Case Study; Big Data, Big Rewards

CASE STUDY 2

BIG DATA, BIG REWARDS

 Today’s companies are dealing with an avalanche of data from social media, search, and sensors as well as from traditional sources. In 2012, the amount of digital information generated is expected to reach 988 exabytes, which is the equivalent to a stack of books from the sun to the planet Pluto and back. Making sense of “big data” has become one of the primary challenges for corporations of all shapes and sizes, but it also represents new opportunities. How are companies currently taking advantage of big data opportunities?

The British Library had to adapt to handle big data. Every year visitors to the British Library Web site perform over 6 billion searches, and the library is also responsible for preserving British Web sites that no longer exist but need to be preserved for historical purposes, such as the Web sites for past politicians. Traditional data management methods proved inadequate to archive millions of these Web pages, and legacy analytics tools couldn’t extract useful knowledge from such quantities of data. So the British Library partnered with IBM to implement a big data solution to these challenges. IBM BigSheets is an insight engine that helps extract, annotate, and visually analyze vast amounts of unstructured Web data, delivering the results via a Web browser. For example, users can see search results in a pie chart. IBM BigSheets is built atop the Hadoop framework, so it can process large amounts of data quickly and efficiently.State and federal law enforcement agencies are analyzing big data to discover hidden patterns in criminal activity such as correlations between time, opportunity, and organizations, or non-obvious relationships (see Chapter 4) between individuals and criminal organizations that would be difficult to uncover in smaller data sets. Criminals and criminal organizations are increasingly using the Internet to coordinate and perpetrate their crimes. New tools allow agencies to analyze data from a wide array of sources and apply analytics to predict future crime patterns. This means that law enforcement can become more proactive in its efforts to fight crime and stop it before it occurs.In New York City, the Real Time Crime Center data warehouse contains millions of data points on city crime and criminals. IBM and the New York City Police Department (NYPD) worked together to create the warehouse, which contains data on over 120 million criminal complaints, 31 million national crime records, and 33 billion public records. The system’s search capabilities allow the NYPD to quickly obtain data from any of these data sources. Information on criminals, such as a suspect’s photo with details of past offenses or addresses with maps, can be visualized in seconds on a video wall or instantly relayed to officers at a crime scene.Companies are also using big data solutions to analyze consumer sentiment. For example, car-rental giant Hertz gathers data from Web surveys, e-mails, text messages, Web site traffic patterns, and data generated at all of Hertz’s 8,300 locations in 146 countries. The company now stores all of that data centrally instead of within each branch, reducing time spent processing data and improving company response time to customer feedback and changes in sentiment. For example, by analyzing data generated from multiple sources, Hertz was able to determine that delays were occurring for returns in Philadelphia during specific times of the day. After investigating this anomaly, the company was able to quickly adjust staffing levels at its Philadelphia office during those peak times, ensuring a manager was present to resolve any issues. This enhanced Hertz’s performance and increased customer satisfaction.There are limits to using big data. Swimming in numbers doesn’t necessarily mean that the right information is being collected or that people will make smarter decisions. Last year, a McKinsey Global Institute report cautioned there is a shortage of specialists who can make sense of all the information being generated. Nevertheless, the trend towards big data shows no sign of slowing down; in fact, it’s much more likely that big data is only going to get bigger.

CASE STUDY QUESTIONS

QUESTION 1: 2.5 pts

Why did the companies described in this case need to maintain and analyze big data? Mention two reasons for both of the New York Police Department (NYPD) and Hertz need to maintain and analyze big data? What business benefits did they obtain, mention two only?

QUESTION 2: 1 pt

Identify three decisions that were improved by using big data.

QUESTION 3: 1.5 pts

What kinds of organizations are most likely to need the big data management and analytical tools? Why?

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