Analytics can mean different things to different people. There is the pure data analytics concept of gaining insight out of different data sets in order to make more timely and smarter business decisions, or to get a predictive head start on future trends that could indicate hidden, non-captured value or even avoiding potential problems before they happen. With data packaged in the appropriate way and flowing through the right data architecture, these traditional benefits of a modern data analytics strategy are now very much achievable and can apply to most industrial sectors in business today. Linking this data analytics approach with the current advancements in artificial intelligence around Natural Language Processing (NLP) and Machine Learning (ML) and others, opens up almost endless possibilities.
General data analytics can also be linked with investment analytics to bring insight into the investment process. Performance measurement and attribution, risk analytics and rules-based compliance alert processing are all complex capabilities to implement in their own right. One thing they all have in common is that they are all very data intensive and lend themselves well to leveraging today’s cloud based big data framework. Calculations that can require thousands (if not millions) of data points are now able to be accomplished with speed and accuracy using advancements in dynamic calculation engines and in-memory processing. What could take hours to calculate in traditional approaches can now take minutes or even seconds. This opens up the possibilities of not only being able to calculate official book of record investment analytics results and storing them much more timely, cost effectively and efficiently; but it also breaks down the barriers of what-if and on-the-fly calculations to pursue different investment views at a moments notice.