The goal of a data governance framework is to strike a balance between risk mitigation and agility that leads to innovation.
As the volume of data organizations collect grows exponentially and data becomes more important than ever to cope with rapidly changing economic conditions, data governance is essential. And a data governance framework, when well developed, provides an organization with the necessary structure for data-driven decision making.
There is a need to manage risks, which can range from the use of poor quality data leading to a poor decision to a potential violation of regulatory restrictions. And there is also a need to foster informed decisions that lead to growth.
But setting limits on which employees can use what data, while further limiting how certain employees can use data based on their roles, and simultaneously encouraging those same employees to explore and innovate with data are principles. apparently opposed.
So a good data governance framework strikes a balance between risk management and empowerment, according to Sean Hewitt, president and CEO of Succeed Data Governance Services, who spoke at a virtual event on April 26 hosted by Eckerson Group on Data Governance.
A good data governance framework instills confidence in employees that whatever data mining and decision-making they do in their role, they do it with appropriate governance guardrails in place so that they explore and make decisions safely and without danger. their organization.
“The goal is to create a balance between governance controls and then agility and innovation,” Hewitt said. “You need to use enough governance to mitigate those risks and [at the same time] maximize the value you get from the data.”
However, finding this balance is not easy.
But there is a process organizations can follow to develop an effective data governance framework, according to Hewitt.
Implementation of data governance
Before even assigning data stewards and setting limits on who can access what data, organizations need to understand exactly what data they have.
Without knowing metadata – data about data – businesses risk becoming disorganized.
“Metadata is the fuel of data governance, so organizations need to build that intelligence,” Hewitt said. “If they don’t have the metadata available, they’re flying blind.”
Sean HewittPresident and CEO, Succeed Data Governance Services
Metadata lets organizations know, for example, where their data comes from, such as a point-of-sale application, whether it was entered manually or automatically, how many people viewed and used a point or set of data, whether the data has been manipulated or modified and by whom, and the age of the data.
Metadata also allows organizations to define and catalog their data so that it can be easily found when needed for decision making.
“The starting point of data governance is metadata,” said Josh Reid, partner and principal advisor at Succeed Data Governance Services. “Metadata really helps you build your data intelligence. Everything we document is metadata, and it’s essential for a data governance program.”
After gaining an understanding of the data available, organizations should assign responsibility for different datasets and data domains to a data manager or owner.
And what exactly those responsibilities need to be explicitly defined, according to Hewitt.
“Organizations need to be crystal clear because confusion is the enemy of data governance,” he said.
Data managers/owners are likely the ones empowered to set limits on who can use a particular set of data to mitigate risk, while simultaneously ensuring that employees can securely access the data they need. to fulfill their responsibilities.
They are also usually the ones responsible for keeping the data up to date and ensuring that the metadata is accurate so that the data can be easily found and used correctly.
Once data stewards/owners are determined, comes the development of a data governance plan – the actual steps that lead to balancing risk management and employee empowerment. The development and implementation of processes and policies support the desired outcome of a strong data governance framework.
“The vision, mission, and goals bring everyone together, create a common understanding, and lead everyone in the same direction,” Reid said. “Together, with a strategy and a roadmap, the leadership team and the people doing the work have a clear understanding of what is going to be done, when it needs to be done, and what success will look like.”
Finally, organizations need to ensure everyone is set up in their role to execute their data governance plan – including education – and they need to monitor and improve their plans over time.
This monitoring and improvement process includes putting in place a set of metrics that can be reviewed regularly to monitor progress, confirm goals are being met, and make improvements to the data governance plan as needed.
“All of these things need to be integrated into a data governance framework for it to work optimally,” Hewitt said.
Despite what seem like logical steps to implementing a strong data governance framework, many organizations struggle with data governance.
And at the heart is the cultural mindset, according to Hewitt.
Too many organizations see developing a data governance framework as a project rather than a process. A project is a once-done endeavor that has an end date, while a process is something that is ongoing.
“That’s why a lot of organizations are struggling,” Hewitt said. “They don’t accept that data governance is a cultural transformation.”
Those who recognize the need for data governance – often those in IT departments – must therefore be adept at managing change, he continued.
“They need to understand the mindset, history and traditions of their organization,” Hewitt said. “They need to understand the power relationships so they can use those power relationships to help overcome some of the barriers.”
And they need to show quick success, Reid added.
This can cultivate management support. Then, as momentum builds, those who benefit from the data governance framework—end users authorized to work with data with appropriate safeguards—can gain confidence. Finally, after successful pilot programs in selected areas, data governance can expand to all departments until it becomes part of the culture of the organization.
It’s a top-down approach, then bottom-up, and finally middle-out, according to Reid.
“It shows that everyone in the organization has a role to play in data governance,” he said.