Image from Defense Advanced Research Projects Agency (Source: Wikimedia Commons)
Ideas for Leaders #176

Developing Talent to Manage Big Data

This is one of our free-to-access content pieces. To gain access to all Ideas for Leaders content please Log In Here or if you are not already a Subscriber then Subscribe Here.

Key Concept

The sudden and prominent appearance of big data in the business world means many organizations need to start thinking about investing in new staff specifically trained in big data analysis. How can HR executives stay ahead and ensure they find and retain the right people for this important role? 

Idea Summary

The type and amount of information collected by organizations today is on a scale never seen before. This explosion in the volume of data received through sources such as social media feeds, customer service databases, etc., has created a new opportunity for businesses to compete by collecting and analysing this so called “big data.” And with this opportunity comes an increased demand for big data analysts; according to a 2012 survey by InformationWeek, 40% of respondents said they planned to increase their staff in big data and analytics in the upcoming year, further estimating that big data staffing will increase by 11% over the next two years.

But its sudden appearance in the marketplace means there is also currently a shortage of such individuals; most existing leaders cannot adequately identify and optimize business applications in big data. With demand for such analysts expected to increase, HR executives may soon find themselves in the difficult position of hiring from a shrinking talent pool.

So how are organizations planning on recruiting and developing big data talent? Some are incorporating questions into the interview process that test candidates’ agility and logic; for example, Google ask questions like, “How many golf balls would fit in a school bus?” or “How many sewer covers are there in Manhattan?” Respondents are not expected to get answers right, but rather their willingness to experiment is at test. Similarly, Capital One and Proctor & Gamble also assess candidates during the recruitment stage.

Other organizations prefer on-the-job training; a growing number of organizations are offering big data training and development through conferences, seminars, online courses, webinars, and certification programs.

Business Application

The decision as to whether to employ new big data staff or train existing employees will differ from organization to organization. But the following four steps will be beneficial for all HR executives to help bridge the big data talent gap:

  1. Educate yourself about big data:  be proficient in big data and familiar with the skills and abilities needed to be successful. Also, strive to understand how big data can be applied to recruiting as well, and become a leader in using it to advance the HR function.
  2. Educate managers and senior leaders about big data: this means not only developing new knowledge and skills, but understanding the real potential of big data. Managers and leaders at all levels must be educated.
  3. Develop creative strategies to recruit and retain big data talent: think outside the box and become more creative in recruiting big data analysts. As there is a shortage of them in the market, retaining this talent may become a challenge. As such, consider revised compensation, incentive, and recognition systems designed to keep them within the organization.
  4. Offer solutions to build big data talent in their organizations: developing an organization-wide big data literacy program, like Proctor & Gamble did, may be worth considering for your organization too. On-the-job training, seminars, self-paced learning programs, etc., can all provide developmental opportunities. In addition, it may also help identify employees who possess an aptitude for, and interest in, big data analysis.
Contact Us

Authors

Institutions

Source

Idea conceived

  • May 2013

Idea posted

  • July 2013

DOI number

10.13007/176

Subject

Real Time Analytics