Data Model Dissonance
- by Tony Davis
So often at the start of the development of database applications, there is a premature rush to the keyboard. Unless, before we get there, we’ve mapped out and agreed the three data models, the Conceptual, the Logical and the Physical, then the inevitable refactoring will dog development work. It pays to get the data models sorted out up-front, however ‘agile’ you profess to be.
The hardest model to get right, the most misunderstood, and the one most neglected by the various modeling tools, is the conceptual data model, and yet it is critical to all that follows. The conceptual model distils what the business understands about itself, and the way it operates. It represents the business rules that govern the required data, its constraints and its properties. The conceptual model uses the terminology of the business and defines the most important entities and their inter-relationships. Don’t assume that the organization’s understanding of these business rules is consistent or accurate. Too often, one department has a subtly different understanding of what an entity means and what it stores, from another. If our conceptual data model fails to resolve such inconsistencies, it will reduce data quality. If we don’t collect and measure the raw data in a consistent way across the whole business, how can we hope to perform meaningful aggregation?
The conceptual data model has more to do with business than technology, and as such, developers often regard it as a worthy but rather arcane ceremony like saluting the flag or only eating fish on Friday. However, the consequences of getting it wrong have a direct and painful impact on many aspects of the project.
If you adopt a silo-based (a.k.a. Domain driven) approach to development), you are still likely to suffer by starting with an incomplete knowledge of the domain. Even when you have surmounted these problems so that the data entities accurately reflect the business domain that the application represents, there are likely to be dire consequences from abandoning the goal of a shared, enterprise-wide understanding of the business.
In reading this, you may recall experiences of the consequence of getting the conceptual data model wrong. I believe that Phil Factor, for example, witnessed the abandonment of a multi-million dollar banking project due to an inadequate conceptual analysis of how the bank defined a ‘customer’.
We’d love to hear of any examples you know of development projects poleaxed by errors in the conceptual data model.
Cheers,
Tony
