The recent discussions about the “new” world of business analytics should come with a disclaimer: “It’s not really brand new!”
Businesses have been using analytics for years. It may be more accurate to say that analytics is experiencing a renaissance brought about by recent advances and investment in technological and data capabilities. As a result, business analytics has reached the next level of maturity.
But business isn’t the only field notable for major advances in analytics in recent years. If anything, there may be a stronger case for the sciences leading the vanguard of analytics. Universities, research labs and other science-focused organizations have been applying and refining analytics approaches to solve some incredibly complex problems through the years in the areas of astrophysics and the social sciences, just to name a few. The only difference is that they never called it “analytics;” for them, it’s all science.
Cross-pollination between the two worlds
This environment— marked by a reinvigorated interest in business analytics, combined with separate, but related, advances in analytics in the sciences—is one that is ripe for cross-pollination.
Already, we are beginning to see techniques borrowed from the world of science and applied to business challenges.
In one example, a leading audit analytics software developed integration capabilities for R and Python, two popular programming languages for statistics, research and data science developed during the early ‘90s. Integrating these languages is the first step toward achieving machine learning and predictive analytics, which are concepts not usually heard in the field of audit. Audit used to focus solely on historical evidence, but to provide more value to today’s organizations, the practice has ventured into model-building and forecasting.
In another example, a financial services organization leveraged tools used by DNA researchers to unlock insights buried in tens of thousands of emails. The sheer volume of emails received by the organization left it vulnerable to serious issues if it failed to handle the emails in a timely manner. With only a limited number of personnel responsible for making sense of all the messages, the organization resorted to “text analytics.”
Text analytics made it possible for the company to parse individual messages for key words and phrases and route the email to the appropriate handler. To elevate its text analytics capabilities, the organization applied learning from bio-informatics, wherein scientists match DNA sequences. DNA, after all, is represented by a series of letters that occur in non-random patterns, very similar to what one might see in an email message.
Using algorithms originally developed to compare DNA sequences, this organization cracked the code on thousands of messages received every day. This approach was deployed in the organization’s workflow to tag, route, and prioritize messages, allowing the company to get its customer interactions back under control.
More sharing in the works
These developments are in the nascent stage now, but there are plenty of signs of a coming explosion in shared analytics tools, techniques and processes between the sciences and the business world. From major airlines and insurers, to oil and gas companies and beyond, the business community is actively hunting for science-based approaches that can give them that competitive edge.
The author is a director with the Risk Advisory group of Navarro Amper & Co., the local member firm of Deloitte Southeast Asia Ltd.—a member firm of Deloitte Touche Tohmatsu Limited—comprising Deloitte practices operating in Brunei, Cambodia, Guam, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam.