Data lives at the intersection of an organization’s people, processes and systems. To truly resolve a conflict or misalignment, it’s highly likely there will need to be changes to your processes and the systems you use.
Data governance teams and related groups, such as Information Architecture, are uniquely placed to work on data definition conflicts. Sometimes they’re not brought to the table because they’re not seen as a direct stakeholder. But there’s an opportunity for governance professionals to be neutral facilitators with their deep expertise in how the organization’s data flows and works.
I’ve seen governance organizations shy away from this work because they understand they don’t have the authority to make any one change. In reality, data governance practitioners have an opportunity to influence all of the organization’s players, including its senior leaders.
Here are four steps you can follow to effectively resolve data conflicts.
Bringing together stakeholders is the absolute foundation for your success. Even if you’ve already been working with some of them (for example, on a business glossary project), it’s important to look at all the stakeholders you need for your data conflict resolution initiative.
Work through these questions as you plan out the stakeholder meeting:
- Who are all of the producers and consumers of the data?
- How does data get created and brought into the organization?
- Who owns the business processes?
- Who are the primary consumers of the data, both internally and externally?
- What stakeholders need to be brought into the data conflict efforts because they create or use the data?
- What other stakeholders should be included? (Think broadly — for example, representatives from Information Security, Legal, Privacy, Compliance and Human Resources.)
- Who gets to make the final decision to implement changes around the information?
Step 1 takeaway: Cast the net wide to include people outside the key stakeholders. You need to communicate, at some level, with a broader group to truly resolve data definition conflicts.
Data definition conflicts often come to us like big balls of yarn — a tangled mess no one wants to unwind.
It’s essential to work together to break things apart, for example, in a whiteboard session or working line by line through glossary definitions.
Collectively, identify points of agreement, specific points of conflict and the cause of conflict — for example:
- Different contexts for similar data
- Overloaded data fields
- Name conflicts
Step 2 takeaway: Get agreement early on so you can move forward. And if you have to get very granular — “Can we agree at the top level that a customer is someone who gives us money?” — know that can be part of the process.
Next, you’ll start to formulate a plan of action for resolving data definition conflicts. Think about what the data should be to meet all of the various business needs — and what are the resolution options?
Then, identify the impact of each solution, determining how they will affect a business or technical process or require a system change.
Finally, identify the expected benefit of each solution. Think about what happens if the organization makes the changes. How will you you know things are working? What will success look like for you?
Step 3 takeaway: Get consensus from the cross-functional team on the impact and benefits of the plan.
Use your organization’s data governance framework for making decisions around data.
- Who in the organization is required to make these changes happen
- How you will track and measure the implementation and adoption of the solution
- How this is a great opportunity for data governance practitioners to demonstrate their value
Step 4 takeaway: You’re facilitating a cross-functional group of people to solve a problem for the organization and working to optimize the benefits of this roll-up-the-sleeve work. That’s powerful!
Viva la Resolution
Are you ready to champion data definition conflict resolution efforts at your organization? As I said before on this blog, it’s messy but essential work — and I encourage you to be willing to dive into the icky stuff! By doing so, you’ll demonstrate the true business value of data governance.