Data is a critical resource for every organization. We depend on data every day to keep records, produce reports, deliver information, monitor performance, and make decisions. Data assets power BI dashboards, self-service analytics, data science projects, AI and machine learning, and more. The data resource is on par with financial and human resources as a core component of doing business, yet data management practices are often quite casual and unstructured. Data governance brings the same level of discipline and structure to data management as is typical when managing financial and human resources.

Building a data governance program is a complex process that focuses people, processes, policies, rules, and regulations on achieving specific goals for a managed data resource. Successful and effective data governance depends on achieving the right balance between control-oriented and collaborative governance to address the often conflicting needs of enterprise data management, self-service, agile development, big data, and cloud deployments.

Data quality is one of the most difficult challenges for nearly every business, data management program, and BI and analytics team. Data resources power enterprise reporting, BI dashboards, self-service analytics, data science efforts, AI and machine learning, and more. The most common approach to data quality problems is reactive—a process of fixing problems when they are discovered and reported. But reactive data quality methods are not quality management; they are simply quality maintenance—a never-ending cycle of continuously fixing defects but rarely removing the causes. The only proven path to sustainable data quality is through a comprehensive quality management program that includes data profiling, data quality assessment, root cause analysis, data cleansing, and process improvement.

Data governance success relies upon a strategy and roadmap that improves data and delivers business value. In this course, you will learn to build a successful data governance strategy that identifies an agenda, engages with team members, and prioritizes activities focused on fixing problems to streamline and simplify data usage.

In this class, Evan Levy will review the business drivers for a data governance program and show you how to identify and prioritize program goals and objectives, identify teams and team members (along with their roles and responsibilities), and identify an agenda and process that delivers business value.