Data Quality Reboot Series For Big Data: Part 4 Big Data Governance

There was lots of feedback on the last blog (“Risk Data, Risky Business?”) that clearly indicates the divide between definitions in trust and quality. It is a great jumping off point for the next hot topic, data governance for big data.

The comment I hear most from clients, particularly when discussing big data, is, “Data governance inhibits agility.” Why be hindered by committees and bureaucracy when you want freedom to experiment and discover?

Current thinking: Data governance is freedom from risk.The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial. Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data. Success has been achieved through legislative means by enacting policies and procedures that reduce risk to the business from bad data and bad data management project implementation. Data governance was meant to keep bad things from happening.

Today’s data governance approach is important and certainly has a place in the new world of big data. When data enters the inner sanctum of an organization, management needs to be rigorous.

Yet, the challenge is that legislative data governance by nature is focused on risk avoidance. Often this model is still IT led. This holds progress back as the business may be at the table, but it isn’t bought in. This is evidenced by committee and project management style data governance programs focused on ownership, scope, and timelines. All this management and process takes time and stifles experimentation and growth.

Reboot: Data governance is freedom to achieve. Big data has brought to the forefront the age-old conflict between the business and IT. The business needs speed and agility and wants to get the job done. It does not want to be hampered by process, development timelines, and certainly does not want to hear “no.”

Data governance needs to evolve to develop policies that are not just about what you can’t do, but what you can do.

If you really want your data governance program to mature and truly be business led, the greatest pivot will be for IT to give up control of the data and the facilitation of data governance. If the business is happy with 3.8 vs. 4.0, it is their prerogative trust estimation. If the business is confident in high variability of data introduced into analysis, this is a policy. Data governance is not to argue the point; it is to create a continuum of trust and enablement. Have the business take over and define the amount of governance and control it wants over its use. Have the business create a framework that aligns trust in data with use.

In the world of big data, the fact that most of it is out of your control means that data governance needs to span across more use cases than will happen within the walls of the organization. You need policies that guide toward opportunities as much as you need gates to avoid risk.

Comments

Closing the Feedback Loop

"If you really want your data governance program to mature and truly be business led, the greatest pivot will be for IT to give up control of the data and the facilitation of data governance... Data governance is not to argue the point; it is to create a continuum of trust and enablement." - Amen!

We need to move from a WRITE-CENTRIC universe where business entities and objects are rigidly defined in advance, which necessarily leads to drift from operational reality - to READ-CENTRIC business entities and objects, which allow definitions and capabilities that can be flexibly exploited to support adaptation and multiple perspective (aka - "The Real World").

Glad I stumbled on your post!

Best,
Dave

This is a great post and

This is a great post and elegantly stated. Big Data Governance is a topic that is gaining more traction and requires a paradigm shift in thinking.

Read-centric universe

Dave,
I like that take. Definitions can provide a perspective, but it is the context and relationships across data that get to flexibility.

Paradigm shift in thinking

David,

The shift will certainly be a journey. I'll be interested to watch how big data teams and traditional data teams align and integrate. I'm thinking this is going to be the first step when organizations want to operationalize big data insight. What do you see are hurdles or tipping points?