Democratization of Advanced Analytics
Authored by: Paul Lashmet (Arcadia Data) and Peter Memon (Synechron)
Mind the gap between Technology and Business Intent
Solutions to complex analytical problems often rely on the craftsmanship of highly-specialized experts in data science, machine learning, and other branches of artificial intelligence. The extent to which advanced analytics creates business value today is still limited (especially for businesses like financial services) because of a few key reasons, all of which also drive up the cost of monetizing a company’s data in the end. These include:
- Finding the right combination of advanced analytical skills and domain expertise doesn’t meet industry demand.
- Complex technical architecture as well as complex underlying data structure unique to specific relevant data-collection processes.
- Businesses often have in place a complicated set of tools – oftentimes legacy systems even – that make incorporating other newer [perhaps more complex] tools extremely hard to integrate.
The combination of these three primary factors present a significant challenge to generating positive return on investment (ROI) and establish a sizeable gap between the ability to implement sophisticated analytical tools and the actual intent of the business.