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Chapter 6: Foundations of business intelligence: Databases and information Management 223 well on a small scale, but it slowed to a crawl when eventual sales from trade shows and conferences. Many employees tried to access it at once. Very few Switching to Twitter mentions as the key metric to of the company’s 13,000 IT specialists had the exper- measure changes the sales department’s focus. The tise to troubleshoot this problem. David Gleason, department pours its energy and resources into the bank’s chief data officer at the time, said he monitoring website clicks and social media traffic, liked Hadoop but felt it still wasn’t ready for prime which produce many unqualified leads that never time. According to Gartner Inc.

Research director lead to sales. For information management Neil Heudecker, technology originally built to index the web may not Although big data is very good at detecting be sufficient for corporate big-data tasks. Correlations, especially subtle correlations that an analysis of smaller data sets might miss, big data It often takes a lot of work for a company to com- analysis doesn’t necessarily show causation or which bine data stored in legacy systems with data stored correlations are meaningful. For example, examin- in Hadoop. Although Hadoop can be much faster ing big data might show that from 2006 to 2011, the than traditional databases for some tasks, it often United States murder rate was highly correlated with isn’t fast enough to respond to queries immediately the market share of Internet Explorer because both or to process incoming data in real time (such as declined sharply. Nevertheless, that doesn’t mean using smartphone location data to generate just-in- there is any meaningful connection between the two time offers).

Hadoop vendors are responding with Several years ago, Google developed what improvements and enhancements. For example, it thought was a leading-edge algorithm using Hortonworks produced a tool that lets other applica- data it collected from web searches to determine tions run on top of Hadoop. Other companies are exactly how many people had influenza and how offering tools as Hadoop substitutes. Databricks the disease was spreading. It tried to calculate the developed Spark open-source software that is more number of people with flu in the United States adept than Hadoop at handling real-time data, and by relating people’s location to flu-related search the Google spinoff Metanautix is trying to supplant queries on Google. The service has consistently Hadoop entirely.

Overestimated flu rates when compared to conven- tional data collected afterward by the U.S. Centers It is difficult to find enough technical IT special- for Disease Control (CDC). According to Google ists with expertise in big-data analytical tools, Flu Trends, nearly 11 percent of the U.S. Popula- including Hive, Pig, Cassandra, MongoDB, or tion was supposed to have had influenza at the flu Hadoop. On top of that, many business managers season’s peak in mid-January 2013. However, an lack numerical and statistical skills required for article in the science journal Nature stated that finding, manipulating, managing, and interpreting Google’s results were twice the actual number the data.

Centers for Disease Control and Prevention estimated, which had 6 percent of the population Even with big-data expertise, data analysts need coming down with the disease. Why did this hap- some business knowledge of the problem they are pen? Several scientists suggested that widespread trying to solve with big data.

For example, if a phar- media coverage of that year’s severe flu season in maceutical company monitoring point-of-sale data the United States, which was further amplified by in real time sees a spike in aspirin sales in January, social media coverage, tricked Google. Google’s it might think that the flu season is intensifying. Algorithm only looked at numbers, not the context However, before pouring sales resources into a big of the search results.

Campaign and increasing flu medication produc- tion, the company would do well to compare sales Big data can also provide a distorted picture of the patterns to past years. People might also be buying problem. Boston’s Street Bump app uses a smart- aspirin to nurse their hangovers following New phone’s accelerometer to detect potholes without Year’s Eve parties. In other words, analysts need to the need for city workers to patrol the streets. Users know the business and the right questions to ask of of this mobile app collect road condition data while the data. They drive and automatically provide city govern- ment with real-time information to fix problems and Just because something can be measured plan long-term investments.

However, what Street doesn’t mean it should be measured. Suppose, for Bump actually produces is a map of potholes that instance, that a large company wants to measure favors young, affluent areas where more people own its website traffic in relation to the number of smartphones.

The capability to record every road mentions on Twitter. It builds a digital dashboard bump or pothole from every enabled phone is not to display the results continuously.

In the past, the company had generated most of its sales leads and 224 Part ii: information technology infrastructure the same as recording every pothole. Data contain Sources: Samuel Greengard, “Why Some Big Data Efforts Come Up Short,” systematic biases, and it takes careful thought to CIO Insight, February 12, 2015; David Court, “Getting Big Impact from spot and correct for those biases. Big Data,” McKinsey Quarterly 1, 2015; Elizabeth Dwoskin, “The Joys and Hype of Software Called Hadoop,” Wall Street Journal, December 16, 2014; And let’s not forget that big data poses some Tim Harford, “Big Data: Are We Making a Big Mistake?” Financial Times challenges to information security and privacy. Magazine, March 28, 2014; Laura Kolodny, “How Consumers Can Use Big As Chapter 4 pointed out, companies are now Data,” Wall Street Journal, March 23, 2014; Joseph Stromberg,“Why Google aggressively collecting and mining massive data Flu Trends Can’t Track the Flu (Yet),” smithsonianmag.com, March 13, 2014; sets on people’s shopping habits, incomes, hobbies, Gary Marcus and Ernest Davis, “Eight (No, Nine!) Problems with Big Data,” residences, and (through mobile devices) move- New York Times, April 6, 2014; Thomas H.

Davenport, “Big Data at Work,” ments from place to place. They are using such big Harvard Business School Publishing, 2014; Samuel Greengard, “Companies data to discover new facts about people, to classify Grapple with Big Data Challenges,” Baseline, October 29, 2013; Shira Ovide, them based on subtle patterns, to flag them as risks “Big Data, Big Blunders,” Wall Street Journal, March 11, 2013; and John (for example, loan default risks or health risks), to Jordan, “The Risks of Big Data for Companies,” Wall Street Journal, October predict their behavior, and to manipulate them for 20, 2013. Maximum profit. Case study Questions When you combine someone’s personal informa- 6-13 What business benefits did the companies tion with pieces of data from many sources, you can infer new facts about that person (such as the and services described in this case achieve by fact that they are showing early signs of Parkinson’s analyzing and using big data?

Disease, or are unconsciously drawn toward products 6-14 Identify two decisions at the organizations that are colored blue or green). If asked, most people described in this case that were improved by might not want to disclose such information, but using big data and two decisions that big data they might not even know such information about did not improve.

Privacy experts worry that people will 6-15 List and describe the limitations to using big be tagged and suffer adverse consequences without data. Due process, denied the ability to fight back, or even 6-16 Should all organizations try to analyze know that they have been discriminated against. Why or why not?

What people, organization, and technology issues should be addressed before a company decides to work with big data? MyMisLab Go to the Assignments section of your MyLab to complete these writing exercises. 6-17 Define web mining and describe the three ways that web mining looks for patterns in data. 6-18 Discuss how the following facilitate the management of big data: Hadoop, in-memory computing, analytic platforms. Chapter 6 references Aiken, Peter, Mark Gillenson, Xihui Zhang, and David Rafner. “Data Management and Data Ad- ministration: Assessing 25 Years of Practice.” Journal of Database Management (July–September 2011).

Barth, Paul S. “Managing Big Data: What Every CIO Needs to Know,” CIO Insight (January 12, 2012). Barton, Dominic, and David Court. “Making Advanced Analytics Work for You,” Harvard Business Review (October 2012).

“Big Data Fatigue?” MIT Sloan Management Review (June 23, 2014). Beath, Cynthia, Irma Becerra-Fernandez, Jeanne Ross, and James Short. “Finding Value in the Information Explosion.” MIT Sloan Management Review 53, No. 4 (Summer 2012). Bughin, Jacques, John Livingston, and Sam Marwaha. “Seizing the Potential for Big Data.” McKin- sey Quarterly (October 2011).

Chapter 6: Foundations of business intelligence: Databases and information Management 225 Court, David. “Getting Big Impact from Big Data.” McKinsey Quarterly 1 (2015). Davenport, Thomas H., and D.

“Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review (October 2012). Davenport, Thomas H. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. (Boston: Harvard Business Press, 2014.) Eckerson, Wayne W. “Analytics in the Era of Big Data: Exploring a Vast New Ecosystem,” TechTarget (2012). “Data Quality and the Bottom Line.” Data Warehousing Institute (2002).

Greengard, Samuel. “Big Data Unlocks Business Value,” Baseline (January 2012). Hayashi, Alden M. “Thriving in a Big Data World,” MIT Sloan Management Review (Winter 2014). Henschen, Doug. “MetLife Uses NoSQL for Customer Service Breakthrough,” Information Week (May 13, 2013). Hoffer, Jeffrey A., Ramesh Venkataraman, and Heikki Toppi.

