Updated September 2, 2020
There’s a lot of talk these days about “right-sizing” regulations, particularly for smaller financial institutions. The question of how to reduce regulatory burdens without increasing risk is certainly a pressing one. But what about right-sizing your data governance program? Can you implement a lean data governance program in such a complex regulatory environment? Is it possible to reduce the complexity of your governance program without incurring undue risk?
Of course it is! If done properly, starting small with a data governance program accelerates an organization’s path to Data Intelligence.
What is the role of data governance?
Data governance helps your organization achieve Data Intelligence. It helps you make sense of your data so you can use it as an asset. Data governance is essentially about establishing practices and processes to help you understand data and standardize the answers to some important questions:
- What data do we have?
- Where did this data come from?
- How is this data used?
- Who is responsible for this data?
- Can we trust this data?
These questions are very high level, and, often, they are very loaded questions for certain people in your team. Answering these questions for your organization’s data environment can feel overwhelming, so start small.
Why start small?
Data governance requires participation across the enterprise—and to be successful it also needs commitment from your executives. Starting small allows you to demonstrate that data governance is not simply a better way to meet stringent regulations (though that’s reason enough to implement a program) but also a way to achieve high level organizational objectives fast. Starting small allows you to build a repeatable process, train staff incrementally, and, ultimately, create a “right-sized” data governance program with a better chance of success.
How do you set up a data governance program?
When I say start small, I mean really small. For example, choose a single yet critical report—the one your institution requests over and over, or the one that has grown exponentially over the past five years as new questions keep getting asked. Take this report and follow these six steps to start a data governance program that will allow you to scale systematically and swiftly:
1. Identify roles and responsibilities
Determine who touches this report and why. Who creates it? Who approves it? Who uses it? What do those people use it for and what makes it relevant to them? Who provides the data? Who owns those systems? Who owns the processes?
Answering those questions provides you with the broad strokes of a data governance operating model—a framework to help the producers and consumers of those reports collaborate more easily and securely.
2. Define your data domains
Identify the different data elements that your report uses, as well as the data types and data values associated with those elements. Assign domain owners to begin establishing a stewardship hierarchy. By establishing data domains, you will identify additional stakeholders who should be included in your operating model. When it comes to something like customer data, for example, you want to be sure that everyone who uses it has a seat at the governance table.
3. Establish data workflows
Think of this as a data supply chain. Now that you have a good understanding of the data informing the report, begin to prioritize that data. What data is important to this report? Where does that data come from? How does it end up in this report?
4. Establish data controls
This is where you get to the core of true data governance—establishing appropriate controls and processes to optimize your data’s quality and integrity.
Define key controls, metrics, and data thresholds. Develop report processes around what data is used and how it is ingested. Establish a feedback mechanism to identify, prioritize and resolve data related issues.
When you reach this step, you’ll see how critical roles and responsibilities are. Think about how data for operational reports typically has a lower threshold than data for regulatory reports. Having the right experts in the room who can define appropriate thresholds allows you to avoid a “one-size-fits-all” approach that often dooms governance programs. Not all data is created the same way and goes through the same processes, so make sure you have the right people involved when documenting and implementing the appropriate controls.
5. Identify authoritative data sources
Now that you’ve established the purpose of the report and prioritized its key data elements and controls, you can more easily determine which data sources should be the authoritative sources for that report going forward. Assess these sources against the controls you’ve established and create a roadmap for promoting adoption of these data sources enterprise-wide.
6. Establish policies and standards
Yes, you’ve been working on policies and standards since Step 1. But now that you’ve proven the value of data governance to your supporting stakeholders, it’s time to roll out those policies and standards more widely. The governance structure you prototyped around your first report will serve as the framework. Remember to clearly communicate roles and responsibilities and always align policies with your institution’s broader data management strategies.
In order to achieve Data Intelligence, organizations must adopt a data governance program. They must start small and build on the momentum and success of the foundational program to achieve their data governance goals. Data governance leaders must train the organization to foster collaboration and establish a successful data governance program. Once you follow the six steps listed above, your organization is in a good position to expand and grow the program to increase adoption and amplify the impact of a data governance program on making data-driven and innovative business decisions.