A Canonical Model is the marriage of your data’s business semantics and the related business rules governing your enterprise asset. Your data assets can be represented by structure (Relational Data) or non- structure (Big Data) in multiple ontological frameworks. Your business semantic is composed of the natural business language used for conducting its affairs stored in the model. It should reflect the terms of how the Business conducts its affairs in the course of events. And the set of rules, canon law, come in the form of explicit policies governing the conduct of the organization (SOA).
So do we need canonical data modeling? I think so. I’ve been a canonical practitioner for a couple of years where an organization chooses to integrate its SOA, Relational, and Big Data environments into a corporate enterprise data model. This environment allows your organization to better leverage its web services, aligns those services to the corporate data model, and enforces a common language dictated by the owners (the Business).
Is the canonical data model of interest to you? If so, please give me a thumbs up. In my next blog entry, I plan to show readers how to set up a Canonical Modeling environment (Part 2) and execute its implementation in a working IT environment (Part 3). The emphasis of implementation will apply a few simple best practices:
- Managing the Canonical model in a tool agnostic setting (Part 2)
- Enterprise Model driven development and data governance (Part 3)
- Integration of your business rules using common ETL and SOA practices (Part 3)
We will summarize the discussion (Part 4) on why a Canonical Modeling environment provides greater integration between applications and how it better aligns the Business Strategy to the IT strategy. And we’ll provide some real world examples where I have successfully implemented a Canonical Modeling effort.
Thank you.
