly organized Start Menu, try to be a little vigilant about keeping it that way.  Every time you install a new piece of software, the icons that get created will almost certainly violate your organizational structure. So to keep your Start Menu pristine and organized, make sure you do the following whenever you install a new piece of software: Check whether the software was installed into the “just you” area of the Start Menu, or the “all users” area, and then move it to the correct area. Remove all the unnecessary icons (like the “Read me” icon, the “Help” icon (you can always open the help from within the software itself when it’s running), the “Uninstall” icon, the link(s)to the manufacturer’s website, etc) Rename the main icon(s) of the software to something brief that makes sense to you.  For example, you might like to rename Microsoft Office Word 2010 to simply Word Move the icon(s) into the correct folder based on your Start Menu organizational structure And don’t forget:  when you uninstall a piece of software, the software’s uninstall routine is no longer going to be able to remove the software’s icon from the Start Menu (because you moved and/or renamed it), so you’ll need to remove that icon manually. Tip #21.  Tidy C:\ The root of your C: drive (C:\) is a common dumping ground for files and folders – both by the users of your computer and by the software that you install on your computer.  It can become a mess. There’s almost no software these days that requires itself to be installed in C:\.  99% of the time it can and should be installed into C:\Program Files.  And as for your own files, well, it’s clear that they can (and almost always should) be stored somewhere else. In an ideal world, your C:\ folder should look like this (on Windows 7): Note that there are some system files and folders in C:\ that are usually and deliberately “hidden” (such as the Windows virtual memory file pagefile.sys, the boot loader file bootmgr, and the System Volume Information folder).  Hiding these files and folders is a good idea, as they need to stay where they are and are almost never needed to be opened or even seen by you, the user.  Hiding them prevents you from accidentally messing with them, and enhances your sense of order and well-being when you look at your C: drive folder. Tip #22.  Tidy Your Desktop The Desktop is probably the most abused part of a Windows computer (from an organization point of view).  It usually serves as a dumping ground for all incoming files, as well as holding icons to oft-used applications, plus some regularly opened files and folders.  It often ends up becoming an uncontrolled mess.  See if you can avoid this.  Here’s why… Application icons (Word, Internet Explorer, etc) are often found on the Desktop, but it’s unlikely that this is the optimum place for them.  The “Quick Launch” bar (or the Superbar in Windows 7) is always visible and so represents a perfect location to put your icons.  You’ll only be able to see the icons on your Desktop when all your programs are minimized.  It might be time to get your application icons off your desktop… You may have decided that the Inbox/To-do folder on your computer (see tip #13, above) should be your Desktop.  If so, then enough said.  Simply be vigilant about clearing it and preventing it from being polluted by junk files (see tip #15, above).  On the other hand, if your Desktop is not acting as your “Inbox” folder, then there’s no reason for it to have any data files or folders on it at all, except perhaps a couple of shortcuts to often-opened files and folders (either ongoing or current projects).  Everything else should be moved to your “Inbox” folder. In an ideal world, it might look like this: Tip #23.  Move Permanent Items on Your Desktop Away from the Top-Left Corner When files/folders are dragged onto your desktop in a Windows Explorer window, or when shortcuts are created on your Desktop from Internet Explorer, those icons are always placed in the top-left corner – or as close as they can get.  If you have other files, folders or shortcuts that you keep on the Desktop permanently, then it’s a good idea to separate these permanent icons from the transient ones, so that you can quickly identify which ones the transients are.  An easy way to do this is to move all your permanent icons to the right-hand side of your Desktop.  That should keep them separated from incoming items. Tip #24.  Synchronize If you have more than one computer, you’ll almost certainly want to share files between them.  If the computers are permanently attached to the same local network, then there’s no need to store multiple copies of any one file or folder – shortcuts will suffice.  However, if the computers are not always on the same network, then you will at some point need to copy files between them.  For files that need to permanently live on both computers, the ideal way to do this is to synchronize the files, as opposed to simply copying them. We only have room here to write a brief summary of synchronization, not a full article.  In short, there are several different types of synchronization: Where the contents of one folder are accessible anywhere, such as with Dropbox Where the contents of any number of folders are accessible anywhere, such as with Windows Live Mesh Where any files or folders from anywhere on your computer are synchronized with exactly one other computer, such as with the Windows “Briefcase”, Microsoft SyncToy, or (much more powerful, yet still free) SyncBack from 2BrightSparks.  This only works when both computers are on the same local network, at least temporarily. A great advantage of synchronization solutions is that once you’ve got it configured the way you want it, then the sync process happens automatically, every time.  Click a button (or schedule it to happen automatically) and all your files are automagically put where they’re supposed to be. If you maintain the same file and folder structure on both computers, then you can also sync files depend upon the correct location of other files, like shortcuts, playlists and office documents that link to other office documents, and the synchronized files still work on the other computer! Tip #25.  Hide Files You Never Need to See If you have your files well organized, you will often be able to tell if a file is out of place just by glancing at the contents of a folder (for example, it should be pretty obvious if you look in a folder that contains all the MP3s from one music CD and see a Word document in there).  This is a good thing – it allows you to determine if there are files out of place with a quick glance.  Yet sometimes there are files in a folder that seem out of place but actually need to be there, such as the “folder art” JPEGs in music folders, and various files in the root of the C: drive.  If such files never need to be opened by you, then a good idea is to simply hide them.  Then, the next time you glance at the folder, you won’t have to remember whether that file was supposed to be there or not, because you won’t see it at all! To hide a file, simply right-click on it and choose Properties: Then simply tick the Hidden tick-box:   Tip #26.  Keep Every Setup File These days most software is downloaded from the Internet.  Whenever you download a piece of software, keep it.  You’ll never know when you need to reinstall the software. Further, keep with it an Internet shortcut that links back to the website where you originally downloaded it, in case you ever need to check for updates. See tip #33 below for a full description of the excellence of organizing your setup files. Tip #27.  Try to Minimize the Number of Folders that Contain Both Files and Sub-folders Some of the folders in your organizational structure will contain only files.  Others will contain only sub-folders.  And you will also have some folders that contain both files and sub-folders.  You will notice slight improvements in how long it takes you to locate a file if you try to avoid this third type of folder.  It’s not always possible, of course – you’ll always have some of these folders, but see if you can avoid it. One way of doing this is to take all the leftover files that didn’t end up getting stored in a sub-folder and create a special “Miscellaneous” or “Other” folder for them. Tip #28.  Starting a Filename with an Underscore Brings it to the Top of a List Further to the previous tip, if you name that “Miscellaneous” or “Other” folder in such a way that its name begins with an underscore “_”, then it will appear at the top of the list of files/folders. The screenshot below is an example of this.  Each folder in the list contains a set of digital photos.  The folder at the top of the list, _Misc, contains random photos that didn’t deserve their own dedicated folder: Tip #29.  Clean Up those CD-ROMs and (shudder!) Floppy Disks Have you got a pile of CD-ROMs stacked on a shelf of your office?  Old photos, or files you archived off onto CD-ROM (or even worse, floppy disks!) because you didn’t have enough disk space at the time?  In the meantime have you upgraded your computer and now have 500 Gigabytes of space you don’t know what to do with?  If so, isn’t it time you tidied up that stack of disks and filed them into your gorgeous new folder structure? So what are you waiting for?  Bite the bullet, copy them all back onto your computer, file them in their appropriate folders, and then back the whole lot up onto a shiny new 1000Gig external hard drive! Useful Folders to Create This next section suggests some useful folders that you might want to create within your folder structure.  I’ve personally found them to be indispensable. The first three are all about convenience – handy folders to create and then put somewhere that you can always access instantly.  For each one, it’s not so important where the actual folder is located, but it’s very important where you put the shortcut(s) to the folder.  You might want to locate the shortcuts: On your Desktop In your “Quick Launch” area (or pinned to your Windows 7 Superbar) In your Windows Explorer “Favorite Links” area Tip #30.  Create an “Inbox” (“To-Do”) Folder This has already been mentioned in depth (see tip #13), but we wanted to reiterate its importance here.  This folder contains all the recently created, received or downloaded files that you have not yet had a chance to file away properly, and it also may contain files that you have yet to process.  In effect, it becomes a sort of “to-do list”.  It doesn’t have to be called “Inbox” – you can call it whatever you want. Tip #31.  Create a Folder where Your Current Projects are Collected Rather than going hunting for them all the time, or dumping them all on your desktop, create a special folder where you put links (or work folders) for each of the projects you’re currently working on. You can locate this folder in your “Inbox” folder, on your desktop, or anywhere at all – just so long as there’s a way of getting to it quickly, such as putting a link to it in Windows Explorer’s “Favorite Links” area: Tip #32.  Create a Folder for Files and Folders that You Regularly Open You will always have a few files that you open regularly, whether it be a spreadsheet of your current accounts, or a favorite playlist.  These are not necessarily “current projects”, rather they’re simply files that you always find yourself opening.  Typically such files would be located on your desktop (or even better, shortcuts to those files).  Why not collect all such shortcuts together and put them in their own special folder? As with the “Current Projects” folder (above), you would want to locate that folder somewhere convenient.  Below is an example of a folder called “Quick links”, with about seven files (shortcuts) in it, that is accessible through the Windows Quick Launch bar: See tip #37 below for a full explanation of the power of the Quick Launch bar. Tip #33.  Create a “Set-ups” Folder A typical computer has dozens of applications installed on it.  For each piece of software, there are often many different pieces of information you need to keep track of, including: The original installation setup file(s).  This can be anything from a simple 100Kb setup.exe file you downloaded from a website, all the way up to a 4Gig ISO file that you copied from a DVD-ROM that you purchased. The home page of the software manufacturer (in case you need to look up something on their support pages, their forum or their online help) The page containing the download link for your actual file (in case you need to re-download it, or download an upgraded version) The serial number Your proof-of-purchase documentation Any other template files, plug-ins, themes, etc that also need to get installed For each piece of software, it’s a great idea to gather all of these files together and put them in a single folder.  The folder can be the name of the software (plus possibly a very brief description of what it’s for – in case you can’t remember what the software does based in its name).  