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nalysts have coined the term Big Data to refer to the vast and constantly increas-ing amounts of data that modern organizations need to capture, store, process, and analyze. Managing Big Data represents a very real problem that every business faces.The following case provides a variety of examples of companies that are utilizing Big Data in creative and profi table ways. The fundamental concept that underlies all of these cases is that Big Data will continue to get “bigger,” so organizations will have to devise ever-more innovative solutions to manage these data.
Human Resources. Employee benefi ts, particularly healthcare, represent a major busi-ness expense. Consequently, some companies have turned to Big Data to better manage these benefi ts. Caesars Entertainment (www.caesars.com), for example, analyzes health-insurance claim data for its 65,000 employees and their covered family members. Managers can track thousands of variables that indicate how employees use medical services, such as the number of emergency room visits and whether employees choose a generic or brand name drug.
For instance, data revealed that too many employees with medical emergencies were being treated at hospital emergency rooms rather than at less-expensive urgent-care facilities. The company launched a campaign to remind employees of the high cost of emergency room vis-its, and they provided a list of alternative facilities. Subsequently, 10,000 emergencies shifted to less-expensive alternatives, for a total savings of $4.5 million.
Big Data is also having an impact on hiring. An example is Catalyst IT Services (www .catalystitservices.com), a technology outsourcing company that hires teams for program-ming jobs. In 2013, the company planned to screen more than 10,000 candidates. Not only is traditional recruiting too slow, but too often the hiring managers subjectively choose can-didates who are not the best fi t for the job. Catalyst addresses this problem by requiring candidates fi ll out an online assessment. It then uses the assessment to collect thousands of data points about each candidate. In fact, the company collects more data based on how candidates answer than on what they answer.
For example, the assessment might give a problem requiring calculus to an applicant who is not expected to know the subject. How the candidate reacts—laboring over an answer, answer-ing quickly and then returnanswer-ing later, or skippanswer-ing the problem entirely—provides insight into how that candidate might deal with challenges that he or she will encounter on the job. That is,
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someone who labors over a diffi cult question might be effective in an assignment that requires a methodical approach to problem solving, while whereas an applicant who takes a more aggressive approach might perform better in a different job setting.
The benefi t of this big-data approach is that it recognizes that people bring different skills to the table and that there is no one-size-fi ts-all person for a job. Analyzing millions of data points can reveal which attributes candidates bring to specifi c situations.
As one measure of success, employee turnover at Catalyst averages about 15 percent per year, compared with more than 30 percent for its U.S. competitors and more than 20 percent for similar companies overseas.
Product Development. Big Data can help capture customer preferences and put that information to work in designing new products. In this area, both online companies and traditional companies are using Big Data to achieve competitive advantage.
Physical manufacturers are using Big Data to measure customer interest. For example, as Ford Motor Company (www.ford.com) was designing the fi rst subcompact model on its new global platform—a common set of components that Ford would incorporate into its cars and trucks around the world—the company had to decide which features that were common in one region should be made available in all regions. One feature the company considered was a “three blink” turn indicator that had been available on its European cars for years. Unlike the turn signals on its U.S. vehicles, this indicator fl ashes three times at the driver’s touch and then automatically shuts off.
Ford decided that conducting a full-scale market research test on this blinker would be too costly and time consuming. Instead, it examined auto-enthusiast Web sites and owner forums to discover what drivers were saying about turn indicators. Using text-mining algo-rithms, researchers culled more than 10,000 mentions and then summarized the most relevant comments.
The results? Ford introduced the three-blink indicator on the new Ford Fiesta in 2010, and by 2013 it was available on most Ford products. Although some Ford owners complained online that they have had trouble getting used to the new turn indicator, many others defended it. Ford managers note that the use of text-mining algorithms was critical in this effort because they provided the company with a complete picture that would not have been available using traditional market research.
Operations. For years, companies have been using information technology to make their operations more effi cient. They can now use Big Data to capture much more information from a wealth of new sources.
Consider United Parcel Service (UPS). The company has long relied on data to improve its operations. Specifi cally, it uses sensors in its delivery vehicles that can, among other things, capture the truck’s speed and location, the number of times it is placed in reverse, and whether the driver’s seat belt is buckled. These data are uploaded at the end of each day to a UPS data center, where they are analyzed overnight. By combining GPS information and data from sensors installed on more than 46,000 vehicles, UPS in 2012 reduced fuel consumption by 8.4 million gallons, and it cut 85 million miles off its routes.
Marketing. Marketing managers have long used data to better understand their customers and to target their marketing efforts more directly. Today, Big Data enables marketers to craft much more personalized messages.
