Chapter 6- Case study- Big Data, Big Rewards
1. Describe the kinds of big data collected by the organizations described in this case. The organizations described in this case are The British Library, NYPD, Vestas and Hertz. Each of these organizations collects different kinds of data. The British Library has to accommodate about 6 billion searches annually and collects and preserves all websites that are of any historical significance. This also includes websites of past politicians. NYPD collects data on criminals and the criminal complaints and the information collected stores all required information about a criminal such as photo, past offences, address etc. Vestas uses data from global weather systems as well as data from other turbines and location based data including barometric pressure, temperature, wind speed, etc. to increase their wind library which helps them place the turbines in exact locations where the wind power can be harnessed optimally. Other companies like Hertz use big data solutions to analyze customer sentiment. They look for ways to improve their process and make it more efficient by analyzing the data gathered and use it to make smarter decisions that improve business and leave the customers happy. These big data reveal more patterns than smaller data sets and provide insight into many smaller nuances that is otherwise difficult to spot. 2. List and describe the business intelligence technologies described in this case. All three business intelligence technologies are described in this case. a) Management information systems
b) Decision support systems
c) Executive support systems
MIS reports can easily be prepared from the big data and can be presented to management or higher officials as the case might be. It collects, stores and analyses data with intricate details that can be visualized quickly in seconds and instantly related when required. DSS focus on problems that are unique and rapidly changing. IBM’s Bigsheets is an example of how well The British Library has used a technology that helps extract, annotate and visually analyze vast amounts of unstructured web data. It can process large amounts of data quickly and efficiently and make visually appealing pie charts or bar diagrams to make better sense of the data. ESS helps senior management make non-routine decisions. Hertz noticed a pattern in the traffic congestion in Philadelphia using big data analytics and came to a solution that increased customer satisfaction tremendously. The company was able to investigate the traffic anomaly and adjust staffing levels during the peak times and ensure a manager was present to resolve any issues. 3. Why did the companies described in this case need to maintain and analyze big data? What business benefits did they obtain? The companies described in this case needed to maintain and analyze big data as traditional methods of managing data proved to be inadequate and the tools could not extract useful data that could be used for analytics. Law enforcement agencies use big data to analyze and discover hidden patterns in criminal activities and non-obvious relationships that help them identify and get hold of the criminals quickly. In the case of Hertz, using big data helped save time spent processing data and in turn improved the company response time to customer feedback and helped implement changes that will benefit the customer and improve the services of the company. In the case of the British Library, the necessity was to search enormous amounts of unstructured data that legacy analytics programs could not handle so that the users can have the data presented in a format that is easy to understand, can be fetched in seconds and provides them the information they look for. This helps them to provide accurate information in searches so that they can retail their user base. At Vestas, the challenge was to shrink the wind grid pattern to maximize the wind pattern library for a...
Please join StudyMode to read the full document