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The Smart Techie was renamed Siliconindia India Edition starting Feb 2012 to continue the nearly two decade track record of excellence of our US edition.

Proper Care and Feeding of Analytics Pros

By Dhiraj Rajaram
Founder & CEO-Mu Sigma
Thursday, November 29, 2012
Dhiraj Rajaram
Mu Sigma is a provider of decision sciences and analytics services, helping companies institutionalize data-driven decision making. Headquartered in Northbrook, IL, the company was founded in 2004 by Dhiraj C Rajaram and has raised around $133 million funding from General Atlantic and Sequoia Capital.




In the era of Big Data, a lot has been written about how hard it is to find analytics professionals, otherwise known as data scientists. A McKinsey study published last year claims that the U.S. alone will face a shortage of 140,000 to 190,000 such professionals, as well as 1.5 million managers and analysts needed to analyze big data and make decisions based on their findings.
This article assumes you have already been lucky or skilled enough to hire some of these elusive people, and covers how to keep them engaged and retained. Mu Sigma employs nearly 2000 data scientists– arguably one of the biggest such staffs in the world – so we have a lot of experience keeping them happy.

What’s our secret? Over the years we have found three main success factors:

1.Foster collaboration

Data scientists are essentially schooling fish –they need like-minded people around to bounce ideas off of. If they work alone or in too small a group they get lonely and bored. Solitary confinement saps them of their natural curiosity and stifles their creativity. Brainstorming and collaboration are key to their survival and ability to flourish.

2.Allow them to spend time on discovery-driven analytics, not just problem-driven analytics

Data scientists need the freedom to explore their ideas – and often the wackiest ideas lead to the best breakthroughs. Be sure to give them the opportunity to spend time on discovery-driven analytics, not just problem-driven analytics. What does this mean? Traditionally, analytics teams are problem driven; that is, they are asked to solve a particular problem by applying analytics techniques. For instance, “Why do we see a dip in sales?” or “Which insurance claims are likely to be fraudulent?” Increasingly, however, businesses are giving their data scientists the latitude to perform discovery-driven analytics – allowing them to explore data for patterns, and see where it takes them.


You stand a much better chance of discovering a game-changing insight by taking this approach, because you do not start with preconceived notions. Does each path pay off? Of course not. But the ones that do tend to be more insightful, and more impactful.

A related approach we love is the concept of the Simmer Project.A tactic our client Microsoft introduced us to, Simmer Projects are essentially side projects that are not expected to contribute any revenue in the short term – really just experiments. They simmer on the side for a few months – or maybe even a few years – while their creators watch and wait, occasionally adding ingredients, and then decide if they should be moved to the front burner or poured down the drain.

Each researcher typically has 1-2 Simmer Projects going on at any given time, and the best ones eventually get put on Microsoft’s roadmap. I am told that some of Microsoft’s most well known products have begun in this manner.

What a great example of a company taking a long-term view on innovation – instead of putting every available body to work on short-term goals, Microsoft has given its employees creative license to experiment in the kitchen.


3.Ongoing Education

The field of analytics is constantly changing. New technologies and approaches are emerging every month. Just like software developers, data scientists need to be up to speed on the latest tools and trends. It’s crucial that employers give them the opportunity for continuous learning and development – from mentors, from each other, and through more formal training, education and conferences. They like continual challenge, and frankly, most data scientists I know (myself included) love to show how smart they are.


At Mu Sigma, we run an internal training program called Mu Sigma University that not only inducts new hires into our way of doing things, but also serves as a platform for continuous learning for veterans. It’s not just a benefit to employees, but also ultimately to our clients who have access to extremely well trained analytics consultants and practitioners.
These tactics have all worked for Mu Sigma. What steps is your organization taking to preserve and retain these precious resources?

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