Then you would gather all of these folders together into one place, and call it something like “Software” or “Setups”. If you have enough of these folders (I have several hundred, being a geek, collected over 20 years), then you may want to further categorize them.  My own categorization structure is based on “platform” (operating system): The last seven folders each represents one platform/operating system, while _Operating Systems contains set-up files for installing the operating systems themselves.  _Hardware contains ROMs for hardware I own, such as routers. Within the Windows folder (above), you can see the beginnings of the vast library of software I’ve compiled over the years: An example of a typical application folder looks like this: Tip #34.  Have a “Settings” Folder We all know that our documents are important.  So are our photos and music files.  We save all of these files into folders, and then locate them afterwards and double-click on them to open them.  But there are many files that are important to us that can’t be saved into folders, and then searched for and double-clicked later on.  These files certainly contain important information that we need, but are often created internally by an application, and saved wherever that application feels is appropriate. A good example of this is the “PST” file that Outlook creates for us and uses to store all our emails, contacts, appointments and so forth.  Another example would be the collection of Bookmarks that Firefox stores on your behalf. And yet another example would be the customized settings and configuration files of our all our software.  Granted, most Windows programs store their configuration in the Registry, but there are still many programs that use configuration files to store their settings. Imagine if you lost all of the above files!  And yet, when people are backing up their computers, they typically only back up the files they know about – those that are stored in the “My Documents” folder, etc.  If they had a hard disk failure or their computer was lost or stolen, their backup files would not include some of the most vital files they owned.  Also, when migrating to a new computer, it’s vital to ensure that these files make the journey. It can be a very useful idea to create yourself a folder to store all your “settings” – files that are important to you but which you never actually search for by name and double-click on to open them.  Otherwise, next time you go to set up a new computer just the way you want it, you’ll need to spend hours recreating the configuration of your previous computer! So how to we get our important files into this folder?  Well, we have a few options: Some programs (such as Outlook and its PST files) allow you to place these files wherever you want.  If you delve into the program’s options, you will find a setting somewhere that controls the location of the important settings files (or “personal storage” – PST – when it comes to Outlook) Some programs do not allow you to change such locations in any easy way, but if you get into the Registry, you can sometimes find a registry key that refers to the location of the file(s).  Simply move the file into your Settings folder and adjust the registry key to refer to the new location. Some programs stubbornly refuse to allow their settings files to be placed anywhere other then where they stipulate.  When faced with programs like these, you have three choices:  (1) You can ignore those files, (2) You can copy the files into your Settings folder (let’s face it – settings don’t change very often), or (3) you can use synchronization software, such as the Windows Briefcase, to make synchronized copies of all your files in your Settings folder.  All you then have to do is to remember to run your sync software periodically (perhaps just before you run your backup software!). There are some other things you may decide to locate inside this new “Settings” folder: Exports of registry keys (from the many applications that store their configurations in the Registry).  This is useful for backup purposes or for migrating to a new computer Notes you’ve made about all the specific customizations you have made to a particular piece of software (so that you’ll know how to do it all again on your next computer) Shortcuts to webpages that detail how to tweak certain aspects of your operating system or applications so they are just the way you like them (such as how to remove the words “Shortcut to” from the beginning of newly created shortcuts).  In other words, you’d want to create shortcuts to half the pages on the How-To Geek website! Here’s an example of a “Settings” folder: Windows Features that Help with Organization This section details some of the features of Microsoft Windows that are a boon to anyone hoping to stay optimally organized. Tip #35.  Use the “Favorite Links” Area to Access Oft-Used Folders Once you’ve created your great new filing system, work out which folders you access most regularly, or which serve as great starting points for locating the rest of the files in your folder structure, and then put links to those folders in your “Favorite Links” area of the left-hand side of the Windows Explorer window (simply called “Favorites” in Windows 7):   Some ideas for folders you might want to add there include: Your “Inbox” folder (or whatever you’ve called it) – most important! The base of your filing structure (e.g. C:\Files) A folder containing shortcuts to often-accessed folders on other computers around the network (shown above as Network Folders) A folder containing shortcuts to your current projects (unless that folder is in your “Inbox” folder) Getting folders into this area is very simple – just locate the folder you’re interested in and drag it there! Tip #36.  Customize the Places Bar in the File/Open and File/Save Boxes Consider the screenshot below: The highlighted icons (collectively known as the “Places Bar”) can be customized to refer to any folder location you want, allowing instant access to any part of your organizational structure. Note:  These File/Open and File/Save boxes have been superseded by new versions that use the Windows Vista/Windows 7 “Favorite Links”, but the older versions (shown above) are still used by a surprisingly large number of applications. The easiest way to customize these icons is to use the Group Policy Editor, but not everyone has access to this program.  If you do, open it up and navigate to: User Configuration > Administrative Templates > Windows Components > Windows Explorer > Common Open File Dialog If you don’t have access to the Group Policy Editor, then you’ll need to get into the Registry.  Navigate to: HKEY_CURRENT_USER \ Software \ Microsoft  \ Windows \ CurrentVersion \ Policies \ comdlg32 \ Placesbar It should then be easy to make the desired changes.  Log off and log on again to allow the changes to take effect. Tip #37.  Use the Quick Launch Bar as a Application and File Launcher That Quick Launch bar (to the right of the Start button) is a lot more useful than people give it credit for.  Most people simply have half a dozen icons in it, and use it to start just those programs.  But it can actually be used to instantly access just about anything in your filing system: For complete instructions on how to set this up, visit our dedicated article on this topic. Tip #38.  Put a Shortcut to Windows Explorer into Your Quick Launch Bar This is only necessary in Windows Vista and Windows XP.  The Microsoft boffins finally got wise and added it to the Windows 7 Superbar by default. Windows Explorer – the program used for managing your files and folders – is one of the most useful programs in Windows.  Anyone who considers themselves serious about being organized needs instant access to this program at any time.  A great place to create a shortcut to this program is in the Windows XP and Windows Vista “Quick Launch” bar: To get it there, locate it in your Start Menu (usually under “Accessories”) and then right-drag it down into your Quick Launch bar (and create a copy). Tip #39.  Customize the Starting Folder for Your Windows 7 Explorer Superbar Icon If you’re on Windows 7, your Superbar will include a Windows Explorer icon.  Clicking on the icon will launch Windows Explorer (of course), and will start you off in your “Libraries” folder.  Libraries may be fine as a starting point, but if you have created yourself an “Inbox” folder, then it would probably make more sense to start off in this folder every time you launch Windows Explorer. To change this default/starting folder location, then first right-click the Explorer icon in the Superbar, and then right-click Properties:Then, in Target field of the Windows Explorer Properties box that appears, type %windir%\explorer.exe followed by the path of the folder you wish to start in.  For example: %windir%\explorer.exe C:\Files If that folder happened to be on the Desktop (and called, say, “Inbox”), then you would use the following cleverness: %windir%\explorer.exe shell:desktop\Inbox Then click OK and test it out. Tip #40.  Ummmmm…. No, that’s it.  I can’t think of another one.  That’s all of the tips I can come up with.  I only created this one because 40 is such a nice round number… Case Study – An Organized PC To finish off the article, I have included a few screenshots of my (main) computer (running Vista).  The aim here is twofold: To give you a sense of what it looks like when the above, sometimes abstract, tips are applied to a real-life computer, and To offer some ideas about folders and structure that you may want to steal to use on your own PC. Let’s start with the C: drive itself.  Very minimal.  All my files are contained within C:\Files.  I’ll confine the rest of the case study to this folder: That folder contains the following: Mark: My personal files VC: My business (Virtual Creations, Australia) Others contains files created by friends and family Data contains files from the rest of the world (can be thought of as “public” files, usually downloaded from the Net) Settings is described above in tip #34 The Data folder contains the following sub-folders: Audio:  Radio plays, audio books, podcasts, etc Development:  Programmer and developer resources, sample source code, etc (see below) Humour:  Jokes, funnies (those emails that we all receive) Movies:  Downloaded and ripped movies (all legal, of course!), their scripts, DVD covers, etc. Music:  (see below) Setups:  Installation files for software (explained in full in tip #33) System:  (see below) TV:  Downloaded TV shows Writings:  Books, instruction manuals, etc (see below) The Music folder contains the following sub-folders: Album covers:  JPEG scans Guitar tabs:  Text files of guitar sheet music Lists:  e.g. “Top 1000 songs of all time” Lyrics:  Text files MIDI:  Electronic music files MP3 (representing 99% of the Music folder):  MP3s, either ripped from CDs or downloaded, sorted by artist/album name Music Video:  Video clips Sheet Music:  usually PDFs The Data\Writings folder contains the following sub-folders: (all pretty self-explanatory) The Data\Development folder contains the following sub-folders: Again, all pretty self-explanatory (if you’re a geek) The Data\System folder contains the following sub-folders: These are usually themes, plug-ins and other downloadable program-specific resources. The Mark folder contains the following sub-folders: From Others:  Usually letters that other people (friends, family, etc) have written to me For Others:  Letters and other things I have created for other people Green Book:  None of your business Playlists:  M3U files that I have compiled of my favorite songs (plus one M3U playlist file for every album I own) Writing:  Fiction, philosophy and other musings of mine Mark Docs:  Shortcut to C:\Users\Mark Settings:  Shortcut to C:\Files\Settings\Mark The Others folder contains the following sub-folders: The VC (Virtual Creations, my business – I develop websites) folder contains the following sub-folders: And again, all of those are pretty self-explanatory. Conclusion These tips have saved my sanity and helped keep me a productive geek, but what about you? What tips and tricks do you have to keep your files organized?  Please share them with us in the comments.  Come on, don’t be shy… Similar Articles Productive Geek Tips Fix For When Windows Explorer in Vista Stops Showing File NamesWhy Did Windows Vista’s Music Folder Icon Turn Yellow?Print or Create a Text File List of the Contents in a Directory the Easy WayCustomize the Windows 7 or Vista Send To MenuAdd Copy To / Move To on Windows 7 or Vista Right-Click Menu TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Acronis Online Backup DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows Track Daily Goals With 42Goals Video Toolbox is a Superb Online Video Editor Fun with 47 charts and graphs Tomorrow is Mother’s Day Check the Average Speed of YouTube Videos You’ve Watched OutlookStatView Scans and Displays General Usage Statistics

