Real-time Big Data Analytics is a reality for StubHub with Oracle Advanced Analytics
- by Mark Hornick
What can you use for a comprehensive platform for real-time analytics?
How can you process big data volumes for near-real-time recommendations and dramatically reduce fraud?
Learn in this video what Stubhub achieved with Oracle R Enterprise from the Oracle Advanced Analytics option to Oracle Database, and read more on their story here.
Advanced analytics solutions that impact the bottom line of a business are challenging due to the range of skills and individuals involved in realizing such solutions. While we hear a lot about the role of the data scientist, that role is but one piece of the puzzle. Advanced analytics solutions also have an operationalization aspect that also requires close proximity to where the transactional activity occurs.
The data scientist needs access to the right data with which to model the business problem. This involves IT for data collection, management, and administration, as well as ensuring zero downtime (a website needs to be up 24x7). This also involves working with the data scientist to keep predictive models refreshed with the latest scripts.
Integrating advanced analytics solutions into enterprise apps involves not just generating predictions, but supporting the whole life-cycle from data collection, to model building, model assessment, and then outcome assessment and feedback to the model building process again. Application and web interface designers need to take into account how end users will see and use the advanced analytics results, e.g., supporting operations staff that need to handle the potentially fraudulent transactions.
As just described, advanced analytics projects can be "complicated" from just a human perspective. The extent to which software can simplify the interactions among users and systems will increase the likelihood of project success. The ability to quickly operationalize advanced analytics projects and demonstrate measurable value, means the difference between a successful project and just a nice research report.
By standardizing on Oracle Database and SQL invocation of R, along with in-database modeling as found in Oracle Advanced Analytics, expedient model deployment and zero downtime for refreshing models becomes a reality. Meanwhile, data scientists are also able to explore leading edge techniques available in open source. The Oracle solution propels the entire organization forward to realize the value of advanced analytics.
