Your Data Governance Processes Shouldn’t Feel Like the DMV

Your Data Governance Processes Shouldn’t Feel Like the DMV

Data governance has developed a bad reputation as just another cumbersome part of risk and compliance. Many enterprises view it like a trip to the DMV — the dreaded Department of Motor Vehicles — which is notorious for its old-school approach of making you wait an absurdly long time only to provide poor service and disappointment. It doesn’t have to be that way, but the government is often slow to modernize. 

Sound familiar? Despite substantial financial and resource investments, many enterprise data governance teams are running programs designed more than a decade ago by large financial institutions to manage risk and legal compliance. And, let’s be honest, how much does a tech company, a consumer goods manufacturer, or even a municipality have in common with investment banks or financial companies? 

The good news is, data governance doesn’t have to be intimidating. In fact, it can be downright liberating. I would argue that a new outlook on governance is essential for achieving any of your business initiatives this year. Forecasting revenue, stocking products, charting shipping routes, and delivering better customer service can all benefit from modern data governance practices that accelerate adoption of data and analytics infrastructure, tools, and data-driven processes. 

While the exact tactics for instituting effective data governance may vary for different organizations, all modern enterprises need to leave the DMV-like nature behind by prioritizing self-service, transparency, and collaboration. In order for data governance to advance business goals, CDOs must focus on agility, automation, and integration. Ultimately, data governance should be an enabler for your entire data and analytics process — freeing your team to focus on getting the work done versus how to do it in the first place.

Encourage participation

It’s not easy to create a data governance structure that people want to participate in. No one wants to be force-fed confusing rules and policies that slow them down. And you don’t want that either, because it ultimately costs the business. A simple process with clear rules creates the foundation for enthusiastic participation.

For example, Indeed is the top job site in the world, with more than 300 million unique monthly visitors. The company implemented a new data governance program and credits its success to working with, not against, its unique culture. Indeed found that, within the organization, many people were already doing data governance, including a group campaigning to get documentation of the most popular datasets in the company’s legacy catalog. Understanding what was working for them, and then elevating and empowering those individuals to be part of creating company-wide solutions, increased buy-in and the usability of the program.

Enterprises looking to transition to a more effective data governance model can learn from Indeed’s example by engaging with data users to see what governance mechanisms are working best for them. When people feel like the governance process is based on their ideas and feedback, they’re much more likely to participate in it. 

Identify opportunities for automation

You can overcome the perception of governance as red tape by automating as much as possible. But surface-level automation isn’t going to cut it. Automation with real impact must elevate enterprise knowledge that helps people make important decisions about risk, resilience, and revenue growth.

For example, imagine that a column of Personal Identifiable Information (PII) unexpectedly appears in a data source. Your data governance platform should be able to automatically identify the column and all of the downstream systems containing that table, apply the appropriate policies, and alert the right people, saving a potentially costly headache for your company. 

Or consider a situation that so many executives are facing right now. Your company is laying people off but isn’t necessarily cutting back on programs. Retaining years of institutional knowledge about data, dashboards, and projects is critical for business continuity. Beyond the obvious challenges, this presents major hurdles for the existing employees trying to pick up specific projects. They may be stuck without answers to even the most basic questions about the data’s source or prior use. One way to avoid this is to automate the transfer of data ownership to the right person, whether they are next in the chain of report or a peer with similar access rights and job function. That will ensure your data teams continue functioning at the right speed rather than waiting days or weeks for IT to reassign data ownership manually.

Governance everywhere

Now that you have buy-in and automation, you need to consider program sustainability. That involves ensuring your governance process is unobtrusive and meets users where they are. That might mean making sure the right metadata is presented directly in Tableau or, if you need to undertake a manual process, ensuring it's happening in places like Slack or Jira, where team members are already managing projects and workflows. 

It’s almost conventional wisdom that the democratization of data and compliance are opposing forces. But that doesn’t have to be the case. The days of data access being concentrated in the hands of a few IT people should be behind us. Knowing what data you have, understanding its levels of quality, sensitivity, and even popularity, and giving people the opportunity to view metadata, request access, or suggest changes to the steward, encourage participation within governance policy. 

Whether you’re focused on enabling data discovery, undergoing a digital transformation, implementing data mesh, or building out a semantic layer, governance is the foundation for success. But company-wide buy-in demands different thinking and processes. I believe 2023 can be the year we move past governance feeling like a trip to the DMV if CDOs are more strategic, revenue-focused, and deliver positive business impact.

About the Author

Jon Loyens is Co-Founder and Chief Product Officer at data.world, where he leads product, design, and product engineering for the modern data catalog company. He has more than 20 years of experience leading data-driven product management, expanding data programs, and creating data-driven cultures for enterprises. Before co-founding data.world, Loyens was VP of Engineering at HomeAway, a vacation rental marketplace, and Chief Technology Officer at Marketvine, a SaaS company focused on e-commerce merchandising.

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