I heart the Data Management Association’s Data Management Functional Framework wheel. I really do. It provides a great anchoring point from which to prioritize and work through an organization’s data management challenges. I tend to get a lot of questions though from clients regarding what that framework really means in terms of managing the day-to-day activities and actually implementing projects.
A spider map can help an organization bridge the framework gap from conceptual to tactical. A spider map will drill down into a specific use case and show which pieces of the functional framework apply, and more specifically, what activities within those framework pieces are needed to support the use case.
Let’s take an example. A healthcare organization has prioritized consumer engagement as a major strategic initiative. The current state environment presents with the following data management challenges:
- a nascent data governance program with a small team and a recently formed cross-functional task force to support the development of enterprise policies, processes and standards for consumer engagement
- no master data management
- a meta data repository that is being implemented
- no enterprise data models, conceptual or logical
- not enterprise data dictionary
- no inventory of data assets
- data quality efforts underway in the enterprise data warehouse only
- enterprise security policies in place but not tied to data assets except in an excel spreadsheet
- a newly formed initiative for enterprise content management tied to lifecycle governance, sitting outside of the company’s data management efforts
The following spider map was created for the organization to help delineate the actual work activities that are important to the strategic consumer engagement objectives. You’ll see that this spider map has as its center circle the use case, Consumer Engagement. The surrounding seven circles are the functional framework components and activities that are most important to supporting this business priority.
If we tease apart a few of the circles the, we get to a deeper level of understanding how data management and data governance activities specifically will support this initiative – and actually, are crucial to the success of this initiative.
- Data governance
- What do we mean by a “consumer”? Is that the same as a “member”, or is it “broader”?
- As we merge data about a “consumer” together from multiple sources, how will we do that? What are the policies and processes that will govern it?
- Meta data
- What are the systems in which data about “consumers” exist?
- What attributes do we need about “consumers”? What systems, databases, or tables within databases are the authoritative sources of those attributes?
- We want to store specific data security classifications so they can be broadly searched and applied for access, authentication, use and re-use purposes.
- Master data
- Are we able to uniquely identify a “consumer” across the multiple systems in which we have information about them?
- Do we have unique, cradle-to-grave ids for “consumers”? We will need this for cross-channel integration and to append new information to the correct “consumer”.
- Data quality
- What is the quality of the “consumer” data that we have in-house? Externally sourced?
- If we match/merge data from multiple systems to create a master record, what quality and metrics do we need in a match to ensure uniqueness of identity?
As you can see from this example, a spider map can be leveraged to help the client visualize how to go from the conceptual level to a very specific set of tactical activities needed to support an enterprise project or use case. A spider map is also a story about what needs to be done, and supports enterprise communication and stakeholder collaboration efforts. As the old adage goes, a picture is worth a thousand words.