Assignment

List the Problems/Issues

Listen the Solutions Taken By the Company

Evaluate the Solutions/Analysis

List Recommendations

Assignment

List the Problems/Issues

Listen the Solutions Taken By the Company

Evaluate the Solutions/Analysis

Recommendations

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Today's companies are dealing with an avalanche of data from social media, search, and sensors as well as from traditional sources. In 2012, an estimated 988 exabytes of digital information was generated, equivalent to a stack of books from the sun to Pluto and back. Making sense of "big data" has become one of the primary chal lenges for corporations of all shapes and sizes, but it also represents new opportunities. How are companies currently taking advantage of "big data? US state and federal law enforcement agencies are analyzing big data to discover hidden patterns in crim inal activity such as correlations between time, opportu- nity, and organizations, or non-obvious relationships (see Chapter 4) between individuals and criminal organiza tions that would be difficult to uncover in smaller data sets. New tools allow agencies to analyze data from a wide array of sources, including the Internet, and apply analytics to predict future crime patterns. 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 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 capa bilities 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. Other organizations are using the data to go green, or, in the case of Vestas, to go even greener. Headquartered in Denmark, Vestas is the world's largest wind energy company, with over 43,000 wind turbines across 66 countries. Location data are important to Vestas so that it can accurately place its turbines for optimal wind power generation Areas without enough wind will not generate the necessary power, but areas with too much wind may damage the turbines. Vestas relies on location based data to determine the best spots to install their turbines. To gather data on prospective turbine locations Vestar's wind library combines data from global weather systems along with data from existing turbines. The company's previous wind library provided information in a grid pattern, with each grid measuring 27 x 27 kilometers (17x17 miled. Vestas engineers were able to bring the resolution down to about 10x10 meters (32x32 feet) to establish the exact wind flow pattern at a Chapter Foundations of Business Intelligence Databases and information Management 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 Philadelphi 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 Herte's performance, and increased customer satisfaction. There are limits to using big data. A number of companies have rushed to start big data projects without first establishing a business goal for this new informa tion. Swimming in numbers doesn't necessarily mean that the right information is being collected or that people will make smarter decisions particular location. To further increase the accuracy of its turbine placement models, Vestas needed to shrink the grid area even more, and this required to tiesas mach data as the previous system and a more powerful data management platform The company implemented a solution consisting of IBM InfoSpherelliginsights software running on a high- performance IBM System x iDataflex server. (Info- pherefliglesights is a set of software tools for "big data" analysis and visualization and is powered by Apache Hadoop) Using these technologies, Vestas increased the size of its wind library and is able to manage and analyze location and weather data with models that are much more powerful and precise Vestas's wind library currently stores 2.8 petabytes of data and includes approximately 178 parameters, such as barometric pressure, humidity, wind direction, tempera ture, wind velocity, and other company historical data Vestas plans to add global deforestation metrics, satellite images, geospatial data, and data on phases of the moon and tides. The company can now reduce the resolution of its wind data grids by nearly 90 percent, down to a 3x3 kilometer area (about 18x1.8 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 return on investment much more quickly AutoZone uses big data to help it adjost inventory and product prices at some of its 5,000 stores. For example, a customer walking into an AutoZone store in Waco, Texas, might find a deal on Gabriel shocks which that person would not find in most other AutoZone stores The Mulberry, Florida AutoZone store might feature a special on a bug deflector. To target these deals at the local level, the auto parts retailer analyzes information gleaned from a variety of databases, such as the types of cars driven by people living around its retail outlets. Software from NuoDB, which uses a cloud services model, makes it possible to quickly increase the amount of data analyzed without bringing down the system, or changing a line of code Companies are also using big data solutions to analyze consumer sentiment. For example, car-tal giant Hertz gathers data from Web surveys, e-mails, text messages, Web site traffic patterns, and data generated at all of Herta's 8,300 locations in 146 countries. The company now stores all of that data centrally instead of within each branch, reducing time 215 advantage because Sear's cost structure remained one of the highest in its industry. The Sears company has continued to embrace new technology to revive flagging sales online shopping. mobile apps, and an Amazon.com-like marketplace with other vendors for 18 million products, along with heavy in-store promotions. So far, these efforts have not paid off, and sales have declined since the 2005 merger with Kmart. The company posted a loss of $930 million for 2012 Sears Holdings CEO Lou DAmbrosio thinks that even more intensive use of technology and mining of customer data will be the answer. The expectation is that deeper knowledge of customer perferences and buying patterns will make promotions, merchandising, and selling much more effective. Customers will flock to Sears stores because they will be carrying exactly what of the highest in its industry spete procnning data anuoving conhomoaantage become Sean co tine top curth u For asamde by analyvinghts greerend roe maliga sounces, lartex w Scan company has t andakriving sale dclays wcre (xcuting fe nrtarten iPhilakrighia_ during specific times of the day. 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