Do you need big data? Maybe the question is better phrased as: Can you afford not to use big data? The age of big data is here, and to ignore its benefits is to run the risk of missed opportunities.
Organizations using big data are quickly reaping rewards, as a survey of 2,022 managers worldwide indicated recently. In fact, 71 percent of respondents agreed that organizations using big data will gain a “huge competitive advantage.” These managers also saw the need for big data: 58 percent responded that they never, rarely, or only sometimes have enough data to make key business decisions. Furthermore, they’ve witnessed the benefits: 67 percent agreed that big data has helped their organization to innovate. So why did only 28 percent find that their access to useful data significantly increased in a year? According to Amy Braverman, a principal statistician who analyzes NASA’s spacecraft data, the problem is in interpreting the new kinds and volumes of data we are able to collect. “This opportunistic data collection is leading to entirely new kinds of data that aren’t well suited to the existing statistical and data-mining methodologies,” she said. IT and business leaders agree: in a recent survey, “determining how to get value” was identified as the number 1 challenge of big data. With a strong need to combat the high hurdle of usability, how should a company get started using big data? The quick answer seems to be to hire talent. But not just anyone will do. Here are some points to ponder when hiring data professionals:
1. Look for candidates with a strong educational background in analytics/statistics. You want someone who knows more than you do about handling copious amounts of data.
2. The ideal candidates will have specific experience in your industry or a related industry. “When you have all those Ph. D.s in a room, magic doesn’t necessarily happen because they may not have the business capability,” said Andy Rusnak, a senior executive at Ernst & Young.
3. Search for potential candidates from industry leader organizations that are more advanced in big data.
4. Communication skills are a must. Look for a candidate “who can translate Ph.D. to English,” says SAP Chief Data Scientist David Ginsberg. He adds, “Those are the hardest people to find.”
5. Find candidates with a proven record of finding useful information from a mess of data, including data from questionable sources. You want someone who is analytical and discerning.
6. Look for people who can think in 8- to 10-week periods, not just long-term. Most data projects have a short-term focus.
7. Test candidates’ expertise on real problems. Netflix’s Director of Algorithms asks candidates, “You have this data that comes from our users. How can you use it to solve this particular problem?”
1-18. Let’s say you work in a metropolitan city for a large department store chain and your manager puts you in charge of a team to find out whether keeping the store open an hour longer each day would
increase profits. What data might be available to your decision-making process? What data would be important to your decision?
1-19. What kinds of data might we want in OB applications?
1-20. As Braverman notes, one problem with big data is making sense of the information. How might
a better understanding of psychology help you sift through all this data?
with any paper
Sources: M. Taes, “If I Could Have More Data…,” The Wall Street Journal, March 24, 2014, R5;
S. Thurm, “It’s a Whole New Data Game,” The Wall Street Journal, February 10, 2015, R6; and
J. Willhite, “Getting Started in ‘Big Data’,” The Wall Street Journal, February 4, 2014, B7.
Go to mymanagementlab.com for the following Assisted-graded writing questions:
1-21. Now that you’ve read the chapter and Case Incident 1, if you were an Apple manager whose employees were losing their jobs to overseas workers, what would you advise your teams to do in order to find re-employment in their professions? What types of training—basic, technical, interpersonal, problem-solving—would you recommend?
1-22. In relation to Case Incident 2, why do you think it is important to have educated, experienced statisticians on any team that is using big data for decision-making? What might be the consequences of hiring someone with less experience?
1-23. MyManagementLab Only – comprehensive writing assignment for this chapter.