c4s-search-the-future-of-business-intelligence

Here’s a startling statistic – 80% of corporate business intelligence projects fail. These initiatives don’t fall short because servers melt down or lots of data isn’t successfully crunched; they fail because after long and often expensive implementation cycles, they produce no concrete value for the enterprise. Put another way, they fail to meaningfully transform a business process or processes in a way that creates competitive advantage.

I have recently spoken to many “Heads of BI” at my clients who work in the Financial services sector. I was struck by the fact that, while most of the clients I spoke to would probably be considered as “hot on business intelligence”, many struggle to make the transition from data capture and analysis (research and development) to outcomes (ie production systems that create competitive advantage).

They believe this is because they rely on tools and systems that put the burden on business users to uncover insights and take action. As the volume of raw data that financial enterprises generate as part of their daily operations grows exponentially, the ability of legacy analytical tools to provide clear insights is degrading rapidly. And simply trying to cram more information into a dashboard will only compound the problem by presenting too many signals for most business users to understand and derive meaningful insights from.

Fortunately, many of these companies have started to recognise this issue and search for solutions. There seems to be a large shift towards and huge emphasis on machine learning and Big Data, specifically around how these technologies can be applied to drive better outcomes for financial institutions. This has been a massive area of focus for one of my clients who; as a company, have worked tirelessly to develop a purpose-built platform that combines internet-scale data integration, data enrichment, machine learning, and automation recipes to deliver actionable insights directly to their business users at their point-of-work”. Their overall goal is to close the data-insight-action loop.

These “Systems of Insight” go far beyond the capabilities of legacy analytics platforms. Rather than just looking back to past data to infer trends, they leverage advances in machine learning, AI and predictive analytics to deliver insights where and when they are needed most to recommend actions, drive outcomes, and ultimately change the way companies use their data to do business.