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  • Big Data – Learning Basics of Big Data in 21 Days – Bookmark

    - by Pinal Dave
    Earlier this month I had a great time to write Bascis of Big Data series. This series received great response and lots of good comments I have received, I am going to follow up this basics series with further in-depth series in near future. Here is the consolidated blog post where you can find all the 21 days blog posts together. Bookmark this page for future reference. Big Data – Beginning Big Data – Day 1 of 21 Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21 Big Data – Evolution of Big Data – Day 3 of 21 Big Data – Basics of Big Data Architecture – Day 4 of 21 Big Data – Buzz Words: What is NoSQL – Day 5 of 21 Big Data – Buzz Words: What is Hadoop – Day 6 of 21 Big Data – Buzz Words: What is MapReduce – Day 7 of 21 Big Data – Buzz Words: What is HDFS – Day 8 of 21 Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21 Big Data – Buzz Words: What is NewSQL – Day 10 of 21 Big Data – Role of Cloud Computing in Big Data – Day 11 of 21 Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21 Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21 Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21 Big DataData Mining with Hive – What is Hive? – What is HiveQL (HQL)? – Day 15 of 21 Big Data – Interacting with Hadoop – What is PIG? – What is PIG Latin? – Day 16 of 21 Big Data – Interacting with Hadoop – What is Sqoop? – What is Zookeeper? – Day 17 of 21 Big Data – Basics of Big Data Analytics – Day 18 of 21 Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21 Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21 Big Data – Final Wrap and What Next – Day 21 of 21 Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • dpkg: warning: files list file for package 'x' missing

