Key takeaways:
- Establishing an effective data governance structure is essential in deciding who in your organization can be assigned accountability over the various data assets.
- Data quality tools can streamline accountability and integrate with existing data catalogs for efficient reporting.
- Measuring how efficiently your data quality team responds to issues is one major KPI for data strategy leaders. While there may be an abundance of data quality issues, knowing who oversees the data asset and which teams can resolve issues can be efficient in ownership.
Establishing effective data governance is essential for creating quality data that can be used internally and externally throughout your organization. But when it comes to creating quality data, there’s an ongoing issue that enterprise data strategy leaders face: assigning accountability over that data.
During a recent Enterprise Data Strategy Board panel discussion on elevating data governance at a large enterprise, senior data leaders at the world’s biggest companies shared insights about implementing the best structures and technologies for data quality accountability.
Here are their insights into how you can ensure your data team creates shared responsibility for reporting and using data in your organization.
Establish an effective data governance structure
Creating an effective governance structure is essential in assigning accountability, as Enterprise Data Strategy Board Member Renee Langeness, Director of Data Governance at Principal Financial Group, detailed.
“For a long time, I believe everybody thinks that it’s somebody else’s job to be accountable for the appropriate data management practices,” Renee said during the panel discussion. “We have to shift that mindset to the accountability that resides with the person or people who are managing that data.”
To implement an effective governance structure, her team at Principal Financial Group spent the past year redefining their roles and creating clear responsibilities for each position.
Renee explained her team created specific responsibilities for roles with ownership over data assets, including ensuring all data quality requirements are met across their organization. Her team then made standardized procedures to hold the data owners accountable for any reporting.
She shared how the clear responsibilities and governance structure over data assets were key steps in creating accountability across Principal Financial Group.
“Data management without quality checks or requirements put in place is not good data management,” Renee added.
Enterprise Data Strategy Board Member Scott Zinn, Vice President of Enterprise Data Governance at American Express, shared the same sentiment as Renee when discussing the importance of establishing effective governance over data quality.
“In one month in this space, I don’t know how many conversations I had around who owns what,” Scott said. “If you don’t formalize that, if you don’t use some degree of domain or subdomain for the way you organize data on your company, it gets tough to move past that part of the conversation.”
“Data management without quality checks or requirements put in place is not good data management.”
Renee Langeness, Principal Financial Group
Data quality tools are effective for assigning accountability
You can also leverage various tools to help your team assign accountability for the data across your organization.
Enterprise Data Strategy Board Member Lisa Novier, Head of Governance, Risk, and Compliance for Data Analytics at Envestnet, discussed how she’s currently choosing a new tool that prioritizes integration with existing data catalogs.
“We’re looking at transitioning to Collibra Owl, because it integrates with our data quality catalog,” Lisa said. “The data quality tool integrates with our governance catalog, and I think that’s been one of the challenges for me in the past. If the tool doesn’t integrate with your catalog, and you can’t have the visibility into the data quality linked to the data domains that you’ve set up, it can be very challenging for everybody to understand whether the assets they use have the quality that they need.”
Lisa noted how it requires collaboration across all business lines and stakeholders when it comes to choosing the right tool or deciding if it’s more effective for your company to build one internally. She also said it’s necessary to ensure the tool you select meets all requirements of your organization.
“Traditionally, we’ve used an internal tool, but we’ve decided that that’s not going to give us the transparency and visibility that we need into data quality because the output of that sits with one group,” Lisa said. “Now we’re moving into something that can view that data quality across a broader audience of people in the departments.”
“If the tool doesn’t integrate with your catalog, and you can’t have the visibility into the data quality linked to the data domains that you’ve set up, it can be very challenging for everybody to understand whether the assets they use have the quality that they need.”
Lisa Novier, Envestnet
Determine which KPIs you should measure for data quality
Part of assigning accountability for the quality of your data includes understanding which metrics you should measure. Scott explained how one key area of measurement he finds important is how effective your data quality structure performs when challenges arise.
“Ultimately, it starts with how well we are reducing the issues, and when we do have data quality issues, how efficient we are reactively in solving those,” Scott said.
He explained how it’s essential to measure how fast you can identify any data quality issues with the correct accountability party in your organization. While he noted it’s important to measure the quantity of data quality issues in your organization, he also shared how it’s critical to locate the right teams in your organization who are accountable for the data assets and resolving issues quickly.
Lisa added that it’s essential to develop KPIs for qualitative and quantitative data quality measurement. While you may experience high volumes of data quality issues, she stated, “If the data consumers aren’t saying that they can’t consume it because it doesn’t meet their needs for the business, then you still have a problem. So I think you must look at both sides of the equation.”
“Ultimately, it starts with how well we are reducing the issues, and when we do have data quality issues, how efficient we are reactively in solving those.”
Scott Zinn, American Express
Gain more insights into how you can elevate data governance and quality in your organization
Renee, Lisa, and Scott gave more leadership insights into how you can effectively assign accountability for data quality across your organization during the Enterprise Data Strategy Board panel discussion.
They also discussed how data governance councils can help provide quality data and how you can build a data governance council for long-term success.
If you lead data management, analytics, and governance at a large organization, you can apply to learn more about how the Enterprise Data Strategy Board gives you unbiased peer insights in our confidential, vendor-free community on the emerging challenges you face.
