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Avoid Data Governance Failure

Follow the example of someone who's launched a governance program to ensure your data governance success.

• By Jill Dyché
• January 29, 2018

I've been writing and talking about data governance for more than a decade. TDWI first published my "Ten Mistakes to Avoid When Launching a Data Governance Program" booklet, coauthored with Kimberly Nevala, in 2008. By then we'd been consulting with companies worldwide on how to get governance right. We figured we had it down. Most of the time, we did. When data governance worked, companies were poised for more strategic efforts -- digital transformation, anyone? -- comfortable with their data policy-making prowess.

What we couldn't account for (no one really can) was organizational dynamics. To borrow a phrase from Tolstoy, each unhappy corporate culture is unhappy in its own way. The corollary, of course, is that happy corporate cultures have some commonalities that you can learn from. Rather than feature another beleaguered data governance manager wondering why no one adheres to policy, I thought I'd feature a success story.

Here's a note I got from Bradley, a former client. Bradley, an industrial engineer by training, got data governance off the ground at his employer. Now it's simply part of how things get done. Here he lists three reasons why.

Dear Jill:

I stumbled upon the blog post in which you told a story about a manufacturer whose culture was impeding progress. I thought this would be a good time to check in. Since we saw each other, I've realized that none of our strategic initiatives (including revamping our supply chain and evolving toward the "factory of the future") can be accomplished without data governance.

I'm happy that we started small. We designated data categories and policies. No one was more surprised than I when other departments began adopting our governance model. That evolved into de facto enterprise data governance. It's an acknowledged success and gets regular accolades inside and outside the company.

You warned us about the missteps, and we still have our share of politics, but as I see it, we did three things right:

1. It was hard for some people to accept, but we finally agreed there would never be a single version of the truth. This was a huge shift because we'd invested millions in our enterprise data warehouse. However, it freed us to be honest about the need to manage the data generated by a few dozen data sources and allowed us to standardize some processes around them.

2: We agreed that if we were going to commit to data governance as part of our team responsibilities, we needed to up our analytics game. There's no sense putting the structures and skills in place to do the same old thing. Data mining and artificial intelligence are both core to our company's future, and they both get more realistic as our data gets better.

3: We are improving our measurement capability. Like most manufacturers, we have a legacy of Six Sigma and zero defects. We've discovered that these practices apply to data, too. We're extending some of our measurement processes to embrace better data quality, higher productivity, faster time-to-decision, and higher-impact decision making. We've already seen great results and they improve iteratively.

I wanted you to know that by working with our management and with each other, our departments have a new belief that data will help us improve our operations. Even better, it will allow us to effectively compete.

-- Bradley, Michigan

I love how Bradley starts out by acknowledging inherent corporate obstacles. Many people allow the realities of entrenched cultures, job titles, and ownership skirmishes to win out, effectively sabotaging data governance before it even begins.

Rogue data will always exist, whether it's in a system no one uses or a server under someone's desk. The extent to which data needs to be shared across business processes and functions is the extent to which it needs to be governed. It's a cliché, but the low-hanging fruit mantra applies: start with data that's used cross-functionally.

Mostly I like how Bradley treats data governance as a means to an end. Positioning data governance as a strategic imperative is foolproof for any company intent on becoming more customer-focused or competitive. Executives, many of whom actively use the term data-driven in their leadership and town hall meetings, are more attentive when data governance enables strategy, less so when the "raw materials" discussion marginalizes data as a technology problem.

As a consultant, I would often urge my clients to articulate guiding principles for their data. This is much harder than it first appears. In this context, Bradley and his team have done the hard part. Have you?