In today’s world, ‘data governance’ is inherent to every growing organization due to the copious amounts of data that gets collected and leveraged for all crucial decisions. But what is data governance exactly?  

It is essentially a set of policies, procedures, and standards that ensure an organization’s effective and efficient use of data. It encompasses the entire data lifecycle, from its acquisition and storage to processing, analysis, and dissemination. 

Some of the primary steps in implementing guidelines to ensure that the data within an organization is managed in a consistent and controlled manner are: 

• Evaluate the current state 
• Defining the scope 
• Developing a data governance framework 
• Creating policies and procedures 
• Establishing data quality standards 
• Providing training and support 
• Monitoring and measuring data
• Continuous improvement

Easy breezy, right? Beers are on me for anyone who has been successful at following every step of this process with zero resistance and grief.  

Jokes aside, “Governance” is the toughest part of data management. In a medium to large-scale organization, it takes substantial time, the right tools, budgets, resources, and change management to achieve success.  

Governance works differently in different organizations. It starts with a visionary leader who understands the value of adhering to data-governing principles and enforces them top-down in the organization. Generally, convincing leaders of the overarching policies required to maintain data quality is an uphill battle as sometimes it can be seen as overhead. Every group/division within an organization is driven to meet its own metrics/KPIs and this can seem like additional work and overhead.  

Often organizations take the start-up approach of speed to market, quick changes to strategy, quick trials, and a try and trash approach which involves getting things done faster by unconventional methodologies. However, speedy success with rising numbers results in bandage solutions, and data quality is the last thing on their mind. Policies and standards are considered as an impediment that slow success. Tackling data quality and maintaining standards are the first to be deprioritized. Securing buy-ins from top-level leaders is therefore of the utmost importance. 

So how do we make data governance work? My recommendation is to use the ‘CLEAN, SHOW, ENFORCE’ (CSE) approach.  

Clean: Identify the most critical and impactful business process, and clean the data related to it 

Show: Present the quantifiable impact of this clean-up to the leaders and stakeholders to gain their buy-in 

Enforce: Steadily move the quality control process upstream to the business process itself so that it becomes self-service. Once the business owners own the vetting of data quality at the source, the governance team can monitor, measure, and enforce the standards needed. 

But, what’s the starting point? Well, to begin with, hire the right team. Start small. The team should be comprised of a knowledgeable data leader who is a great storyteller, a person to manage projects, create policies and define standards, and a technical person to analyze data and execute.  Once the team is in place, get ready to do some dirty work and follow the CSE approach mentioned above. You are the ‘Pooper Scoopers’. That’s right. With the analyst and engineering skills in the team, analyze the data and start cleaning it (or a subset of the data). You can call yourself data cleaners or Mr. Clean or any name that works well for you. I prefer the term ‘Pooper Scooper’. At this point, you are the ones cleaning up others’ messes. 

With the gained leadership support and people on board, enforce quality controls in the business process. Build tools and support methodology to assist the business process team in implementing the quality controls. Create monitoring tools and generate metrics constantly to provide feedback and guidance to the teams to continue to adhere to the policies. This is when you will have a proper governance model in place. Any new business process would follow the standards and policies defined by the team.

Some overlap is to be expected during the transition to a self-serve model where you will continue to have the cleaning team for some of the processes even after implementing governance. The transition duration may differ based on the organization’s size, culture, and geo-location. The bottom line is that people prefer doers to preachers. Be a doer and gain trust before sending out a list of guidelines, standards, and policies to be followed. 

With our extensive experience in data governance, Fresh Gravity has helped clients of different sizes and at multiple stages in various industries. We can evaluate your organizational style and do everything from offer advise about to fully build your data governance framework. We can be your pooper scooper team analyzing the data, determining what needs to be cleaned, and performing the cleaning. We can build monitoring and metrics solutions to keep you on tap on the quality of the data.  

If you want to learn more about our offerings, talk about governance, or say hi, please write to me at babu.raj@freshgravity.com or leave a comment with your contact information for a follow-up.