Data Governance is a collection of policies, roles, standards, tools, and metrics that help organizations manage and gain better control over their data and data-related assets, ultimately ensuring that the data collected is useful and secure. Businesses with access to high-quality data that is secure, certified, and organized make better business decisions, create new products and services, improve profitability, meet compliance requirements and optimize operations.
Data governance programs are critical to the success of an organization’s data strategy. However, they are not widely acknowledged or known throughout the organization. Despite the dangers of poor data ethics and management, most C-suite executives have not grasped the value of data governance. They are behind in taking the necessary steps to ensure effective data governance.
The need for data governance is well-known, but without the correct personnel and leadership to oversee data governance initiatives, they fail. The solution is to establish a data governance leader along with a specific role such as the data steward who is solely responsible for managing a company’s data assets.
Data is invaluable to C-suite executives, and data quality is the root of the success of modern enterprise growth. With disparate data sources, combined with sensitive data such as PII, PCI, and PHI data, the ability to summarize, manipulate, and gather insights from data can become complex, and data quality must remain flawless.
This brings in data stewards, a relatively new role responsible for the maintenance of data. They ensure the compliance and quality of the data that they oversee. According to research by Gartner, poor data quality costs organizations an average of $12 million a year. Organizations need data stewards to lead good data management practices and monitor data quality problems. Many businesses recognize the value of data as an asset only if there is one sole person responsible for the management, security, and compliance of where the data has been, where it’s going, and who has access to it. As those businesses apply new business models to generate insights and deliver customer value, they need to have the proper leadership and expertise in place to manage the data.
Data governance leadership is the bridge between the different departments such as data engineering, IT, Infosec, and analytics teams. A clear RACI (Responsible, Accountable, Consulted, Informed) framework should be established at the beginning to ensure clarity, ownership, and responsibility across departments. An example of RACI is provided below:
An organization that lacks a strong emphasis on data value leads to a weak data culture. A report from a 2019 Deloitte report found that companies with a higher emphasis on data were twice as likely to exceed business goals compared to companies with weaker data cultures. In order for enterprises to account for the lack of data leadership, they need to maximize data value. Data value is the difference between outcomes that a business achieves by using data compared to what they would have achieved if they did not use that specific data. The value is therefore reflected in improved risk management, increased revenue, or decreased costs.
Data is difficult to manage when it is constantly overlooked, and there is a lack of visibility of data across the organization. This leads to the development of data silos. A data silo is a collection of data controlled by a single department and is stored in a standalone system. The problem is that silos are isolated from other departments within the organization, leading to mismanagement and lack of accessibility across departments. Data governance programs help improve visibility and eliminate data silos by organizing, integrating, and cataloging a company's data. Data governance ensures that the C-Suite has more visibility and control over the data being gathered across the different departments, enabling data sharing, ensuring security, and accelerating time to insight.
The integration effort and the technology, architecture, and processes required to source and create persistent data stores are the responsibility of the data engineering team. The data governance team owns building the data foundation and certifying data after the data engineering team completes data integrations.
Another major challenge with data governance is how to quantify its effectiveness correctly. Time is valuable for enterprises, and data governance helps cut down on minute tasks by streamlining and sorting data. According to a 2019 McKinsey Global Data Transformation Survey, an average of 30 percent of an organization’s time was spent on non-value-added tasks because of poor data quality and availability.
Monitoring ROI and KPI metrics to track the success and growth of data governance technologies and solutions strengthens the value of governance to the C-suite and furthers data governance within the enterprise. It also helps establish funding for data governance programs. The following working KPIs can be used as reference points for data governance leaders:
Time to find an organization’s assets
Time to onboard data sources to solve a new use case
Information to use
Active data assets
Number of days between requests and fulfillment of data
Avoidance of Risk
Data assets covered by governance policies
Proper governance enables cross-functional collaboration and promotes a data-driven culture. Investing in data governance is only effective if the program can address critical issues such as providing data stewardship and leadership, breaking down data silos, and correctly leveraging success metrics. With the right technology and a strong leadership team, C-Suites will find that data governance can overcome common data challenges, unlock new revenue streams, and significantly optimize operations.