Data is a critical resource for every organization. We depend on data every day to keep records, produce reports, deliver information, monitor performance, make decisions, and much 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, IT organization, and BI program. 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.

The frequent occurrence of data misuse and theft has caused companies to reexamine their approach to data protection and privacy. Most business people think little about their responsibilities when using data to support business decision making. The evolution of laws around the world focused on data protection and privacy has established a new set of responsibilities in the world of data usage and business analytics. The day is quickly approaching where companies will need to be able to manage and track data usage, data location, and customer consent.

During this class, Evan Levy will discuss the details of data privacy and protection and review the activities that go into supporting a data privacy and protection initiative in your analytics environment.