This site uses cookies to provide you with a more responsive and personalized service. By using this site you agree to our use of cookies. Please read our cookie notice for more information on the cookies we use and how to delete or block them.

Bookmark Email Print this page

Do You Need Data Scientists?

Short Takes...on Analytics

Posted by Tom Davenport, Independent Senior Advisor to Deloitte Analytics

I’ve recently been doing research on the rise of the data science function and the data scientist role within organizations. To say the least, it has been fascinating to talk to people who are intimately involved in the management of big data within their firms. In general, I have found that they are hybrids of data management specialists and quantitative analysts. They should have the data management skills to wrestle with big data—to extract it from a rapidly-flowing stream, to clean it, and to structure it sufficiently so it can be analyzed. The quantitative analytics strength comes in handy when companies want to make sense of what’s happening in the external world (big data is more likely to be about what’s happening externally), or to create algorithms that can help drive product usage (such as features that help identify people you may know on social network as an example created by data science teams).

How does data science differ from traditional analytics functions? One is that data scientists are more likely to be associated with a product engineering or development team, and to be creating prototypes and demos as outputs, rather than reports or presentations. Another is that they are a bit less likely to spend time on analytics due to the effort devoted to data management—as one big data executive put it, “Big data often means small math.” Data scientists are also a lot more likely than typical quants to spend their days writing code. Most data scientists are also likely to be obsessed with speed and rapid development—probably because many work for startups.

Someday we’ll probably have standard tools that can help subdue big data, and the people with PhDs in experimental physics or computational biology—common backgrounds for data scientists—can go back to pure science or back to Wall Street. Today, however, they’re essential for any organization that wants to make progress at managing big data and turning it into something useful.

This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor.

Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication.

About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Please see for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

Copyright © 2012 Deloitte Development LLC. All rights reserved.
Member of Deloitte Touche Tohmatsu Limited


Related links

Share this page

Email this Send to LinkedIn Send to Facebook Tweet this More sharing options

Stay connected