Data Governance: Why You Need a Business-Led Use Case Approach

Is Data Governance considered a cost rather than an asset?
Do you feel data governance is an afterthought, a compliance burden that stifles innovation?
Are you struggling to convince leadership of the ROI of data governance initiatives?
Do you find yourself constantly battling departmental resistance when it comes to data access and controls?
If you answered “Yes”, then you are not alone.

The Issue

Traditional data governance focuses on centralized policies and controls. Unfortunately, it’s also slow, bureaucratic, and difficult to demonstrate business value through this approach. More than that, business leaders want immediate results – not to invest two or more years building out a data governance foundation with unclear ROI.

The Solution

A business-led use case approach cuts through the noise to focus data efforts on solving the most important problems and delivers immediate value.

The Approach

Let us take a deeper look at each step:

Step 1: Identify Business Needs

What are the top business questions you want to answer? Make sure the business questions have measurable outcomes. One way is to look through your department objectives. For example, it could be something like “We want to increase customer deposits by 15%.”

Step 2: Identify Use Cases

What use cases can help achieve this business goal? Make sure the use cases focus on solving the end user problem.  For example, “Streamline the onboarding process, so customers can stand up a deposit account in 50% less time than what it takes today.” Remember, a business question can have multiple use cases associated with it. If your organization uses agile methodology, I strongly recommend framing the use cases as user stories.

Step 3: Determine Data Asset(s) Needed

Now that you have defined the business questions to solve and associated use cases, what data assets (think, flat datasets) would you need to solve this? For increasing deposits, it could be something like:

• We need a holistic view of our customers and their product penetration.
• We need to better understand customer segments, preferably at a customer and household level.
• We need demographics data, financial history, and account activity.

This is a crucial step, as you want to be exhaustive, but realistic. Most companies falter at this step by trying to include all datasets they think may be of use. Think of this like project prioritization – if you can only have five data assets, what would they be?

Step 4: Develop Governance Capabilities

This is where you are putting on your experience mindset. What should be true, so your data consumers are satisfied with the dataset? Think of questions like:

• Can the dataset be trusted?
• Can it be easily discovered?
• Can my data consumers easily understand the data elements and its lineage?
• Does this dataset include any sensitive attributes?
• Should this dataset be restricted to specific consumers?

By asking these questions, you can determine the set of policies and procedures you would need to build and deploy/enhance. In the example above, you only need to build a data quality, metadata, and lineage, and data access policy and procedure. This is not to say that other governance capabilities like retention and lifecycle management are not important, but they can be added later.

Benefits

Make your Data more nimble

Instead of building walls around your data, use a business-led approach to weave data governance principles into the fabric of your data lifecycle. Foster a culture where data responsibility and awareness are second nature. Data quality checks are seamlessly integrated into the data workflows, data classification is embedded in applications, and data lineage is automatically tracked.

Shift from reactive data management to a proactive data lifecycle that unlocks the full potential of your data as a strategic asset.