Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

Doug Henschen

Doug Henschen

Executive Editor, InformationWeek

Big Data And Analytics Expertise: Beg, Borrow Or Steal?

Demand for big data and analytics experts is on the rise, but it will take years for the supply to catch up. Here's how to cope.

InformationWeek Green - Dec. 5, 2012
InformationWeek Green
Download the entire December 2012 InformationWeek special issue on semantic databases, distributed in an all-digital format as part of our Green Initiative

(Registration required.)

It's a good time to be a big data and analytics expert -- 18% of big data-focused companies in our InformationWeek 2012 State of IT Staffing Survey want to increase staff in this area by more than 30% in the next two years, but 53% say it'll be hard to find people with the required skills.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

People with experience in both areas are "going for crazy prices," says Brian Courtney, general manager of industrial data intelligence at GE Intelligent Platforms. "We have a lot of analytics talent on staff already, but getting high-end analysts with big data experience is that much harder."

The genealogy site Ancestry.com is another big data shop looking to hire. It recruits from the likes of Google, Yahoo and Microsoft to find people with experience running Hadoop clusters, designing high-scale search technologies and analyzing big data clickstreams. Insurance company UnitedHealth Group, headquartered just outside of Minneapolis, also needs more analytics and data management professionals. The health insurer hasn't experimented with Hadoop or NoSQL databases yet, but it's an early implementer of SAS High-Performance Analytics running on EMC's Greenplum Data Computing Appliance. It's working with in-database text mining to prepare for a future in which it will have to analyze text-rich electronic medical records for patient care trends.

With companies as diverse as industrial giants, Internet businesses and insurance companies all competing for the same scarce people, it's no wonder there's a talent gap. It's also clear that there's no single prescription for filling the positions available. Among respondents, 33% say they'll "do a mix of retraining and hiring/contracting." The second-largest group, 28%, will "mostly retrain staff and hire/contract a few people." The third-most-popular choice is to "hire or contract to fill needs," cited by 16%. The smallest group, 11%, will "retrain staff we already have."

We hate to dash the hopes of those counting on a lot of external hiring, but it's unlikely you'll fill the talent gap with recent graduates and people lured away from other companies. The good news? It's a good bet you won't have to beg existing employees -- particularly younger employees -- to line up for training opportunities. If you've attended a conference on Hadoop or NoSQL in the last year, you've undoubtedly seen the throngs of 20- and 30-something data geeks (and some 40- and 50-something geeks) packing into the keynotes and seminars.

Few companies are crawling with big data experts, but if you work for a large or sophisticated company, there's a good chance there are analytics experts on staff. They're often found in the research and development or finance departments, and some companies are pushing these groups to share the expertise.

If you're set on new hiring, consider a recent grad. With the rise of Internet commerce early in the last decade, followed by the publication of best-selling business books like Competing On Analytics and The Numerati, interest in big data analytics started taking off in the academic world about five years ago. The stock-in-trade at schools of mathematics, computer science and business was typically degrees in statistics, operations research, computer science and management science, respectively. But interest in big data has sparked new degree programs in analytics, machine learning and data science.

When big new technology waves come along, as we've seen in the last five years with big data analytics, it takes about 10 years to train the next generation on the new skills that are needed, according to Jim Spohrer, IBM's director of global university programs. "We're in one of those 10-year cycles right now," Spohrer says. If the 10-year pattern holds for big data analytics, a new wave of graduates is just starting to emerge and will reach a steady stream by 2018. Top schools include North Carolina State University, which has a well-known one-year Master of Analytics program that had extensive support from analytics vendor SAS. Others on Spohrer's list include the University of Ottawa, Northwestern University, DePaul University and the University of Connecticut. The latest addition to the list of schools supported by SAS is Louisiana State University, which just launched a 10-month degree program patterned after the one at North Carolina State.

What makes an analytics degree different from, say, a degree in statistics or computer science? At LSU, an analytics degree combines the computer science topics of data management and business intelligence with training in statistics, predictive analytics and operations research. There's also a focus on areas such as fraud detection, risk management, text mining and process improvement.

If poaching experienced employees from other IT shops is more your style, realize that it's not just a matter of offering more money. Top talent wants to know they'll be working with the latest technologies, have access to training and can collaborate with like-minded colleagues. The best big data analytics experts have a mix of business and data acumen. Keep all that in mind in your quest to hire or be hired.



Related Links

Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.