Automating data governance for improved BI insight

In the past, there has been a view that data governance is a barrier to insight, a 'nay-sayer in the room,' or a set of policies that adds time and money to data projects. That view has to change, and is changing. And in the new self-service BI environment, with the associated explosion of data - in which almost anyone can access BI tools, run reports and generate their own data visualization projects - sound data governance is not just a requirement, it's an enabler for greater insight. A business-wide data governance implementation can not only carry out the base expectation of keeping the business out of jail, it can also speed up time to insight, improve data quality, and save time and money.

The self-service model has transformed approaches to data within organizations, although 'insight silos' can form as a result. We'll look at what data governance needs to do in this environment, and how collaboration improves as a result of better governance.

Core to this automation is ensuring that data is audited, activity is logged, access is controlled, and data definitions are consistent. The alternative, happening across many businesses, is a level of manual data governance that is hard to maintain and potentially costly. Ultimately - and most importantly - data governance should empower the end user, the 'data consumer.' Automation can take away a lot of the dirty work but it can also ensure that data quality is maintained and that the right person has access to the right information.

What is data governance?

A data governance program will set out who is responsible for different aspects of ...