    - by Mark
    I get this warning for several packages every time I install any package or perform apt-get upgrade. Not sure what is causing it; it's a fresh Debian install on my OpenVZ server and I haven't changed any dpkg settings. Here's an example: root@debian:~# apt-get install cowsay Reading package lists... Done Building dependency tree Reading state information... Done Suggested packages: filters The following NEW packages will be installed: cowsay 0 upgraded, 1 newly installed, 0 to remove and 0 not upgraded. Need to get 21.9 kB of archives. After this operation, 91.1 kB of additional disk space will be used. Get:1 http://ftp.us.debian.org/debian/ unstable/main cowsay all 3.03+dfsg1-4 [21.9 kB] Fetched 21.9 kB in 0s (70.2 kB/s) Selecting previously unselected package cowsay. dpkg: warning: files list file for package 'libssh2-1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libkrb5-3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libwrap0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libcap2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libpam-ck-connector:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libc6:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libtalloc2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libselinux1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libp11-kit0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libavahi-client3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libbz2-1.0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libpcre3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgpm2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgnutls26:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libavahi-common3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libcroco3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'liblzma5:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libpaper1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libsensors4:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libbsd0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libavahi-common-data:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libss2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libblkid1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libslang2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libacl1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libcomerr2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libkrb5support0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'e2fslibs:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'librtmp0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libidn11:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libpcap0.8:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libattr1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libdevmapper1.02.1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'odbcinst1debian2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libexpat1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libltdl7:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libkeyutils1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libcups2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libsqlite3-0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libck-connector0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'zlib1g:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libnl1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libfontconfig1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libudev0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libsepol1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libmagic1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libk5crypto3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libunistring0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgpg-error0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libusb-0.1-4:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libpam0g:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libpopt0:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgssapi-krb5-2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgeoip1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libcurl3-gnutls:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libtasn1-3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libuuid1:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgcrypt11:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libgdbm3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libdbus-1-3:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libsysfs2:amd64' missing; assuming package has no files currently installed dpkg: warning: files list file for package 'libfreetype6:amd64' missing; assuming package has no files currently installed (Reading database ... 21908 files and directories currently installed.) Unpacking cowsay (from .../cowsay_3.03+dfsg1-4_all.deb) ... Processing triggers for man-db ... Setting up cowsay (3.03+dfsg1-4) ... root@debian:~# Everything works fine, but these warning messages are pretty annoying. Does anyone know how I can fix this?

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  • Data Quality Through Data Governance

    Data Quality Governance Data quality is very important to every organization, bad data cost an organization time, money, and resources that could be prevented if the proper governance was put in to place.  Data Governance Program Criteria: Support from Executive Management and all Business Units Data Stewardship Program  Cross Functional Team of Data Stewards Data Governance Committee Quality Structured Data It should go without saying but any successful project in today’s business world must get buy in from executive management and all stakeholders involved with the project. If management does not fully support a project because they see it is in there and the company’s best interest then they will remove/eliminate funding, resources and allocated time to work on the project. In essence they can render a project dead until it is official killed by the business. In addition, buy in from stake holders is also very important because they can cause delays increased spending in time, money and resources because they do not support a project. Data Stewardship programs are administered by a data steward manager who primary focus is to support, train and manage a cross functional data stewards team. A cross functional team of data stewards are pulled from various departments act to ensure that all systems work to ensure that an organization’s goals are achieved. Typically, data stewards are subject matter experts that act as mediators between their respective departments and IT. Data Quality Procedures Data Governance Committees are composed of data stewards, Upper management, IT Leadership and various subject matter experts depending on a company. The primary goal of this committee is to define strategic goals, coordinate activities, set data standards and offer data guidelines for the business. Data Quality Policies In 1997, Claudia Imhoff defined a Data Stewardship’s responsibility as to approve business naming standards, develop consistent data definitions, determine data aliases, develop standard calculations and derivations, document the business rules of the corporation, monitor the quality of the data in the data warehouse, define security requirements, and so forth. She further explains data stewards responsible for creating and enforcing polices on the following but not limited to issues. Resolving Data Integration Issues Determining Data Security Documenting Data Definitions, Calculations, Summarizations, etc. Maintaining/Updating Business Rules Analyzing and Improving Data Quality

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  • SQL SERVER – Advanced Data Quality Services with Melissa Data – Azure Data Market

    - by pinaldave
    There has been much fanfare over the new SQL Server 2012, and especially around its new companion product Data Quality Services (DQS). Among the many new features is the addition of this integrated knowledge-driven product that enables data stewards everywhere to profile, match, and cleanse data. In addition to the homegrown rules that data stewards can design and implement, there are also connectors to third party providers that are hosted in the Azure Datamarket marketplace.  In this review, I leverage SQL Server 2012 Data Quality Services, and proceed to subscribe to a third party data cleansing product through the Datamarket to showcase this unique capability. Crucial Questions For the purposes of the review, I used a database I had in an Excel spreadsheet with name and address information. Upon a cursory inspection, there are miscellaneous problems with these records; some addresses are missing ZIP codes, others missing a city, and some records are slightly misspelled or have unparsed suites. With DQS, I can easily add a knowledge base to help standardize my values, such as for state abbreviations. But how do I know that my address is correct? And if my address is not correct, what should it be corrected to? The answer lies in a third party knowledge base by the acknowledged USPS certified address accuracy experts at Melissa Data. Reference Data Services Within DQS there is a handy feature to actually add reference data from many different third-party Reference Data Services (RDS) vendors. DQS simplifies the processes of cleansing, standardizing, and enriching data through custom rules and through service providers from the Azure Datamarket. A quick jump over to the Datamarket site shows me that there are a handful of providers that offer data directly through Data Quality Services. Upon subscribing to these services, one can attach a DQS domain or composite domain (fields in a record) to a reference data service provider, and begin using it to cleanse, standardize, and enrich that data. Besides what I am looking for (address correction and enrichment), it is possible to subscribe to a host of other services including geocoding, IP address reference, phone checking and enrichment, as well as name parsing, standardization, and genderization.  These capabilities extend the data quality that DQS has natively by quite a bit. For my current address correction review, I needed to first sign up to a reference data provider on the Azure Data Market site. For this example, I used Melissa Data’s Address Check Service. They offer free one-month trials, so if you wish to follow along, or need to add address quality to your own data, I encourage you to sign up with them. Once I subscribed to the desired Reference Data Provider, I navigated my browser to the Account Keys within My Account to view the generated account key, which I then inserted into the DQS Client – Configuration under the Administration area. Step by Step to Guide That was all it took to hook in the subscribed provider -Melissa Data- directly to my DQS Client. The next step was for me to attach and map in my Reference Data from the newly acquired reference data provider, to a domain in my knowledge base. On the DQS Client home screen, I selected “New Knowledge Base” under Knowledge Base Management on the left-hand side of the home screen. Under New Knowledge Base, I typed a Name and description of my new knowledge base, then proceeded to the Domain Management screen. Here I established a series of domains (fields) and then linked them all together as a composite domain (record set). Using the Create Domain button, I created the following domains according to the fields in my incoming data: Name Address Suite City State Zip I added a Suite column in my domain because Melissa Data has the ability to return missing Suites based on last name or company. And that’s a great benefit of using these third party providers, as they have data that the data steward would not normally have access to. The bottom line is, with these third party data providers, I can actually improve my data. Next, I created a composite domain (fulladdress) and added the (field) domains into the composite domain. This essentially groups our address fields together in a record to facilitate the full address cleansing they perform. I then selected my newly created composite domain and under the Reference Data tab, added my third party reference data provider –Melissa Data’s Address Check- and mapped in each domain that I had to the provider’s Schema. Now that my composite domain has been married to the Reference Data service, I can take the newly published knowledge base and create a project to cleanse and enrich my data. My next task was to create a new Data Quality project, mapping in my data source and matching it to the appropriate domain column, and then kick off the verification process. It took just a few minutes with some progress indicators indicating that it was working. When the process concluded, there was a helpful set of tabs that place the response records into categories: suggested; new; invalid; corrected (automatically); and correct. Accepting the suggestions provided by  Melissa Data allowed me to clean up all the records and flag the invalid ones. It is very apparent that DQS makes address data quality simplistic for any IT professional. Final Note As I have shown, DQS makes data quality very easy. Within minutes I was able to set up a data cleansing and enrichment routine within my data quality project, and ensure that my address data was clean, verified, and standardized against real reference data. As reviewed here, it’s easy to see how both SQL Server 2012 and DQS work to take what used to require a highly skilled developer, and empower an average business or database person to consume external services and clean data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, T SQL, Technology Tagged: DQS

