![]() ![]() A typical governance structure includes three components: The program continues to grow over time.Īs the example demonstrates, effective data governance requires rethinking its organizational design. ![]() These efforts have begun to pay off, allowing the organization to stand up priority data domains over the course of a few months (versus years) and reduce the amount of time data scientists spend on data cleanup, accelerating analytics use-case delivery. They then worked in sprints to identify priority data based on the value they could deliver, checking in with the CEO and senior leadership team every few weeks. Within their domains, they selected representatives to act as data-domain owners and stewards and directly linked data-governance efforts to priority analytics use cases. Once these leaders grasped the value of data governance, they became its champions. It assigned to each executive leader (CFO, CMO, and so on) several data domains, or business-data subject areas, some of which, such as consumer transactions and employee data, spanned multiple functions or lines of business. Then, as part of an enterprise-wide analytics transformation, it invested in educating and involving the entire senior-executive leadership team in data governance. For example, a leading global retailer, whose data governance was managed within IT, struggled to capture value from data for years. While many organizations struggle to effectively scale data governance, some have excelled. Building the foundation for effective governance In addition, firms that have underinvested in governance have exposed their organizations to real regulatory risk, which can be costly. Data governance is one of the top three differences between firms that capture this value and firms that don’t. Leading firms have eliminated millions of dollars in cost from their data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. While it’s challenging to directly attribute value to data governance, there are multiple examples of its significant indirect value. ![]() Indeed, the productivity of employees across the organization can suffer: respondents to our 2019 Global Data Transformation Survey reported that an average of 30 percent of their total enterprise time was spent on non-value-added tasks because of poor data quality and availability (Exhibit 1). ![]() Data processing and cleanup can consume more than half of an analytics team’s time, including that of highly paid data scientists, which limits scalability and frustrates employees. Without quality-assuring governance, companies not only miss out on data-driven opportunities they waste resources. While technology solutions such as data lakes and data-governance platforms can help, they aren’t a panacea. In other cases, organizations try to use technology to solve the problem. As a result, it becomes a set of policies and guidance relegated to a support function executed by IT and not widely followed-rendering the initiatives that data powers equally ineffective. The issue frequently starts at the top, with a C-suite that doesn’t recognize the value-creation potential in data governance. The problem is that most governance programs today are ineffective. Good data governance ensures data has these attributes, which enable it to create value. But for data to fuel these initiatives, it must be readily available, of high quality, and relevant. Even running the basic business well isn’t possible. There are no analytics driving new sources of revenue. Without it, there can be no digital transformation to propel the organization past competitors. Executives in every industry know that data is important. ![]()
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