Unmanage Master Data Management

Master Data Management is a discipline which tries to create, maintain and manage a single, standardized conceptual information model of all of an enterprise’s data structures. Taking as its goal that all IT systems eventually will be unified under a single semantic description so that information from all corners of the business can be understood and managed as a whole.

In my opinion, while I agree with the ultimate goal of information interoperability across the enterprise, I disagree with the approach usually taken to get there. A strategy that I might call:

  • Data Management with Multiple Masters
  • Uncontrolled/Unmanaged Master Data Management
  • Associative Search on an Uncontrolled Vocabulary
  • Emergent Data Management (added 2015)
  • Master-less Data Management (added 2015)

takes a different approach. The basic strategy is to permit multiple vocabularies to exist in the enterprise (one for each major context that can be identified). Then we build a cross reference of the semantics only describing the edges between these contexts (the “bridging” contexts between organizations within the enterprise), where interfaces exist. The interfaces that would be described and captured in this way would include non-automated ones (e.g., human mediated interfaces) as well as the traditionally documented software interfaces.

Instead of requiring that the entire content of each context be documented and standardized, this approach would provide the touchpoints between contexts only. New software (or business) integration tasks which the enterprise takes on would require new interfaces and new extensions of mappings, but would only have to cover the content of the new bridging context.

Information collected and maintained under this strategy would include the categorization of data element structures as follows:

  1. Data structure syntax and basic manipulations
  2. Origin Context and element Role (for example, markers versus non-markers)
  3. Storage types: transient (not stored), temporary (e.g. staging schemas and work tables), permanent (e.g., structures which are intended to provide the longest storage
  4. “Pass-through” versus “consumed” data elements. Also called “traveller” and “fodder”, these data structures and elements have no meaning and possibly no existence (respectively) in the Target Context.

For data symbols that are just “passing through” one context to another, these would be the traveller symbols (as discussed on one of my permanent pages and in the glossary) whose structure is simply moved unchanged from one context to the next, until it reaches a context which recognizes and uses them. “Fodder” symbols are used to trigger some logic or filter to change the operation of the bridging context software, but once consumed, do not move beyond the bridge.

The problem that I have encountered with MDM efforts is that they don’t try to scope themselves to what is RECOGNIZABLY REQUIRED. Instead, the focus is on the much larger, much riskier effort of the attempted elimination of local contexts within the enterprise. MDM breaks down in the moment it becomes divorced from a practical, immediate attempt to capture just what is needed today. The moment it attempts to “bank” standard symbols ahead of their usage, the MDM process becomes speculative, and proscriptive. The likelihood of wasting time on symbology which ultimately is wrong and unused is very high, once steps past the interface and into the larger contexts are taken.

Uses of Metamorphic Models in Data Management and Governance

In the Master Data Management arena, Metamorphic Models would allow the capture of the data elements necessary to stitch together an enterprise. By recognizing the information needed to pass as markers or to act as travellers, the scope of the data governance task should be reducible to a practical minimum.

Then the data governance problem can be built up only as needed. The task becomes, properly, just another project-related activity similar to Change Control and Risk Management, instead of the academic exercise into which it often devolves.

The scope of data management should focus on and document 100% of the data being moved across interfaces, whether these interfaces are automated or human-performed. Simple data can just be documented, and the equivalence of syntax and semantics captured. Data elements that act as markers for the processes should be recorded. Also all data elements/structures intended merely to make the trip as travellers should be indicated.

This approach addresses the high-value portion of the enterprise’s data structures, while minimizing work on documenting concepts which only apply within a particular context.

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