In June, CUBE hosted a webinar with Matthew Bernstein and Lynn Molfetta of MC Bernstein Data, as well as our very own Head of Customer Success, Lee Fairey, to discuss the growing significance of Regulatory Intelligence within Information Governance.
During the webinar, Lee, Lynn and Matthew took questions from the audience. CUBE reflects on the answers provided on topics from ROI for RegTech, to the future of information governance. In the second blog of the series, Lee Fairey sets out the importance of data enrichment in the development of information governance.
There are a lot of different solutions that are providing regulatory content in the area of information governance, but this still provides a lot of data which takes a long time to interpret, assess and analyse. How can this be improved?
Simply, I think its data enrichment. There are many companies out there that will collect data and provide data for organisations to interpret and classify. I think with CUBE the one thing that we do – and we do well – is we make sure that we enrich that data. When you’re presented with a complex regulatory landscape, it is particularly helpful to understand what those regulations pertain to – whether that’s a process, whether that’s a topic, whether that’s a jurisdiction.
Data enrichment is key because what that enables organisations to do is consume the data in a meaningful, structured and effective way. I think that’s extremely important in the information governance space because, as we’ve mentioned, there will be regulations out there that talk to the same topic, that will talk to client due diligence. There’ll be regulations that say, ‘you need to keep this information for this period of time’ and then there’ll be data privacy considerations.
Effectively, if you’re smart in the way you collect the data and you use regulatory intelligence tools to analyse that data and enrich that data with some of those key themes and some of the raw extraction that we spoke about earlier, it enables organisations to consume that data much more effectively and has the added benefit of removing human interpretation, which is error prone and inefficient.