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  • Big Data – Is Big Data Relevant to me? – Big Data Questionnaires – Guest Post by Vinod Kumar

    - by Pinal Dave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions of SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. I think the series from Pinal is a good one for anyone planning to start on Big Data journey from the basics. In my daily customer interactions this buzz of “Big Data” always comes up, I react generally saying – “Sir, do you really have a ‘Big Data’ problem or do you have a big Data problem?” Generally, there is a silence in the air when I ask this question. Data is everywhere in organizations – be it big data, small data, all data and for few it is bad data which is same as no data :). Wow, don’t discount me as someone who opposes “Big Data”, I am a big supporter as much as I am a critic of the abuse of this term by the people. In this post, I wanted to let my mind flow so that you can also think in the direction I want you to see these concepts. In any case, this is not an exhaustive dump of what is in my mind – but you will surely get the drift how I am going to question Big Data terms from customers!!! Is Big Data Relevant to me? Many of my customers talk to me like blank whiteboard with no idea – “why Big Data”. They want to jump into the bandwagon of technology and they want to decipher insights from their unexplored data a.k.a. unstructured data with structured data. So what are these industry scenario’s that come to mind? Here are some of them: Financials Fraud detection: Banks and Credit cards are monitoring your spending habits on real-time basis. Customer Segmentation: applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. Customer Sentiment Analysis: Responding to negative brand perception on social or amplify the positive perception. Sales and Marketing Campaign: Understand the impact and get closer to customer delight. Call Center Analysis: attempt to take unstructured voice recordings and analyze them for content and sentiment. Medical Reduce Re-admissions: How to build a proactive follow-up engagements with patients. Patient Monitoring: How to track Inpatient, Out-Patient, Emergency Visits, Intensive Care Units etc. Preventive Care: Disease identification and Risk stratification is a very crucial business function for medical. Claims fraud detection: There is no precise dollars that one can put here, but this is a big thing for the medical field. Retail Customer Sentiment Analysis, Customer Care Centers, Campaign Management. Supply Chain Analysis: Every sensors and RFID data can be tracked for warehouse space optimization. Location based marketing: Based on where a check-in happens retail stores can be optimize their marketing. Telecom Price optimization and Plans, Finding Customer churn, Customer loyalty programs Call Detail Record (CDR) Analysis, Network optimizations, User Location analysis Customer Behavior Analysis Insurance Fraud Detection & Analysis, Pricing based on customer Sentiment Analysis, Loyalty Management Agents Analysis, Customer Value Management This list can go on to other areas like Utility, Manufacturing, Travel, ITES etc. So as you can see, there are obviously interesting use cases for each of these industry verticals. These are just representative list. Where to start? A lot of times I try to quiz customers on a number of dimensions before starting a Big Data conversation. Are you getting the data you need the way you want it and in a timely manner? Can you get in and analyze the data you need? How quickly is IT to respond to your BI Requests? How easily can you get at the data that you need to run your business/department/project? How are you currently measuring your business? Can you get the data you need to react WITHIN THE QUARTER to impact behaviors to meet your numbers or is it always “rear-view mirror?” How are you measuring: The Brand Customer Sentiment Your Competition Your Pricing Your performance Supply Chain Efficiencies Predictive product / service positioning What are your key challenges of driving collaboration across your global business?  What the challenges in innovation? What challenges are you facing in getting more information out of your data? Note: Garbage-in is Garbage-out. Hold good for all reporting / analytics requirements Big Data POCs? A number of customers get into the realm of setting a small team to work on Big Data – well it is a great start from an understanding point of view, but I tend to ask a number of other questions to such customers. Some of these common questions are: To what degree is your advanced analytics (natural language processing, sentiment analysis, predictive analytics and classification) paired with your Big Data’s efforts? Do you have dedicated resources exploring the possibilities of advanced analytics in Big Data for your business line? Do you plan to employ machine learning technology while doing Advanced Analytics? How is Social Media being monitored in your organization? What is your ability to scale in terms of storage and processing power? Do you have a system in place to sort incoming data in near real time by potential value, data quality, and use frequency? Do you use event-driven architecture to manage incoming data? Do you have specialized data services that can accommodate different formats, security, and the management requirements of multiple data sources? Is your organization currently using or considering in-memory analytics? To what degree are you able to correlate data from your Big Data infrastructure with that from your enterprise data warehouse? Have you extended the role of Data Stewards to include ownership of big data components? Do you prioritize data quality based on the source system (that is Facebook/Twitter data has lower quality thresholds than radio frequency identification (RFID) for a tracking system)? Do your retention policies consider the different legal responsibilities for storing Big Data for a specific amount of time? Do Data Scientists work in close collaboration with Data Stewards to ensure data quality? How is access to attributes of Big Data being given out in the organization? Are roles related to Big Data (Advanced Analyst, Data Scientist) clearly defined? How involved is risk management in the Big Data governance process? Is there a set of documented policies regarding Big Data governance? Is there an enforcement mechanism or approach to ensure that policies are followed? Who is the key sponsor for your Big Data governance program? (The CIO is best) Do you have defined policies surrounding the use of social media data for potential employees and customers, as well as the use of customer Geo-location data? How accessible are complex analytic routines to your user base? What is the level of involvement with outside vendors and third parties in regard to the planning and execution of Big Data projects? What programming technologies are utilized by your data warehouse/BI staff when working with Big Data? These are some of the important questions I ask each customer who is actively evaluating Big Data trends for their organizations. These questions give you a sense of direction where to start, what to use, how to secure, how to analyze and more. Sign off Any Big data is analysis is incomplete without a compelling story. The best way to understand this is to watch Hans Rosling – Gapminder (2:17 to 6:06) videos about the third world myths. Don’t get overwhelmed with the Big Data buzz word, the destination to what your data speaks is important. In this blog post, we did not particularly look at any Big Data technologies. This is a set of questionnaire one needs to keep in mind as they embark their journey of Big Data. I did write some of the basics in my blog: Big Data – Big Hype yet Big Opportunity. Do let me know if these questions make sense?  Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – Basics of Big Data Architecture – Day 4 of 21

