Recently I’ve had the opportunity to dig deeply into the CMMI Data Management Maturity model. Since its release, the DMM model has emerged as the de facto standard data management maturity framework (I’ve listed other frameworks at the end of this post).
I’m deeply impressed by the completeness and polish of the DMM model as a comprehensive catalog of processes required for effective data management. Even after decades in the business the broad scope and business focus of the model changed the way I think about data management.
Here are my impressions collected under five distinct categories.
We’re All In This Together
This is a weird thing to say about a business document, but there’s something uplifting and optimistic about the DMM model. The document simply describes each of 26 processes, maturity levels for each process, and questions to ascertain maturity. There’s no discussion of the resistance, process gone wrong, or “train wrecks” that features in so many data management discussions.
I was refreshed and energized to read a document that omitted the obstacles and implicitly assumed that everyone is working together to improve data management. After a couple of hundred pages all that inherent positivity seems to instill a “can do” attitude that changes the question from “why invest in data quality” to “why not?”.
Organizations Should Manage Data as an Asset
This reads as a cliche because we’re often told that we should but the methods for managing data seem out of reach in our own organizations, a frustration registered here and here. The DMM model identifies the processes required and illustrates that your organization already manages data as an asset, its just that some of the processes you use to do so aren’t very mature.
Suddenly data management doesn’t seem so abstract, and isn’t accomplished by doing things you can’t possibly do in your world. Instead, it is finite and the processes involved can be improved incrementally according to your priorities.
Process is the Key to Data Management Maturity
Perhaps ironically, data management maturity is a process thing. The CMMI sees an organization as a business process consisting of business processes, each consisting of other processes, etc, in the manner of a Russian nesting doll.
Data management maturity depends on the level to which all processes related to data management are performed, defined, measured, and optimized.
Warehouses Don’t Deliver Data Management Improvements
Almost every attempt I’ve seen to improve data management maturity has been associated with a data warehousing effort. For most organizations, the warehouse didn’t end up improving data management maturity. That doesn’t mean the projects failed. Those projects started out seeking to both improve reporting and straighten out data management, but longer than expected timelines and strained budgets stripped the plans down to their core reporting goals.
Furthermore, the scope of the warehouse project rarely included improving data-related processes at its data sources. Reporting from the warehouse may have shown consistent customer data across the organization, but different source systems may have different addresses for the same customer, with the expected service consequences. In effect, warehouse development papered over data quality problems by cleansing incoming data without substantially improving core data management.
Does the DMM Model Deliver Business Value?
We in the data community believe intuitively that good data practices result in more efficient, more effective, and more secure operations, and that managing by well-generated metrics enables better decision making than instinctive or poorly sourced analyses.
Early returns are promising: the process-based CMMI framework has proven effective in improving application project performance, and large organizations like the Federal Reserve, Microsoft, Ally Bank, and others are stepping up to recount their experiences with the DMM model.
As we continue to seek “top level management commitment” for data management improvements we’ll look for case studies showing quantified returns on investment from improvements in business processes and risk reductions. I think we’ll find that the returns from improved data management processes are there to be harvested.
Other Data Management Frameworks:
Data Management Body of Knowledge (DMBOK)
- “A standard industry view of data management functions, terminology and best practices, without detailing specific methods and techniques.” https://www.dama.org/content/body-knowledge
Method for an Integrated Knowledge Environment (MIKE 2.0) Information Maturity Model (IMM)
- “An approach for improving how information is managed across the enterprise” “that provides a common business strategy, technology architecture and delivery approach across information management projects.” http://mike2.openmethodology.org/wiki/What_is_MIKE2.0
–Data Management Capability Assessment Model (DCAM)
- “Represents the intersection of data management best practice and the reality of financial services operations.” http://www.edmcouncil.org/dcam