Modern Database Management, 12th ed. (Upper Saddle River, NJ: Prentice Hall, 2015.) Jordan, John. “The Risks of Big Data for Companies,” Wall Street Journal (October 20, 2013). Kajepeeta, Sreedhar. “How Hadoop Tames Enterprises’ Big Data.” Information Week (February 2012).

Kroenke, David M., and David Auer. Database Processing: Fundamentals, Design, and Implementation, 14th ed. (Upper Saddle River, NJ: Prentic Hall, 2016.) Lee, Yang W., and Diane M. “Knowing-Why about Data Processes and Data Quality.” Jour- nal of Management Information Systems 20, No.

3 (Winter 2004). Loveman, Gary. “Diamonds in the Datamine,” Harvard Business Review (May 2003). Marcus, Gary, and Ernest Davis. “Eight (No, Nine!) Problems with Big Data.” New York Times (April 6, 2014). Martens, David, and Foster Provost.

“Explaining Data-Driven Document Classifications,” MIS Quarterly 38, No. 1 (March 2014). McAfee, Andrew, and Erik Brynjolfsson. “Big Data: The Management Revolution.” Harvard Business Review (October 2012). McKinsey Global Institute. “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” McKinsey & Company (2011).

Merrill, Douglas. “Beware Big Data’s Easy Answers,” Harvard Business Review (August 2014). Morrison, Todd, and Mark Fontecchio, “In-memory Technology Pushes Analytics Boundaries, Boosts BI Speeds,” SearchBusinessAnalytics.techtarget.com, accessed May 17, 2013. Morrow, Rich. “Apache Hadoop: The Swiss Army Knife of IT,” Global Knowledge (2013).

Mulani, Narendra. “In-Memory Technology: Keeping Pace with Your Data,” Information Management (February 27, 2013). O’Keefe, Kate.

“Real Prize in Caesars Fight: Data on Players.” Wall Street Journal (March 19, 2015). Redman, Thomas. Data Driven: Profiting from Your Most Important Business Asset. Boston: (Boston: Harvard Business Press, 2008.) Redman, Thomas C. “Data’s Credibility Problem,” Harvard Business Review (December 2013). Rosenbush, Steven, and Michael Totty.

“How Big Data Is Transforming Business,” Wall Street Jour- nal (March 10, 2013). Ross, Jeanne W., Cynthia M. Beath, and Anne Quaadgras.

“You May Not Need Big Data After All,” Harvard Business Review (December 2013). Wallace, David J. “How Caesar’s Entertainment Sustains a Data-Driven Culture,” DataInformed (December 14, 2012).

Telecommunications, the 7C h a p t e r internet, and Wireless technology Learning Objectives after reading this chapter, you will be able to answer the following questions: 7-i What are the principal components of telecommunications networks and key networking technologies? 7-2 What are the different types of networks?

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7-3 How do the internet and internet technology work and how do they support communication and e-business? 7-4 What are the principal technologies and standards for wireless networking, communication, and internet access? Chapter Cases Video Cases Wireless Technology Makes Dundee Case 1: Telepresence Moves out of the Precious Metals Good as Gold Boardroom and into the Field The Battle over Net Neutrality Case 2: Virtual Collaboration with Monitoring Employees on Networks: IBM Sametime Unethical or Good Business? Google, Apple, and Facebook Battle for Your Internet Experience 226 Wireless Technology makes DunDee Precious meTals gooD as golD Dundee Precious Metals (DPM) is a Canadian-based, international mining company engaged in the acquisition, exploration, development and mining, and processing of precious metal properties. One of the company’s principal assets is the Chelopech copper and gold mine east of Sofia, Bulgaria; the company also has a gold mine in southern Armenia and a smelter in Namibia.

The price of gold and other metals has fluctuated wildly, and Dundee was looking for a way to offset lower gold prices by making its mining operations more efficient. However, mines are very complex operations, and there are spe- cial challenges with communicating and coordinating work underground.

