As more and more organizations come to understand the power of data, newer methodologies and tools are being adopted to make it fast and more insightful. Not long ago, the wave in data quality had everyone thinking, correctly, that collecting data is not much use if it’s not of high quality. That meant accurate sampling, reliable sources and measurement techniques, completeness, as well as timely implementation. Now, however, a different sort of challenge faces the stakeholders – the rapidly increasing size of data warehouses.
This means that there is a need now to look beyond data quality, and that a single source or person is no more responsible for organizing and controlling data. The practice that deals with this is known as data governance.
Recognizing Data Governance
An enterprise needs data governance when decisions involving data management or business insights based on these require multiple inputs from different stakeholders. In these terms, data governance calls for studying and understanding the data processes in use and evolving an organization-wide policy. This means that at any level, data is being collected, accessed, or analyzed in line with the business strategy. This may not sound very important to businesses which haven’t arrived at that scale yet, but many medium- to large-sized organizations can testify to the productivity loss in the absence of a well-defined and enforced policy.
Implemented in the right way, data governance is sure to reduce redundancy and improve the quality of business decisions. However, for that to happen, it needs to be adopted across the organization and made an important part of governance.