    - by Pinal Dave
    In yesterday’s blog post we understood how Big Data evolution happened. Today we will understand basics of the Big Data Architecture. Big Data Cycle Just like every other database related applications, bit data project have its development cycle. Though three Vs (link) for sure plays an important role in deciding the architecture of the Big Data projects. Just like every other project Big Data project also goes to similar phases of the data capturing, transforming, integrating, analyzing and building actionable reporting on the top of  the data. While the process looks almost same but due to the nature of the data the architecture is often totally different. Here are few of the question which everyone should ask before going ahead with Big Data architecture. Questions to Ask How big is your total database? What is your requirement of the reporting in terms of time – real time, semi real time or at frequent interval? How important is the data availability and what is the plan for disaster recovery? What are the plans for network and physical security of the data? What platform will be the driving force behind data and what are different service level agreements for the infrastructure? This are just basic questions but based on your application and business need you should come up with the custom list of the question to ask. As I mentioned earlier this question may look quite simple but the answer will not be simple. When we are talking about Big Data implementation there are many other important aspects which we have to consider when we decide to go for the architecture. Building Blocks of Big Data Architecture It is absolutely impossible to discuss and nail down the most optimal architecture for any Big Data Solution in a single blog post, however, we can discuss the basic building blocks of big data architecture. Here is the image which I have built to explain how the building blocks of the Big Data architecture works. Above image gives good overview of how in Big Data Architecture various components are associated with each other. In Big Data various different data sources are part of the architecture hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as non relational data marts and data warehousing solutions. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented. NoSQL in Data Management NoSQL is a very famous buzz word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which takes care of all the kind of data. This is collectively called NoSQL. Tomorrow Next four days we will answer the Buzz Words – Hadoop. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – Evolution of Big Data – Day 3 of 21

    - by Pinal Dave
    In yesterday’s blog post we answered what is the Big Data. Today we will understand why and how the evolution of Big Data has happened. Though the answer is very simple, I would like to tell it in the form of a history lesson. Data in Flat File In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world. It was more like a nightmare when there was any data processing involved in the application. Though, applications developed at that time were also not that advanced the need of the data was always there and there was always need of proper data management. Edgar F Codd and 12 Rules Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models. Relational Database Management Systems Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way. Enormous Data Growth Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application. Data Warehousing The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present. Interesting Challenge Every organization had expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need of the real time intelligence from the fast paced data flow is now becoming a necessity. Large amount (Volume) of difference (Variety) of high speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users. Big Data is Reality! Tomorrow In tomorrow’s blog post we will try to answer discuss Basics of Big Data Architecture. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Working with Temporal Data in SQL Server

    - by Dejan Sarka
    My third Pluralsight course, Working with Temporal Data in SQL Server, is published. I am really proud on the second part of the course, where I discuss optimization of temporal queries. This was a nearly impossible task for decades. First solutions appeared only lately. I present all together six solutions (and one more that is not a solution), and I invented four of them. http://pluralsight.com/training/Courses/TableOfContents/working-with-temporal-data-sql-server

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  • New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

    - by charlie.berger
    Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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  • Big Data – How to become a Data Scientist and Learn Data Science? – Day 19 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the analytics in Big Data Story. In this article we will understand how to become a Data Scientist for Big Data Story. Data Scientist is a new buzz word, everyone seems to be wanting to become Data Scientist. Let us go over a few key topics related to Data Scientist in this blog post. First of all we will understand what is a Data Scientist. In the new world of Big Data, I see pretty much everyone wants to become Data Scientist and there are lots of people I have already met who claims that they are Data Scientist. When I ask what is their role, I have got a wide variety of answers. What is Data Scientist? Data scientists are the experts who understand various aspects of the business and know how to strategies data to achieve the business goals. They should have a solid foundation of various data algorithms, modeling and statistics methodology. What do Data Scientists do? Data scientists understand the data very well. They just go beyond the regular data algorithms and builds interesting trends from available data. They innovate and resurrect the entire new meaning from the existing data. They are artists in disguise of computer analyst. They look at the data traditionally as well as explore various new ways to look at the data. Data Scientists do not wait to build their solutions from existing data. They think creatively, they think before the data has entered into the system. Data Scientists are visionary experts who understands the business needs and plan ahead of the time, this tremendously help to build solutions at rapid speed. Besides being data expert, the major quality of Data Scientists is “curiosity”. They always wonder about what more they can get from their existing data and how to get maximum out of future incoming data. Data Scientists do wonders with the data, which goes beyond the job descriptions of Data Analysist or Business Analysist. Skills Required for Data Scientists Here are few of the skills a Data Scientist must have. Expert level skills with statistical tools like SAS, Excel, R etc. Understanding Mathematical Models Hands-on with Visualization Tools like Tableau, PowerPivots, D3. j’s etc. Analytical skills to understand business needs Communication skills On the technology front any Data Scientists should know underlying technologies like (Hadoop, Cloudera) as well as their entire ecosystem (programming language, analysis and visualization tools etc.) . Remember that for becoming a successful Data Scientist one require have par excellent skills, just having a degree in a relevant education field will not suffice. Final Note Data Scientists is indeed very exciting job profile. As per research there are not enough Data Scientists in the world to handle the current data explosion. In near future Data is going to expand exponentially, and the need of the Data Scientists will increase along with it. It is indeed the job one should focus if you like data and science of statistics. Courtesy: emc Tomorrow In tomorrow’s blog post we will discuss about various Big Data Learning resources. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Unstructured Data - The future of Data Administration

    Some have claimed that there is a problem with the way data is currently managed using the relational paradigm do to the rise of unstructured data in modern business. PCMag.com defines unstructured data as data that does not reside in a fixed location. They further explain that unstructured data refers to data in a free text form that is not bound to any specific structure. With the rise of unstructured data in the form of emails, spread sheets, images and documents the critics have a right to argue that the relational paradigm is not as effective as the object oriented data paradigm in managing this type of data. The relational paradigm relies heavily on structure and relationships in and between items of data. This type of paradigm works best in a relation database management system like Microsoft SQL, MySQL, and Oracle because data is forced to conform to a structure in the form of tables and relations can be derived from the existence of one or more tables. These critics also claim that database administrators have not kept up with reality because their primary focus in regards to data administration deals with structured data and the relational paradigm. The relational paradigm was developed in the 1970’s as a way to improve data management when compared to standard flat files. Little has changed since then, and modern database administrators need to know more than just how to handle structured data. That is why critics claim that today’s data professionals do not have the proper skills in order to store and maintain data for modern systems when compared to the skills of system designers, programmers , software engineers, and data designers  due to the industry trend of object oriented design and development. I think that they are wrong. I do not disagree that the industry is moving toward an object oriented approach to development with the potential to use more of an object oriented approach to data.   However, I think that it is business itself that is limiting database administrators from changing how data is stored because of the potential costs, and impact that might occur by altering any part of stored data. Furthermore, database administrators like all technology workers constantly are trying to improve their technical skills in order to excel in their job, so I think that accusing data professional is not just when the root cause of the lack of innovation is controlled by business, and it is business that will suffer for their inability to keep up with technology. One way for database professionals to better prepare for the future of database management is start working with data in the form of objects and so that they can extract data from the objects so that the stored information within objects can be used in relation to the data stored in a using the relational paradigm. Furthermore, I think the use of pattern matching will increase with the increased use of unstructured data because object can be selected, filtered and altered based on the existence of a pattern found within an object.