Management decided to implement an underground wireless Wi-Fi network that allows electronic devices to exchange data wirelessly at the Chelopech mine to monitor the location of equipment, people, and ore throughout the mine’s tun- nels and facilities. The company deployed several hundred Cisco Systems Inc. High-speed wireless access points (in waterproof, dustproof, and crush-resistant enclosures), extended-range antennas, communications boxes with industrial switches connected to 90 kilometers of fiber optic lines that snake through the mine, emergency boxes on walls for Linksys Voice over Internet Protocol (VoIP) phones, protected vehicle antennas that can withstand being knocked against a mine ceiling, and custom walkie-talkie software. Dundee was able to get access points that normally have a range of 200 meters to work at a range of 600 to 800 meters in a straight line or 400 to 600 meters around a curve.

Another part of the solution was to use AeroScout Wi-Fi radio frequency identification (RFID) technology to track workers, equipment, and vehicles. About 1,000 AeroScout Wi-Fi RFID tags are worn by miners or mounted on vehicles and equipment, transmitting data about vehicle rock loads and © TTstudio/Shutterstock 227 228 Part ii: information technology infrastructure mechanical status, miner locations, and the status of doors and ventilation fans over the mine’s Wi-Fi network. AeroScout’s Mobile View software can display a real-time visual representation of the location of people and items. The software can determine where loads came from, where rock should be sent, and where empty vehicles should go next.

Data about any mishap or slowdown, such as a truck that made an unsched- uled stop or a miner who is behind schedule, are transmitted to Dundee’s surface crew so that appropriate action can be taken. The Mobile View interface is easy to use and provides a variety of reports and rules-based alerts. By using this wireless technology to track the location of equip- ment and workers underground, Dundee has been able to decrease equipment downtime and use resources more efficiently. Dundee also uses the data from the underground wireless network for its Dassault Systemes’ Geovia mine management software and IBM mobile planning software.

Before implementing AeroScout, Dundee kept track of workers by noting who had turned in their cap lamps at the end of their shift. AeroScout has automated this pro- cess, enabling staff in the control room to determine the location of miners quickly.

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It is also essential for workers driving equipment underground to be able to com- municate closely with the mine’s control room. In the past, workers used a radio checkpoint system to relay their location. The new wireless system enables control room staff workers actually to see the location of machinery so they can direct traffic more effectively, quickly identify problems, and respond more rapidly to emergencies. Thanks to wireless technology, Dundee has been able to reduce costs and increase productivity while improving the safety of its workers. Communication costs have dropped 20 percent. According to Dundee CEO Rick Howes, the $10 million project, along with new crushing and conveyor systems, helped lower production costs to $40 a ton from $60.

In 2013, Chelopech ore production topped two million tons, a 12 percent increase over the previous year. Sources: Clint Boulton, “Mining Sensor Data to Run a Better Gold Mine,” Wall Street Journal, February 17, 2015, and “Tags to Riches: Mining Company Tracks Production with Sensors,” Wall Street Journal, February 18, 2015, www.dundeeprecious.com, accessed April 29, 2015; Eric Reguly, “Dundee’s Real-Time Data Innovations Are as Good as Gold,” The Globe and Mail, December 1, 2013; and Howard Solomon, “How a Canadian Mining Company Put a Wi-Fi Network Underground,” IT World Canada, December 3, 2013. The experience of Dundee Precious Metals illustrates some of the powerful capabili- ties and opportunities contemporary networking technology provides. The company uses wireless networking, RFID technology, and AeroScout MobileView software to automate tracking of workers, equipment, and ore as they move through its Chelopech underground mine. The chapter-opening diagram calls attention to important points this case and this chapter raise. The Dundee Precious Metals production environment in its Chelopech mine is difficult to monitor because it is underground yet requires intensive oversight and coordination to make sure that people, materials, and equipment are available when and where they are needed underground and that work is flowing smoothly. Tracking components manually or using older radio identification methods was slow, cumbersome, and error-prone.

Dundee was also under pressure to cut costs because the price of gold had dropped and precious metals typically have wild price fluctuations. Management decided that wireless Wi-Fi technology and RFID tagging provided a solution and arranged for the deployment of a wireless Wi-Fi network throughout the entire underground Chelopech production facility. The network made it much easier to track and supervise mining activities from above ground.

Dundee Precious Metals had to redesign some aspects of its production and other work processes and train employees in the new system to take advantage of the new technology. Here are some questions to think about: Why did wireless technology play such a key role in this solution? Describe how the new system changed the production pro- cess at the Chelopech mine.

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