INTERACTIVE SESSION: TECHNOLOGY BIG DATA, BIG REWARDS Today's companies are dealing with an avalanche of records, and 33 billion public records. The system's data from social media, search, and sensors as well as scatch capabilities allow the NYPD to quickly obtain from traditional sources. In 2012, the amount of digital data from any of these data sources Information on information generated is expected to reach 988 exa- criminals, such as a suspect's photo with details of bytes, which is the equivalent to a stack of books from past offenses or addresses with maps can be visual the sun to the planet Pluto and back. Making sense of ized in seconds on a video wall or instantly relayed 'big data has become one of the primary challenges to offices at a crime scene, for corporations of all shapes and sizes, but it also rep Other organizations are using the data to go resents new opportunities. How are companies cur groen, or in the case of Vestas, to go even greener rently taking advantage of big data opportunities? Headquartered in Denmark, Vestas is the world's The British Library had to adapt to handle big data. largest wind energy company, with over 43,000 Every year visitors to the British Library Web site wind turbines across 66 countries Location data are perform over 6 billion searches, and the library is also important to Vestas so that it can accurately place its responsible for preserving British Web sites that no turbines for optimal wind power generation areas longer exist but need to be preserved for historical without enough wind will not generate the necessary purposes, such as the Web sites for past politicians. power, but areas with too much wind may damage Traditional data management methods proved inade the turbines Vestas relies on location-based data to quate to archive millions of these web pages and leg. determine the best spots to install their turbines acy analytics tools couldn't extract useful knowledge To gather data on prospective turbine locations, from such quantities of data. So the British Library Vestas's wind library combines data from global partnered with IBM to implement a big data solution weather systems along with data from existing to these challenges. IBM BigSheets is an insight turbines. The company's previous wind library engine that helps extract, annotate, and visually and provided information in a grid pattern, with each Tyze vast amounts of unstructured Web data, deliver erid measuring 273 27 kilometers (17 x 17 miles) ing the results via a web browser. For example, users Vestas engineers were able to bring the resolution can see search results in a pinchart. IBM BlaSheets down to about 10 x 10 meters 32x32 feet) to estab is built atop the Hadoop framework, so it can process lish the act wind flow pattern at a particular loca- large amounts of data quickly and efficiently tion. To further increase the accuracy of its turbine State and federal law enforcement agencies are placement models Vestas needed to shrink the end analyzing big data to discover hidden patterns in area even more and this required 10 times as much criminal activity such as correlations between time, data as the previous system and a more powerful opportunity, and organizations, or non-obvious data management platform relationships (see Chapter 4) between individuals The company implemented a solution consisting and crinal organizations that would be difficult to of IHM InfoSphere Biainsights software running on uncover in smaller data sets. Criminals and criminal a high-performance TEM System xDatale server organizations are increasingly using the Internet to (InfoSphere Ricinsights is a set of software tools for coordinate and perpetrate their crimes. New tools big data analysis and visualization and is powered by allow agencies to analyze data from a wide array of Apache Hadoop) Using these technologies Vestes in sources and apply analytics to predict future crime creased the size of its wind library and is able manage patterns. This means that law enforcement can and analyze location and weather data with models become more proactive in its efforts to fight crime that are much more powerful and precise and stop it before it occurs Vestas's wind library currently stores 25 petabytes In New York City, the Real Time Crime Center of data and includes approximately 178 parameters data warehouse contains millions of data points on such as barometric pressure humidity, wind dinec city crime and criminals. 18M and the New York City tion, temperature, wind velocity, and other company Police Department (NYPD) worked together to create Historical data. Vestas plans to add global deforesta the warehouse, which contains data on over 120 mil tion metrics satellite images, geospatial data and lion criminal complaints, 31 million national crime dation phases of the moon and tides 262 Part Two Information Technology Infrastructure The company can now reduce the resolution of its wind data grids by nearly 90 percent, down to a 3x3 kilometer area (about 1.8 x 1.8 miles). This capability enables Vestas to forecast optimal turbine placement in 15 minutes instead of three weeks, saving a month of development time for a turbine site and enabling Vestas customers to achieve a retum on investment much more quickly Companies are also using big data solutions to analyze consumer sentiment For example, car rental giant Herta 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 werccurring 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 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 Some Samuel Omegadt Data Unlock business Value Haseline, January 2012 Paul Sarth, Mani Dua What very CO Need to Know CO, January 12, 2012 M Corporation, West Timing Climate into Capital with the Data 2011: TIM Corporation Extending and enhancing twnforce ment capabilitishow Dutas Giving Hentai and eth Library and Start Thani Up to Archive the Weh 2010 CASE STUDY QUESTIONS 1. Describe the kinds of big data collected by the organizations described in this case, 2. List and describe the business intelligence technologies described in this case. 3. Why did the companies described in this case need to maintain and analyze big data? What business benefits did they obtain? 4. Identify three decisions that were improved by using big data 5. What kinds of organizations are most likely to need big data management and analytical tools? Why