Like many hoteliers, United Kingdom’s InterContinental Hotels Group (IHG; www.ihg .com) has gathered details about the 71 million members of its Priority Club rewards program, such as income levels and whether members prefer family-style or business-traveler accommo-dations. The company then consolidated all of this information into a single data warehouse that extracts information from social media Web sites and processes queries very quickly. A data warehouse is a repository of historical data that are organized by subject to support deci-sion makers in the organization. Using its data warehouse and analytics software, the hotelier launched a new marketing campaign in January 2013. Where previous marketing campaigns generated, on average, between 7 and 15 customized marketing messages, the new campaign
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CASE generated more than 1,500. IHG rolled out these messages in stages to an initial core of 12
customer groups, each of which is defi ned by 4,000 attributes. One group, for instance, tends to stay on weekends, redeem reward points for gift cards, and register through IHG market-ing partners. Utilizmarket-ing this information, IHG sent these customers a marketmarket-ing message that alerted them to local weekend events.
The campaign proved to be highly successful. It generated a 35 percent higher rate of cus-tomer conversions, or acceptances, than previous, similar campaigns.
Sources: Compiled from S. Sikular, “Gartner’s Big Data Defi nition Consists of Three Parts, Not to Be Confused with Three
‘V’s,” Forbes, March 27, 2013; G. Fowler, “Data, Data Everywhere,” The Wall Street Journal, March 13, 2013; G. Press, “What’s To Be Done About Big Data?” Forbes, March 11, 2013; S. Rosenbush and M. Totty, “How Big Data Is Changing the Whole Equation for Business,” The Wall Street Journal, March 11, 2013; D. Clark, “How Big Data Is Transforming the Hunt for Talent,”
Forbes, March 8, 2013; G. Satell, “The Limits of Big Data Marketing,” Forbes, March 6, 2013; V. Mayer-Schonberger and K. Cukier, “Big Data: A Revolution That Will Transform How We Live, Work, and Think,” Eamon Dolan/Houghton Miffl in Harcourt, March 5, 2013; “Big Data: What’s Your Plan?” McKinsey & Company Insights, March, 2013; D. Henschen, “Big Data Revolution Will Be Led by Revolutionaries,” InformationWeek, December 12, 2012; “Integrate for Insight,” Oracle White Paper, October 27, 2012; “Big Data Now,” O’Reilly Media, October, 2012; S. Greengard, “Big Data Challenges Organizations,”
Baseline Magazine, June 13, 2012; S. Lohr, “The Age of Big Data,” The New York Times, February 11, 2012; “Big Data: Lessons From the Leaders,” The Economist (Economist Intelligence Unit), 2012; www.the-bigdatainstitute.com, accessed April 17, 2013.
What We Learned from This Case
Information technologies and systems support organizations in managing—that is, acquiring, organizing, storing, accessing, analyzing, and interpreting—data. As you noted in Chapter 1, when these data are managed properly, they become information and then knowledge. Infor-mation and knowledge are invaluable organizational resources that can provide a competitive advantage.
So, just how important are data and data management to organizations? From confi dential customer information, to intellectual property, to fi nancial transactions, to social media posts, organizations possess massive amounts of data that are critical to organizational success and that they need to manage. Managing these data, however, comes at a huge cost. According to Symantec’s (www.symantec.com) State of Information Survey, digital information annually costs organizations worldwide $1.1 trillion, and it also makes up roughly half of an organiza-tion’s total value. Large organizations spend an average of $38 million annually to maintain and utilize data, and small-to-medium-sized businesses spend $332,000.
This chapter will examine the processes data fi rst into information and then into knowledge.
Managing data is critically important in large organizations. However, it is equally important to small organizations, as you see in IT’s About Business 5.1.
IT’s about [small] business
Databases come in all shapes and sizes. Essentially, a database is a group of logically related fi les that store data and the associations among them. As you will see in this chapter, a database consists of attributes, entities, tables, and relationships. The purpose of a database can differ greatly depending on the nature of the business.
Take, for example, Dennis Rollins, the owner of a small car lot in Bowdon, Georgia. Dennis needed an effective way to manage the data pertaining to his car lot. Achieving a solid online presence can be diffi cult for small used car dealers because there are so many makes and models of cars to sell and so many online outlets through which to advertise. Had Dennis opted to manage his data himself, he would have needed at least one database to manage
his inventory and at least one other database to allow his custom-ers to view product information. He would also have to hire a full-time employee to oversee Internet sales. Adding two databases and a full-time employee was far beyond Dennis’ capacity, so he sought an easier solution.
