Is Your Data Governance Program Succeeding or Struggling?
How are people in your organization talking about your data? Do any of the following statements sound familiar?
“Is the data I need collected somewhere? Where?”
“How do I get the data?”
“How do I know I can trust this data?”
“I don’t know what our standards are.”
“Who is validating this data is accurate?”
“I don’t know what data is required to be entered and when.”
“I have to manage by gut feel at times because I don’t have the data.”
“Our data sucks.”
Whether your organization has been “working” on data governance initiatives for some time, or you are starting a data governance program, chances are you’ve heard statements similar to what is above.
We recently worked with a leading financial services client to modernize their data platform to deliver timely and trusted analytics. They sought a robust platform for their use cases while also being scalable and responsive to the speed of their business. The data platform in any organization will only deliver the data as managed by the company. Therefore, critical to this project was helping the team improve their data quality through an effective data governance program. We heard statements like those mentioned above and many variations on those themes throughout this project.
This company was like many others and lacked a data governance focus for far too long. Core business systems had bloated with excessive and duplicative fields, which confused people entering data. There was a lack of clearly defined data-focused roles in the organization. There were no clearly understood data decision-making processes. There was a lack of controls or monitoring of data. There was silo-ed decision-making regarding data occurring. This resulted in inconsistent, poor quality data, and overall lack of trust in data. Executives consistently had to look at what information they could get and rely on “gut feel” for the rest.
We work with organizations to solve some of the most challenging data and analytics challenges. Without a doubt, one of the toughest challenges for many organizations is effectively executing data governance. Like many others, we worked with this organization to define appropriate roles, both new and aligning existing positions, to build a data-focused team. We developed a right-fit, early-phase data governance framework and helped them define initial data scope tethered to crucial business value. We helped them define data quality metrics and implement a data quality program to monitor, measure, and fix critical data issues.
There are multiple functions involving data that we help organizations improve and leverage to drive business value:
- Enterprise data strategies
- Modern data architectures
- Data analytics and business intelligence
- Data engineering and pipelines
- Data consolidation – data warehouses and lakes
- Advanced analytics and machine learning (ML)
- Artificial intelligence (AI)
- Data monetization
In addition to those data functions above, we view data governance as critical to the success of your data strategy. Your organization aims to have secure, consistent, accurate, and timely information to mitigate risk and drive value through data. How do you accomplish this? A modern architecture and data platform will undoubtedly provide the agility and scalability required; however, people and processes will also play a large part.
1. Right-Fit Model and Roles
Common Pitfall
There is a overload of data governance information that can be referenced when building your program. Google the term “data governance,” and you are sure to be overwhelmed by the number of results. There is no one-size-fits-all model, framework, or org chart. Ciphering through all the “how-to” information available can be challenging to determine what will work for your organization. We have found that organizations experience difficulty defining and adopting the “right-fit, right-now” data governance operating model that involves transparent processes and enforceable policies. Many companies try to implement overly complex governance operating models that require a higher level of data governance maturity than the organization is currently capable of.
Recommended Approach
We have found it effective to ensure top-level executive sponsorship of data strategies exist, and from there, each organization must determine or find the appropriate leader or leaders to drive the program and influence change. From there, define your decision-making model to drive data policies, standards, and expectations. These are key to driving accountability in improving data quality and trust. To succeed in today’s highly data-centric business environment, organizations must establish the people roles with clear responsibilities for data activities. Then the right policies and processes are required to align people in positions, set expectations, enforce data standards, and understand how the organization makes decisions about data. With this framework in place, an organization can inhibit bad actors from compromising enterprise data. Which in turn enables businesses to scale and grow even faster. This also sets the conditions for organizations to achieve a paradigm where they can offer data as a service.
2. Value-driven data governance scope
Common Pitfall
We often see organizations struggle with data governance initiatives because they do not clearly define the scope linked to critical business outcomes. Trying to do too much at once for the team’s size or the resources they can deploy to the program often leads to stalling or stumbling in delivering outcomes. This can lead to people being unclear on what the priorities are when there are so many data fires to put out.
Recommended Approach
There are many data “opportunities in most organizations,” ensuring you focus on the most important ones driving your current business priorities. Spend the time with business leaders to link the data governance capabilities you are deploying, such as a dictionary, glossary, data quality monitoring, to high-value business outcomes. It’s good practice to revisit the governance program scope and priorities at the tempo by which the business requires it, but be judicious that it’s not so frequently that the team can’t see any wins through to the finish.
3. Measure – Monitor – Progress over Perfection – Get something done
Common Pitfall
Measuring progress towards defined goals is an inherent function in businesses today. Data governance is becoming as critical a business function as finance or compliance. Failing to define data quality metrics or quantifiable methods to articulate the improvements or value of the data governance programs is a gap we see frequently.
Recommended Approach
As the case was with our financial services client, we helped them define a set of measurable data quality KPIs. We leveraged their existing reporting tools to develop data quality reports to monitor data health and aid in triggering remediation efforts. Monitoring data quality is just one method of data governance. Still, it can immediately aid in establishing trust with data consumers because they can view a data quality report that tells them their data is good. They also know the monitored rules and the people to engage in addressing gaps when identified.
Ready to Take Control of Your Data?
Without operational data governance, achieving data as a Service (DaaS) is impossible no matter what technologies you have or the cloud platform you use. Organizations have to implement data governance practices into their technical solution to accomplish their goals.
It is critical to identify achievable goals and create a roadmap to reach them to succeed at data governance. Suppose you need the help of experts to assess your enterprise, architect a roadmap, or operationalize your governance. In that case, Caserta has the experience and expertise to meet your organization’s data goals.
