Wouldn’t you like to have all of your processes working synchronously off of a conformed set of Master Data?
With a bit of ambition, you may even envision parallel processes utilizing this master data to harmonize the customer and business experience into something more efficient. If you’ve tried, I bet you’ve found that it’s like herding kittens. The moment something changes, synchronisation becomes an issue because so many departments have very particular perspectives on Master Data.
If you could easily achieve a Master Data ‘golden record’, the customer experience would be an evolving seamless experience. In terms of record capture and utilization, there would be the real possibility of running parallel business processes. It would reduce customer-perceived response timings for fulfillment or for single sign-on!
But what if one of your processes needs a modification due to internal or external influences?
Maybe you will need to capture additional information due to company structural transformation, or limit information capture due to regulation. Perhaps you have acquired a new business asset which is producing or consuming additional data that requires that you rethink your data model.
Your business process driven data capture and MDM (Master Data Management) is now in a state of change. This puts the overall data system at risk of coming out of not reflecting reality.
At the very least it must be scrutinized for impacts of change.
Build change into the heart of Master Data
MDM is clearly achievable if you can harness change, but change is a big challenge. You first have to hit upon an approach that incorporates change into the heart of data management, and that keeps up with your processes changes. If you don’t, then there is an implicit resistance/latency to between reality and your concept of Master Data.
If you are using old « Star-Schema » or « Inmon » approaches, the changes you need will take some time. Probably a lot longer than you expect if you’ve had the experience of most enterprises.
These approaches are broadly accepted as ‘tried and tested’ largely because they have been in the industry for several decades. However, we all know that longevity is not the same as suitability. The level of change we are experiencing today was unthinkable when these older methodologies were conceived. Plus, we’ve even moved on from their updates (read this for more).
If your goal is to allow your business processes to change without affecting your MDM integrity, then you need a methodology which locks in data. It remains trustworthy, while flexing to accommodate changes and updates.
If you can achieve a system of record that achieves both this stability and flexibility to feed your MDM, then you are on the path to a sustainable system of record upon which to build your current and future MDM and synchronised processes.
Virtualized perspectives of data history
Data Vault is the methodology that is increasingly being adopted by perceptive companies who realise that change is inevitable and who embrace it. Data Vault is a discipline which is a hybrid of the old « Star-Schema » and « Inmon » techniques. In fact, it is more closely related to modern « Graph » structures which capture both structure and relationship to reduce the impact of change seen in old approaches. By separating out concerns of structure and relationship, change becomes a fluid part of the model.
Consequently, changes DO NOT impact on current data, introducing finely tuned control.
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