Why Interest In "Data Governance" Is Increasing
The above display of relative search frequencies shows that interest in "data governance" has almost doubled in the past three years. My own anecdotal experience with various media and my own research bears this out.
Data governance, of course, touches on a large number of related topics, tools, and techniques, including but not limited to the following:
- Big data
- Business intelligence
- Data alignment
- Data architecture
- Data dictionaries
- Data governance council
- Data lakes
- Data modeling
- Data provenance & data lineage
- Data quality
- Data science
- Data stewardship
- Data strategy
- Data transformation
- Database management
- Master data management
- Metadata management
- Natural language processing
- Performance measurement
- Semantic consistency
Data Program Management
One of my special interests is Data Program Management. As described in An Introduction to Data Program Management (DPM), DPM has three related components:
- Data Strategy addresses how data programs are planned and executed.
- Data Architecture addresses how the organization’s current and future data and metadata are defined and organized.
- Data Governance addresses how data, metadata, and their associated processes are managed.
Why Interest Is Increasing
Interest in data governance is increasing for several reasons:
- Advances in technology. The tools and techniques for managing and making sense of increasing volumes and types of data (e.g., "big data") are improving and becoming more widely accessible.
- Increasing recognition of data as a strategic asset. This is certainly no surprise for those of us in the IT and data management business but an increasing number of people outside of IT appear to be waking up to this as well.
- Recognition that tools aren't enough. Along with an increasing awareness of what might emerge from better analysis and use of data is an awareness that management needs to make sure that the new tools and techniques are being put to good use and not just "added on" to an increasingly complex stack of local and cloud based technology assets.
Data Governance Challenges
I also sense an awareness on the part of advanced industry thinkers that managing data as a resource may require different approaches to managing how organizations operate. A variety of challenges will need to be addressed including:
- Data & process siloes. Different groups and systems inside and outside the organization need to exchange data and some may use language, terminology, and rules in very different ways, even when referring to the same thing.
- Changing language, laws, & rules. Language and requirements change and systems need to change to accommodate these changes.
- Fraud & gaming the system. "Bad guys" quickly learn how to take advantage of inconsistencies in how data, language, and rules are employed especially in systems that rely on manual data entry or coding processes.
- Unstructured data. Extracting meaningful and consistent meaning from large amounts of unstructured data may require expertise in new or unfamiliar areas such as natural language processing.
- "Firehose" effect. Adding large volumes of transactional data may require the learning of new tools and techniques.
For example, the "siloization" of systems and the data they produce and consume in many organizations can place technical, cost, and -- worst of all -- political barriers in the way of managing data comprehensively and strategically. My colleagues and I are therefor developing ways to collaboratively accelerate how such barriers can be overcome without imposing the imposition of expensive or complex systems or processes that conflict with existing corporate cultures.
Copyright (c) 2017 by Dennis D. McDonald, Ph.D., Alexandria, Virginia USA, email firstname.lastname@example.org, web www.ddmcd.com, phone 703-402-7382.