The principles of data governance

- Let's look at three of the core principles of data governance. They are transparency, accountability, and standardization. I'll begin with the principle of transparency. Everyone within an organization and those who interact with the organization from the outside should be able to understand and be advised in a timely manner of the processes and impacts of data governance. Transparency is essential for avoiding surprises to gain buy-in from those impacted and to build trust in the organization. Transparency should include being clear on why things are being done and the value of those actions. As an example, an organization may have an information security policy that clearly states what kind of data must never be displayed on its public website. The policy should state why this is important and how it's being enforced. Data governance is a positive topic, and that means we want to be open and clear about it throughout the life cycle of data. The second principle is accountability. This encompasses the responsibilities of anyone who has a role to play regarding data in an organization. Each role must be fully described, understood, and agreed upon by all involved parties. Within a mature data governance environment, people are held accountable for taking certain data-related actions at specific times. As an example, if a role requires a person to disclose what stocks they hold and that they must enter that data by a certain date, by not adhering to this requirement could result in termination of employment. The third principle is standardization. With standardization of data, we are concerned with such things as how data is consistently labeled, described, and categorized. An example of data standardization is how an organization might handle U.S. state designations. When California is being entered in a system, is the standard to use CA or to spell out the entire state name, or something else? This is an important question. For example, if a search is made to include results from the California area, the lack of a standard may result in incomplete results. That is, if the search is being made using CA only, the results with the full word California may not show up. Standardization solves this challenge and many more. Among its many benefits, standardization improves the quality of data, helps organizational communication because there is a basis for understanding, and makes data more usable across systems. The collective result: data becomes more valuable. Related to standardization is the concept of master data management, or MDM. To increase the ability for data to be governed, understood by stakeholders, and even shared across enterprise systems, there must be organizational agreement on the details of reference data. By reference data, we can think of core data sets that help run the organization day to day, data such as customer and supplier details, and product and part attributes. Master data management uses this standardization to enable better enterprise operations and data integration within the organization and with external entities. In a later video, we'll see how contemporary data catalogs are enabling enhanced MDM. Data governance is not limited to these principles, but they represent the type of discipline and attention that is required. As this course progresses, other important principles of data governance will be identified and explored.