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Posted by Mike Gilpin on May 27, 2011
I recently attended the second annual “Canonical Model Management Forum” at the Washington Plaza Hotel in Washington, DC (see here for my post about last year’s, first meeting, including Forrester’s definition of canonical modeling). Enterprise or information architects from a number of government agencies as well as several of the major banks, insurance companies, retailers, credit-card operators, and other private-sector firms attended the meeting. There was one vendor sponsor (DigitalML, the vendor of IgniteXML). There were a number of presentations by the attendees about their environments, what had motivated them to establish a canonical model, how that work had turned out, and the important lessons learned.
Last year I also had some recent Forrester survey results to share – we have not yet rerun that survey, but we are on the verge of rerunning it, so I’ll post some key results from that once the data is available.
Last year’s post is still the place to go to get the general overview about why to do canonical modeling, the main use cases, some areas of controversy (still raging), and a list of best practices I heard attendees agree upon.
What’s New In 2011?
Based both on what I heard at this meeting and on other recent interviews:
The main motivation for canonical models is still to increase reuse of shared services, also making them easier and faster to consume. One large oil company I interviewed recently has been measuring this since going live in 2009 and found that 40% of new requests for access to data can now be satisfied by an existing data service. At this year’s CMM Forum, Novartis presented results of its canonical modeling efforts since early 2009 showing reuse ranging from 20% on early projects up to 100% on later projects in domains for study management, drug delivery, and customer master.
But one might ask – what’s the model of the “Big Data”? The underlying data may be structured, semistructured, or unstructured (think Twitter streams), but once analytics extract insights from the data, a structure emerges that complements the canonical model. However, whereas the canonical model expresses a need to govern and standardize, the model of “Big Data” is often dynamic by nature, so don’t try to standardize it, except where particular insights such as phone calling behavior are reasonably stable over the life of a system. Insights from the Twitter stream will never have that kind of stability – think “trending topics.”
There’s much more I could say, but in the interest of time, I’ll stop there. The combination of canonical modeling, data services, and “Big Data” is generating a lot of activity and opportunities for innovation, so watch this space for more tidbits as they emerge. And if you’re a Forrester client and have a canonical modeling initiative you’re considering kicking off, please submit an inquiry to email@example.com on this topic, and I’d be happy to discuss the details of your program. Be sure to send some basic info about what you’re doing as part of the inquiry so I can prepare to give you maximum value.
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