How Much Digital Transformation Does Better Data Program Governance Require?

September 19, 2016 Dennis D. McDonald

By Dennis D. McDonald

I'm researching how mature organizations plan and implement improved data access and analytics.

Initially I focused on the special challenges involved in "big data project management" which I abbreviated as BDPM. That focus evolved into “data program governance" which I wrote about here: Building a Realistic and Effective Data Program Governance Strategy.

I'm finding that "big data" as a concept may not be relevant yet for many organizations, given the realities of how many organizations currently manage their data resources. This is especially true with large or mature organizations where multiple systems and multiple data and metadata stores have to be maintained to support ongoing business processes.

Simply managing how systems interact is challenge enough for many organizations. Going beyond that to develop and implement a more strategic and comprehensive view of how data and metadata are managed requires careful planning and leadership. It also requires a management structure that supports both current operations as well as a future vision requiring innovation and digital transformation.

Just because a move to “big data” can be difficult doesn't mean that significant improvements in how data are currently managed and used are beyond reach in the short term. Examples of practical actions include:

Improved management of data and metadata standards
Operation and maintenance of "open data" portals
Making operational data and research results accessible and shareable
Actively recruiting and training staff on data analysis and data science techniques
Shifting data intensive operations to the cloud
Requiring and sharing data management plans as a condition of project funding
Supporting the needs of both "data sophisticates" and "data newbies."
Incorporating data and metadata management into PMO operations.

One preliminary conclusion I'm reaching is that efficient and effective data management practices need to be "baked into" ongoing programs gradually rather than all at once. Important planning questions are:

What are you trying to accomplish with better data and analytics?
What services do you need to implement over time so that data become well managed and consistently useful?
How much will improved data governance measures cost?
How do you measure the benefits associated with improved data governance?
Can we deliver useful data services while still planning?
How do you objectively answer these questions?

Those last 6 questions involve more than technology and can have significant implications for how the organization is managed. Perhaps a 7th question should be: What competitive risks will the organization face if it does not evolve to a more mature approach to data program governance?