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  • Developing an analytics's system processing large amounts of data - where to start

    - by Ryan
    Imagine you're writing some sort of Web Analytics system - you're recording raw page hits along with some extra things like tagging cookies etc and then producing stats such as Which pages got most traffic over a time period Which referers sent most traffic Goals completed (goal being a view of a particular page) And more advanced things like which referers sent the most number of vistors who later hit a goal. The naieve way of approaching this would be to throw it in a relational database and run queries over it - but that won't scale. You could pre-calculate everything (have a queue of incoming 'hits' and use to update report tables) - but what if you later change a goal - how could you efficiently re-calculate just the data that would be effected. Obviously this has been done before ;) so any tips on where to start, methods & examples, architecture, technologies etc.

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  • Big Data – Beginning Big Data – Day 1 of 21

    - by Pinal Dave
    What is Big Data? I want to learn Big Data. I have no clue where and how to start learning about it. Does Big Data really means data is big? What are the tools and software I need to know to learn Big Data? I often receive questions which I mentioned above. They are good questions and honestly when we search online, it is hard to find authoritative and authentic answers. I have been working with Big Data and NoSQL for a while and I have decided that I will attempt to discuss this subject over here in the blog. In the next 21 days we will understand what is so big about Big Data. Big Data – Big Thing! Big Data is becoming one of the most talked about technology trends nowadays. The real challenge with the big organization is to get maximum out of the data already available and predict what kind of data to collect in the future. How to take the existing data and make it meaningful that it provides us accurate insight in the past data is one of the key discussion points in many of the executive meetings in organizations. With the explosion of the data the challenge has gone to the next level and now a Big Data is becoming the reality in many organizations. Big Data – A Rubik’s Cube I like to compare big data with the Rubik’s cube. I believe they have many similarities. Just like a Rubik’s cube it has many different solutions. Let us visualize a Rubik’s cube solving challenge where there are many experts participating. If you take five Rubik’s cube and mix up the same way and give it to five different expert to solve it. It is quite possible that all the five people will solve the Rubik’s cube in fractions of the seconds but if you pay attention to the same closely, you will notice that even though the final outcome is the same, the route taken to solve the Rubik’s cube is not the same. Every expert will start at a different place and will try to resolve it with different methods. Some will solve one color first and others will solve another color first. Even though they follow the same kind of algorithm to solve the puzzle they will start and end at a different place and their moves will be different at many occasions. It is  nearly impossible to have a exact same route taken by two experts. Big Market and Multiple Solutions Big Data is exactly like a Rubik’s cube – even though the goal of every organization and expert is same to get maximum out of the data, the route and the starting point are different for each organization and expert. As organizations are evaluating and architecting big data solutions they are also learning the ways and opportunities which are related to Big Data. There is not a single solution to big data as well there is not a single vendor which can claim to know all about Big Data. Honestly, Big Data is too big a concept and there are many players – different architectures, different vendors and different technology. What is Next? In this 31 days series we will be exploring many essential topics related to big data. I do not claim that you will be master of the subject after 31 days but I claim that I will be covering following topics in easy to understand language. Architecture of Big Data Big Data a Management and Implementation Different Technologies – Hadoop, Mapreduce Real World Conversations Best Practices Tomorrow In tomorrow’s blog post we will try to answer one of the very essential questions – What is Big Data? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Reading data from an Entity Framework data model through a WCF Data Service

    - by nikolaosk
    This is going to be the fourth post of a series of posts regarding ASP.Net and the Entity Framework and how we can use Entity Framework to access our datastore. You can find the first one here , the second one here and the third one here . I have a post regarding ASP.Net and EntityDataSource. You can read it here .I have 3 more posts on Profiling Entity Framework applications. You can have a look at them here , here and here . Microsoft with .Net 3.0 Framework, introduced WCF. WCF is Microsoft's...(read more)

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  • Big Data’s Killer App…

    - by jean-pierre.dijcks
    Recently Keith spent  some time talking about the cloud on this blog and I will spare you my thoughts on the whole thing. What I do want to write down is something about the Big Data movement and what I think is the killer app for Big Data... Where is this coming from, ok, I confess... I spent 3 days in cloud land at the Cloud Connect conference in Santa Clara and it was quite a lot of fun. One of the nice things at Cloud Connect was that there was a track dedicated to Big Data, which prompted me to some extend to write this post. What is Big Data anyways? The most valuable point made in the Big Data track was that Big Data in itself is not very cool. Doing something with Big Data is what makes all of this cool and interesting to a business user! The other good insight I got was that a lot of people think Big Data means a single gigantic monolithic system holding gazillions of bytes or documents or log files. Well turns out that most people in the Big Data track are talking about a lot of collections of smaller data sets. So rather than thinking "big = monolithic" you should be thinking "big = many data sets". This is more than just theoretical, it is actually relevant when thinking about big data and how to process it. It is important because it means that the platform that stores data will most likely consist out of multiple solutions. You may be storing logs on something like HDFS, you may store your customer information in Oracle and you may store distilled clickstream information in some distilled form in MySQL. The big question you will need to solve is not what lives where, but how to get it all together and get some value out of all that data. NoSQL and MapReduce Nope, sorry, this is not the killer app... and no I'm not saying this because my business card says Oracle and I'm therefore biased. I think language is important, but as with storage I think pragmatic is better. In other words, some questions can be answered with SQL very efficiently, others can be answered with PERL or TCL others with MR. History should teach us that anyone trying to solve a problem will use any and all tools around. For example, most data warehouses (Big Data 1.0?) get a lot of data in flat files. Everyone then runs a bunch of shell scripts to massage or verify those files and then shoves those files into the database. We've even built shell script support into external tables to allow for this. I think the Big Data projects will do the same. Some people will use MapReduce, although I would argue that things like Cascading are more interesting, some people will use Java. Some data is stored on HDFS making Cascading the way to go, some data is stored in Oracle and SQL does do a good job there. As with storage and with history, be pragmatic and use what fits and neither NoSQL nor MR will be the one and only. Also, a language, while important, does in itself not deliver business value. So while cool it is not a killer app... Vertical Behavioral Analytics This is the killer app! And you are now thinking: "what does that mean?" Let's decompose that heading. First of all, analytics. I would think you had guessed by now that this is really what I'm after, and of course you are right. But not just analytics, which has a very large scope and means many things to many people. I'm not just after Business Intelligence (analytics 1.0?) or data mining (analytics 2.0?) but I'm after something more interesting that you can only do after collecting large volumes of specific data. That all important data is about behavior. What do my customers do? More importantly why do they behave like that? If you can figure that out, you can tailor web sites, stores, products etc. to that behavior and figure out how to be successful. Today's behavior that is somewhat easily tracked is web site clicks, search patterns and all of those things that a web site or web server tracks. that is where the Big Data lives and where these patters are now emerging. Other examples however are emerging, and one of the examples used at the conference was about prediction churn for a telco based on the social network its members are a part of. That social network is not about LinkedIn or Facebook, but about who calls whom. I call you a lot, you switch provider, and I might/will switch too. And that just naturally brings me to the next word, vertical. Vertical in this context means per industry, e.g. communications or retail or government or any other vertical. The reason for being more specific than just behavioral analytics is that each industry has its own data sources, has its own quirky logic and has its own demands and priorities. Of course, the methods and some of the software will be common and some will have both retail and service industry analytics in place (your corner coffee store for example). But the gist of it all is that analytics that can predict customer behavior for a specific focused group of people in a specific industry is what makes Big Data interesting. Building a Vertical Behavioral Analysis System Well, that is going to be interesting. I have not seen much going on in that space and if I had to have some criticism on the cloud connect conference it would be the lack of concrete user cases on big data. The telco example, while a step into the vertical behavioral part is not really on big data. It used a sample of data from the customers' data warehouse. One thing I do think, and this is where I think parts of the NoSQL stuff come from, is that we will be doing this analysis where the data is. Over the past 10 years we at Oracle have called this in-database analytics. I guess we were (too) early? Now the entire market is going there including companies like SAS. In-place btw does not mean "no data movement at all", what it means that you will do this on data's permanent home. For SAS that is kind of the current problem. Most of the inputs live in a data warehouse. So why move it into SAS and back? That all worked with 1 TB data warehouses, but when we are looking at 100TB to 500 TB of distilled data... Comments? As it is still early days with these systems, I'm very interested in seeing reactions and thoughts to some of these thoughts...

