Data mismanagement is an epidemic affecting businesses everywhere. With the massive amounts of data being created, transformed and deployed every day, this epidemic should come as no surprise. But the volume of data is doing more than preventing organizations from effectively leveraging data for business intelligence. For many organizations, data mismanagement results in poor decision-making, increased compliance risk, damaged reputation and lost customers and opportunity. The cumulative impact and high costs of these can irreparably harm a business.
Data is such a valuable asset. Businesses cannot afford to habitually mishandle their data. When data is properly managed and used, it becomes an enterprise-wide advantage that drives better decisions, increases revenue and promotes growth. Fundamental to successful data management efforts is enterprise-wide data governance, which, at its core, delivers visibility into, strengthens accountability for, and enables utilization of an organization’s data assets.
Many organizations have data governance initiatives. However, those efforts are often siloed within departments and focused on policies to ensure regulatory compliance and appropriate data access. However, today’s organizations require an enterprise-wide data governance strategy, designed with business users in mind, to encourage a data-driven culture built on data understanding and collaboration.
To ensure the data governance framework stretches enterprise-wide, the program must begin at the executive level.
Data governance initiatives start at the leadership level. The first step is to appoint a data governance leader, typically the Chief Data Officer (CDO). The CDO is responsible for overseeing the entire team and ensuring that critical data governance tasks remain on track across the enterprise. Under the CDO’s guidance, the team should also include leadership from teams across the organization, such as finance, marketing, human resources and IT. Together, the CDO and key stakeholders must develop data governance processes that include policies and procedures and oversight of these rules.
Once data governance leadership has been established, a team that represents all lines of business should be assembled. This team is tasked with the stewardship of the essential components of a data governance program at an enterprise level. Responsibilities include establishing common data definitions and business glossary, developing a data catalog and determining what should be included, creating metrics as well as data quality scoring and monitoring, and clearly defining roles and responsibilities among data owners, business users, and stewards.
Data owners must ensure the continued regulatory compliance, appropriate access, usage and quality of their assigned data assets. They maintain responsibility for that data as it flows through an organization’s data supply chain, and assures that the data is being used and accessed in accordance with defined policies and procedures. Data stewards oversee the interpretation of data sets, produce easily digestible reports, and field questions from business users. Business users are then required to follow all established guidelines and policies outlined by management and report any data anomalies to the data owners. By clearly defining these roles and responsibilities, and engaging everyone in governance, a culture of collaboration is encouraged.
However, establishing a data governance team is only half the equation. Businesses must also adopt tools and technologies that offer integrated data management capabilities on an enterprise scale.
Successful data governance requires a comprehensive strategy, rather than one-off projects siloed within a single department. To facilitate an effective data governance framework, the data governance team must implement an enterprise data intelligence platform that delivers a broad range of integrated features for data governance, data quality and analytics.
The data intelligence platform should deliver complete transparency into an organization’s data landscape, from the data available, its owner/steward, lineage and usage, to its associated definitions, synonyms, and business attributes. It must enable all users to easily define, track, and manage all aspects of their data assets. The platform should also encourage a community approach, bringing people and data together. It should clearly define ownership and accountability for all the high value data assets, so business users know who to ask when they have questions about their data.
In addition, the platform must include data quality capabilities to ensure data remains complete, accurate, relevant, and consistent across the data supply chain to ensure business users have the confidence to utilize all data assets available. Analytics capabilities with machine learning algorithms can then monitor data quality while maximizing governance efforts, ultimately enabling data governance to improve data integrity automatically.
By combining the right data governance team with the right technologies, businesses can drive data utilization and build trust among business users. This will increase the value of data enterprise-wide.
Are you looking for additional details about setting up a new data governance program? Please download the data sheet below.