Data governance(DG for short) is the overall management of principles and practices for ensuring the high quality of data throughout its life cycle. DG oversees the availability, usability, integrity, and security of an enterprise’s data. The Data Governance Institute defines DG as a “practical and actionable framework to help a variety of data stakeholders across any organization identify and meet their information needs.” In other words, DG is an essential component for compiling, organizing, and utilizing big data in ways that are beneficial to the company’s infrastructure. DG helps data stay consistent and trustworthy. This in turn allows businesses with successful DG programs are able to make better business decisions, optimize their company’s operations, create and promote their products, and ultimately become more profitable over time.
Data Governance Framework
The groundwork for a successful DG program contains a governing body, a defined set of procedures, and a plan to execute them. It governing body clearly defines data stewardship, dedicating the owners or custodians of the company’s data assets. This is good for maintaining clear chain-of-custody records and roles with regard to the data. It defines without ambiguity the personnel responsible for ensuring that the data is properly handled and secured.
A clearly defined set of data procedures will effectively cover data storage, archival, backup, and prevention of loss or damage to the company’s data. It outlines how the data is to be used within the company and by whom. This acceptable use policy also designates the level of usage to authorized personnel on a hierarchical basis. Additionally, clearly defined procedures and strategies are essential to maintain compliance with company and government policies.
The goal of and DG program is to ensure that data quality is maintained or, if necessary, improved to the best standards. Implementing a data scrubbing or data cleansing regimen on a regular basis will maintain good data quality by accounting for the various ways in which the same product or customer is described. It also a mends or removes incorrect data from a database.
Data quality is also maintained by implementing master data management. Master Data management allows a company to link all of its critical data from a common data point. MDM is capable of facilitating computing across multiple architectures, platforms and applications. This improves DG and overall company productivity by streamlining the data sharing process between various personnel and departments.
A final key to proper DG is for the company to have an adequate metadata and model management infrastructure in place. The metadata and model management infrastructure is the DG program’s deployment mechanism ; it is also the brains behind DG maintenance. As such, the infrastructure should be easily accessible and user-friendly; it should be intranet based; it should be company efficient, able to execute critical processes with minimal resources and information necessary and it should be enforced, to streamline adherence to company standards and policies.
Additionally, make sure that the infrastructure includes an accessible communication mechanism, which means making sure the company’s intranet is online and accessible by all personnel. The maintenance process needs to be easily accessed and able to be modified with minimal time and resources needed when the time for changes and updates inevitably arrives.
