Building a strong enterprise data governance framework is critical to the success of any business today. It takes months, if not years, to establish a strong data governance program that maintains a solid foundation, but has the flexibility to evolve as an organization’s data management strategy matures. Ultimately, successful data governance is dependent on a careful balance of people, processes, and technology to get the most out of data. Within this ecosystem, technologies are certainly important, but it is the team tasked with developing and maintaining the program that makes the difference between data mediocrity and excellence.
Just as with executing a data governance strategy, it is critical to carefully select your data governance resources. This team will be the ones to seek budget and set business goals and priorities. They will also determine how they want to structure their data governance model and the critical technologies to adopt, and begin the crucial task of evangelizing governance across an enterprise. Because the team in many ways is the conduit to creating a data-driven culture. They want to build enthusiasm and energy around data across the organization, to ensure ongoing utilization, collaboration and communication.
Since data governance starts at the leadership level, it is important to have a team leader to serve as a Chief Data Officer (CDO) or data evangelist. The CDO is responsible for overseeing the entire team, and ensuring that data governance efforts remain on track. He or she may also be responsible for communicating procedures and monitoring success. Under the CDO’s leadership, the team should consist of executive-level leadership from diverse departments, such as finance, marketing, human resources and IT. Together, the CDO and the data governance leadership team must develop data governance processes that include policies and procedures and provide oversight for these rules.
After the leadership team is in place, organizations need management-level employees from diverging lines of business to establish a team responsible for fostering collaboration. Together, they must establish common data definitions, develop and monitor data quality scores, create governance metrics and clearly define roles and responsibilities among data owners, users, and stewards to ensure that governance is meeting compliance standards and everyone is fulfilling their assigned responsibilities.
Next, organizations must appoint data owners, stewards and users. Data owners are responsible for making sure a specific data set’s quality remains intact as it flows through an organizations data supply chain, that data meets any regulatory requirements, and that it is being used and accessed appropriately according to defined policies and procedures. Data stewards oversee the interpretation of data sets, produce easily digestible reports, and field questions from business users. Users are required to adhere to all established guidelines and policies outlined by management and report any anomalies they uncover to the data owners.
Once the data governance team and processes are established, the organization must choose the tools and technologies that will make their vision a reality. The best tools to accomplish this are those that offer integrated data management capabilities on an enterprise scale.
Successful data governance must be executed as an enterprise strategy, not a one-off project or departmental responsibility. To facilitate an effective comprehensive data governance framework, the data governance team should select a solution suite that delivers a broad range of integrated capabilities for tasks like metadata management, data quality and analytics. It 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 needs to enable all users to easily define, track, and manage all aspects of their data assets.
Data governance should be the foundation of data management, and the force that helps cultivate a data-driven culture. To do this, look for an enterprise data intelligence platform that encourages collaboration through its interface and workflows, to champion a community approach and help bridge the business to technical divide, bringing people and data together. It should clearly define ownership and accountability for every data asset, so data consumers know who to ask when they have questions about data meaning and usage. Data quality should be an integral part of any data strategy, and the solution should ensure the accuracy and reliability of data across the data supply chain with data quality checks such as data profiling, consistency, conformity, completeness, timeliness, and reconciliations. Governance should include embedded machine learning for ongoing quality monitoring and improvement.
Equipping the data governance team with the right technologies helps drive data utilization and build trust among business users, and increase the value of enterprise data assets for years to come.
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