This blog post is Human-Centered Content: Written by humans for humans.

Now that we’ve helped define and contextualize data governance in our previous blog post, let’s go over some steps we can take to ensure success. This time, instead of Maslow’s Hierarchy of Needs, we’re going to build from the bottom up as if we’re starting a brand-new company. 

This should help guide your path, no matter where you are in the process. If you are, in fact, starting a brand-new company, congratulations! We’ve got the whole roadmap here for you. If you’re already further along on your company’s data journey, you can double check the basics and make sure your fundamentals are taken care of before continuing down the list. 

Let’s get started! 

Define Goals 

If you don’t know what your end goal is, you’re going to find yourself wandering aimlessly, wasting time and just hoping that what you’re doing has a positive effect on your company’s trajectory. Our biggest recommendations on setting good goals start with collaboration. 

First, you should meet with key stakeholders and enable multi-domain collaboration and buy-in. Charting a course toward great data governance isn’t a one-person job — it’s a group project. It also helps to think of this as an opportunity to gather requirements and ensure data governance policies meet any broader organizational goals. Putting these projects into a global context and ensuring you’re in line with your company’s standards will help alleviate any potential headaches down the road. 

Finally, the goals you come up with should be outcome-based and SMART — Specific, Measurable, Achievable, Relevant and Time-Bound. If the SMART method is new to you, not to worry – there are endless guides, articles and videos out there to help define it in detail. 

Define Roles 

After you’ve mapped out your goals, it’s time to break down the “who’s doing what” of your gameplan. Defining data governance roles clarifies responsibilities and ensures accountability for the duration of your project. 

As a note: This does not, and in many cases should not, need to land entirely on one person or department. It should encourage multi-practice collaboration. Delegate as needed to minimize burnout. 

Here are some data governance roles we typically see and their duties:

• Governance Committee: Creates and maintains the data governance framework. Ensures policies are properly implemented across their domain, resolves conflicts on access to data, and regularly reviews performance of the data governance program. 
• Data Owner: Responsible for decisions around their data domain. Works with the governance committee to define policies. Works with data stewards to ensure proper use of data and policies for company goals. 
• Data Stewards: Responsible for the day-to-day data management. Ensures domain data is accurate and consistent, maintains data dictionary/definitions, and educates data consumers on proper usage. 

Define Policies 

Nailing down policies and procedures relating to data objects and assets is the next step on the ladder and, when done judiciously, helps enable your data governance practitioners to more effectively do their newly assigned roles. These policies should be well-documented and reduce confusion, rather than create it.  

Some example policies include: 

• Data validation policies on ingestion for required fields. 
• Data cleansing policies to remove duplicates and fix data types. 
• Data quality policies to ensure % data completeness. 
• Data privacy policies to mask PII fields in the data set and provide training on these. 
• Data security policies for monitoring access. 

Conduct periodic compliance audits to both make sure that the members of your team understand the policies you’ve outlined and ensure that the policies you have in place continue to make sense. What worked at the beginning may not work as your organization continues to expand, and adaptability is key. 

Select Tools 

With your goals outlined, roles assigned and policies defined, it’s time to review what tools make the most sense for your company’s needs. The tools you choose need to handle various aspects of your specific data governance ecosystem, and both building tools in-house or buying third-party tools are viable. Sometimes, specific tools are not always needed, and you can get by with the out-of-the-box features of established tools. 

In parallel with selecting the tools, a concerted effort should be made to foster a culture of good data governance. If you simply purchase the tools but don’t have a culture that enables potential users to use them, then these tools might be a waste of precious funds that could go to more worthwhile endeavors. 

Track Metrics 

If a tree falls in the woods and no one hears it, did it make a sound? Likewise, if you implement good data governance practices and have no way to track your progress, how can you know how effective they are? 

What this step looks like is going to differ greatly based on what your end goals are. The measures and metrics you lay out should be used to track the success of the data governance framework and make adjustments as needed. 

Be sure to specifically include the goals you set at the beginning of the process as key metrics to measure against so you’re always on track. 

Some examples of metrics to track would be percentage of unmasked PII fields, frequency of ad hoc access permissions and data pipeline downtime. 

Iterate Process 

Once you start to see success, it’s time to keep that success going. Ongoing iterations and improvements on the processes help keep your data compliant as your data governance requirements and data environment evolve. 

Don’t neglect your interpersonal processes as well. Maintain governance collaboration between cross-functional teams as laid out in the “Define Roles” section. As data governance practitioners or responsibilities change, ensure everyone is brought up to speed on their roles and duties to maintain good data governance practices. 

Example iteration tasks are: 

• Regular governance committee meetings to maintain framework. 
• Feedback loop from stakeholders, analysts, etc. 
• Roadmapping and data governance self-scoring. 
• Continued training and awareness sessions. 

Wrapping Up 

These six areas are key pieces of the framework for good data governance. In an upcoming blog, we’ll also lay out some of the challenges that you might face while building the data governance stack and culture you need to accomplish your goals.