Cowboy Analytics and Other Reasons to Grow Data Governance

Loraine Lawson

In 2012, companies began in earnest to address data governance as a business issue. At the same time, several trends from the past year created new conditions for how organizations use data and what they expect from it.

The result? In 2013, data governance needs to evolve in ways that address those trends, according to John O’Brien, principal and CEO of Radiant Advisers. In a TDWI article, “Impact of 2013 Data Strategies for Data Governance,” he suggests specific issues to cover under data governance. Here’s a summary:

2012 Trend: Adopting big data.

Problem: With budgets still tight, organizations will want to ensure their investments in data platforms don’t get bogged down with large datasets and new data types.


2013 Governance Evolution: Extend data governance to include the performance of analytic technologies. This means tackling questions about retention, archival and deletion, as well as encryption and risk.

Payoff: Improved system performance, but also reducing or at least not growing infrastructure costs, according to an Informatica white paper on Holistic Governance. It will also create a better ROI for data projects overall and reduce storage costs in development and test environments.

2012 Trend: Opening up data to more people to support business analytics. “This sparked a slew of commentary recounting the history of business intelligence and data governance as being concerned with controlling and managing data consistency, semantics, and usage,” writes O’Brien. “Some claimed that data governance was the enemy of business analytics, which seemed like quite the stretch, though it wasn't entirely incorrect.”

Problem: Shadow IT projects as a standard practice; data silos.

2013 Governance Evolution: This evolution actually started in 2012, with the focus of data governance shifting from access prevention to “ensuring proper access by individuals.” That needs to continue, along with more education about data types, roles and responsibilities. You may also want to explore new enterprise analytics and information discovery development methodology, which incorporates governance but still allows for “the open agile and iterative nature of analytics,” suggests O’Brien.

Payoff: Informatica points out that governance fills the same roles for data as legal and HR fill for people. This serves that legal function of bringing shadow data into the sunlight so you can integrate and govern that data. This will ensure you’re following compliance and sound data management practices.

2012 Trend: New data strategies, incorporating analytic projects and BI projects, using advanced, statistical algorithms or models.

Problem: Cowboy Analytics: The models are useful and hard-working, but roaming free in the enterprise landscape, without regard to boundaries or rules.

“Over time, an analytic model can ‘weaken’ its prediction if the variables used lose their associations or if the model simply no longer represents the changing business environment accurately,” writes O’Brien. Also, these models may be applied to operational systems and business decisions, he writes, but are not managed alongside business rules and definitions of other enterprise data.

2013 Governance Evolution: Taming the cowboy business analytics by adding a governance framework that can help you evolve with ongoing needs.

“… ‘with great power comes great responsibility’ and without governance programs extending to address business analytics, additional risks may be at hand,” warns O’Brien. “Once again, defining procedures for development, verification, and ongoing management of analytic models will be necessary.” It’s also a good idea to set up an analytics center of excellence as a complement to governance.

Payoff: Avoiding major disaster by keeping the business analytics and BI models refreshed and reliable.

The Big Evolution: All of these issues point to one evolutionary leap that data governance needs to make: Incorporate change management. Informatica considers it a key component of any data governance framework, and a major reason to use a framework rather than strike out on your own.

I can see their point. After all, the way we use data keeps changing, growing and adapting. The only way to stop playing catch-up is to view governance as an ongoing, evolving process.



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