That solution came in the form of Dealer Car Search (http://
dealercarsearch.com), a company that specializes in creating Web sites for car dealers. Dealer Car Search provides products for small businesses, dealers, and dealer chains. What ultimately makes the company so successful, however, is its database. This database provides the data-entry capabilities, analysis capabili-ties, reporting, and search features.
5.1 Rollins Automotive
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Few business professionals are comfortable making or justifying business decisions that are not based on solid information. This is especially true today, when modern information sys-tems make access to that information quick and easy. For example, we have technology that formats data in a way that managers and analysts can easily understand. Consequently, these professionals can access these data themselves and analyze them according to their needs, using a variety of tools. The result is useful information. Executives can then apply their expe-rience to use this information to address a business problem, thereby producing knowledge.
Knowledge management, enabled by information technology, captures and stores knowledge in forms that all organizational employees can access and apply, thereby creating the fl exible, powerful “learning organization.”
Clearly, data and knowledge management are vital to modern organizations. But, why should you learn about them? The reason is that you will play an important role in the devel-opment of database applications. The structure and content of your organization’s database depends on how users (you) defi ne your business activities. For example, when database devel-opers in the fi rm’s MIS group build a database, they use a tool called entity-relationship (ER) modeling. This tool creates a model of how users view a business activity. When you under-stand how to create and interpret an ER model, then you can evaluate whether the developers have captured your business activity correctly.
Keep in mind that decisions about data last longer, and have a broader impact, than deci-sions about hardware or software. If decideci-sions concerning hardware are wrong, then the equip-ment can be replaced relatively easily. If software decisions turn out to be incorrect, they can be modifi ed, though not always painlessly or inexpensively. Database decisions, in contrast, are much harder to undo. Database design constrains what the organization can do with its data for a long time. Remember that business users will be stuck with a bad database design, while the programmers who created the database will quickly move on to their next projects. This is why it is so important to get database designs right the fi rst time—and you will play a key role in these designs.
Relational databases (discussed in detail later in this chapter) store data in fl at, two-dimensional tables, consisting of rows and columns. When you know how data are stored in these tables, then you know what types of data are available for analysis and decision making. Of course, your familiarity with data warehouses will serve the same purpose. Also, understanding relational databases will help you work with database developers in defi ning a new database or suggesting improvements to an existing one. It is one thing for you to say to a database developer, “I wish I could get this information from the database.” It is quite another thing to say, “If you could add this column of data to Table A and this other column of data to Table B, then I could get this information from the database.” An important note: Don’t be concerned that database developers will be insulted if you provide such detailed instructions.
They actually enjoy responding to specifi c, knowledgeable requests from users!
In addition, you might want to create a small, personal database using a software product such as Microsoft Access. In that case, you will need to be familiar with at least the basics of the product.
Now, when Dennis has a new vehicle to sell, he has to enter his data just once onto his customer page on Dealer Car Search. The data then automatically appear on his Web site (http://rollinsautomotive.com) as well as on other car sites (such as http://autotrader.com). Dealer Car Search also sup-plies Dennis with an inventory management system that pro-vides a view of his inventory along with reports to help him determine his pricing. If Dennis changes a price or updates any other information, the change automatically appears on all of the other sites.
The result? The database turned out to be the one-stop solu-tion that Dennis needed. It provides inventory management,
Internet advertising, mobile apps, performance reporting, lead management, and much more. Simply put, this database appli-cation provides a seamless experience that benefi ts Dealer Car Search, Rollins Automotive, and Rollins’s customers.
Sources: Compiled from http://dealercarsearch.com, http://rollinsautomotive .com, accessed February 28, 2013.
Questions
1. Why is Dealer Car Search’s database largely responsible for its success?
2. Why didn’t Dennis Rollins simply build his own database using Access? Support your answer.
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SECTION 5.1 Managing Data After the data are stored in your organization’s databases, they must be accessible to users in
a form that helps users make decisions. Organizations accomplish this objective by developing data warehouses. You should become familiar with data warehouses because they are invalu-able decision-making tools.
You will also make extensive use of your organization’s knowledge base to perform your job.
For example, when you are assigned a new project, you will likely research your fi rm’s knowledge base to identify factors that contributed to the success (or failure) of previous, similar projects.
You begin this chapter by taking a look at the Big Data phenomenon. You continue by examining the multiple problems involved in managing data and the database approach that organizations use to solve those problems. You will then see how database management sys-tems enable organizations to access and use the data stored in the databases. Next, you study data warehouses and data marts and how to utilize them for decision making. You fi nish the chapter by examining knowledge management.