Some of you may be aware that my ‘day’ job involves benchmarking organizations against various process and business improvement models (see: PEP) . If you are reading this note on ManyRoads, you most certainly are aware that I have a passion for genealogy (as my daughter might say: “No Duh.”).
What I am curious about is… do you feel, like I do, that the genealogy industry (e.g., Ancestry.com, familysearch.org, etc.) would benefit from having more reliable, accurate, and predictable data management practices? If this thought piques your interest, please read on.
Data Management Maturity Model
The Carnegie Mellon Software Engineering Institute (SEI) and the Enterprise Data Management Council (EDM Council) have formed a strategic partnership to create a new Data Management Maturity (DMM) Model for the information technology and financial industries. The new model will define the components of data management at the specific business-process level so that organizations can assess themselves against documented best practices and upgrade their management of essential data resources.
The overall goal of this new collaboration is to help the information technology and financial industries become more proficient in their management of critical data and to provide a consistent and comparable benchmark for regulatory authorities in their efforts to control operational risk. The DMM will be constructed based on the foundational process areas found in Capability Maturity Model Integration (CMMI) developed by SEI and funded by the Department of Defense and in the CERT Resiliency Management Model (RMM) developed in collaboration with the Financial Services Technology Consortium. It will result in a framework and accompanying assessment methodology for evaluating the efficiency of data management practices, measuring the maturity of operational integration, and establishing standard best practices that can be adopted by information organizations worldwide.
The goal of managing data as a corporate asset where precision, consistency, comparability and standardized meaning are assured is just now emerging as a business priority among organizations. And while the objectives of data management are conceptually understood, the practice of data management is difficult to implement because of the difficulties in unraveling and reconnecting systems, processes and operational environments that are required to gain control over data as it proliferates throughout large organizations.
At the core of the challenge is the lack of practical expertise and fact that there is no proven operational route map to guide organizations in their goal of enterprise-wide data management. The Data Management Maturity Model objective has been crafted in conjunction with information technology and financial industries practitioners to help respond to the twin forces of need and complexity that characterize the data management challenge. The objective of the DMM is to help organizations turn the ‘art and practice’ into the ‘science and discipline’ of data management.
Any thoughts, comments, questions you might have are most welcome. As this effort gets underway, what would you like to see brought to bear in this realm? If you have ideas, concerns, or would like to participate in any way, please either leave a comment here or contact me directly from our Contact page.
In the end, we will all benefit from improving the reliability of our information sources, databases, and systems.