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  • Accelerate your SOA with Data Integration - Live Webinar Tuesday!

    - by dain.hansen
    Need to put wind in your SOA sails? Organizations are turning more and more to Real-time data integration to complement their Service Oriented Architecture. The benefit? Lowering costs through consolidating legacy systems, reducing risk of bad data polluting their applications, and shortening the time to deliver new service offerings. Join us on Tuesday April 13th, 11AM PST for our live webinar on the value of combining SOA and Data Integration together. In this webcast you'll learn how to innovate across your applications swiftly and at a lower cost using Oracle Data Integration technologies: Oracle Data Integrator Enterprise Edition, Oracle GoldenGate, and Oracle Data Quality. You'll also hear: Best practices for building re-usable data services that are high performing and scalable across the enterprise How real-time data integration can maximize SOA returns while providing continuous availability for your mission critical applications Architectural approaches to speed service implementation and delivery times, with pre-integrations to CRM, ERP, BI, and other packaged applications Register now for this live webinar!

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  • Big Data – Basics of Big Data Analytics – Day 18 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the various components in Big Data Story. In this article we will understand what are the various analytics tasks we try to achieve with the Big Data and the list of the important tools in Big Data Story. When you have plenty of the data around you what is the first thing which comes to your mind? “What do all these data means?” Exactly – the same thought comes to my mind as well. I always wanted to know what all the data means and what meaningful information I can receive out of it. Most of the Big Data projects are built to retrieve various intelligence all this data contains within it. Let us take example of Facebook. When I look at my friends list of Facebook, I always want to ask many questions such as - On which date my maximum friends have a birthday? What is the most favorite film of my most of the friends so I can talk about it and engage them? What is the most liked placed to travel my friends? Which is the most disliked cousin for my friends in India and USA so when they travel, I do not take them there. There are many more questions I can think of. This illustrates that how important it is to have analysis of Big Data. Here are few of the kind of analysis listed which you can use with Big Data. Slicing and Dicing: This means breaking down your data into smaller set and understanding them one set at a time. This also helps to present various information in a variety of different user digestible ways. For example if you have data related to movies, you can use different slide and dice data in various formats like actors, movie length etc. Real Time Monitoring: This is very crucial in social media when there are any events happening and you wanted to measure the impact at the time when the event is happening. For example, if you are using twitter when there is a football match, you can watch what fans are talking about football match on twitter when the event is happening. Anomaly Predication and Modeling: If the business is running normal it is alright but if there are signs of trouble, everyone wants to know them early on the hand. Big Data analysis of various patterns can be very much helpful to predict future. Though it may not be always accurate but certain hints and signals can be very helpful. For example, lots of data can help conclude that if there is lots of rain it can increase the sell of umbrella. Text and Unstructured Data Analysis: unstructured data are now getting norm in the new world and they are a big part of the Big Data revolution. It is very important that we Extract, Transform and Load the unstructured data and make meaningful data out of it. For example, analysis of lots of images, one can predict that people like to use certain colors in certain months in their cloths. Big Data Analytics Solutions There are many different Big Data Analystics Solutions out in the market. It is impossible to list all of them so I will list a few of them over here. Tableau – This has to be one of the most popular visualization tools out in the big data market. SAS – A high performance analytics and infrastructure company IBM and Oracle – They have a range of tools for Big Data Analysis Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Data Scientist. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

    - by Pinal Dave
    Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • The Data Scientist

    - by BuckWoody
    A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it. In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets. The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly. I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas. Persistence The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all? This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization. Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to. Processing Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on. A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one. Presentation This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays. This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does. And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist. Why I’m excited I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail. Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data. So  watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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  • Fraud Detection with the SQL Server Suite Part 2

    - by Dejan Sarka
    This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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  • Big Data – Beginning Big Data Series Next Month in 21 Parts

    - by Pinal Dave
    Big Data is the next big thing. There was a time when we used to talk in terms of MB and GB of the data. However, the industry is changing and we are now moving to a conversation where we discuss about data in Petabyte, Exabyte and Zettabyte. It seems that the world is now talking about increased Volume of the data. In simple world we all think that Big Data is nothing but plenty of volume. In reality Big Data is much more than just a huge volume of the data. When talking about the data we need to understand about variety and volume along with volume. Though Big data look like a simple concept, it is extremely complex subject when we attempt to start learning the same. My Journey I have recently presented on Big Data in quite a few organizations and I have received quite a few questions during this roadshow event. I have collected all the questions which I have received and decided to post about them on the blog. In the month of October 2013, on every weekday we will be learning something new about Big Data. Every day I will share a concept/question and in the same blog post we will learn the answer of the same. Big Data – Plenty of Questions I received quite a few questions during my road trip. Here are few of the questions. I want to learn Big Data – where should I start? Do I need to know SQL to learn Big Data? What is Hadoop? There are so many organizations talking about Big Data, and every one has a different approach. How to start with big Data? Do I need to know Java to learn about Big Data? What is different between various NoSQL languages. I will attempt to answer most of the questions during the month long series in the next month. Big Data – Big Subject Big Data is a very big subject and I no way claim that I will be covering every single big data concept in this series. However, I promise that I will be indeed sharing lots of basic concepts which are revolving around Big Data. We will discuss from fundamentals about Big Data and continue further learning about it. I will attempt to cover the concept so simple that many of you might have wondered about it but afraid to ask. Your Role! During this series next month, I need your one help. Please keep on posting questions you might have related to big data as blog post comments and on Facebook Page. I will monitor them closely and will try to answer them as well during this series. Now make sure that you do not miss any single blog post in this series as every blog post will be linked to each other. You can subscribe to my feed or like my Facebook page or subscribe via email (by entering email in the blog post). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Big Data, PostADay, SQL, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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