Math is the new sexiness in IT

So says Dhiraj Rajaram, founder and CEO of Mu Sigma, a Chicago-based startup providing analytics as a service to a large pool of Fortune 500 clients. He’s probably right, and that’s a problem.

Organizations, even large ones, might be masters of the fields in which they do business, however they’re not masters of applied mathematics, which is at the core of the growing data science trend. When it comes time to undertake a big data strategy that requires turning advanced algorithms on potentially massive data sets, many fast realize they don’t have, or have near enough of, the necessary skills internally. Attempts to hire these skills might prove largely fruitless as the small population of employees with the predicate acumen in both business and calculus are quickly snatched up by an evenly small number of companies.

Data warehouse is one thing

Analyzing traditional business data held in a data warehouse is one thing, nevertheless doing big data and, more exactly, data science is quite another. McKinsey & Co. predicts that by 2018, the United States will have a shortage of “1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions,” and a shortage of nearly 200,000 people with the deep analytical skils necessary for data science.

But enough about big business data. In an era of webscale computing and large clusters running big-data workloads, we’ll as well need more people who can apply mathematics to data with the goal of automating and troubleshooting distributed systems. Sure, predictive analytics can be great for determining how consumers are likely to react to changes to their favorite products, however they as well can be very helpful in helping ensure that complex systems just as Google’s run smoothly.

Anyway you look at it valuable area

Data science as it relates to business decisions is an anyway you look at it valuable area, and all the talk about big data probably ensures a fair investment in learning those skills. For organizations without internal skills, they can always outsource data science to companies just as Mu Sigma and Opera Solutions that exist to provide just such services. New, higher-level software products from startups just as Odiago, Platfora and others promise to alleviate some business-oriented analytic pain, as then.

But applying data science to data about software code or webscale system activity doesn’t always have a direct connection to income, which means it doesn’t get talked about as much. Those skills, but, are arguably as important to our growing Internet economy as big business data is to companies of all types. Hopefully, the message gets out and teenagers start to realize that if they want high-paying jobs with the coolest companies around, they’d better get a lot more interested in math.

The book about the future of business

If big data does in actual fact write the book about the future of business, Mu Sigma’s Rajaram says the climax will be “that mathematicians take the prom queen home.”