Case 3.1 - Big data big rewards

Download Case 3.1 - Big data big rewards

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  1. 1. G S T 5 0 8 3 INFORMATION SYSTEMS & ELECTRONIC COMMERCE Group Members BIG DATA, BIG REWARDS
  2. 2. Company Background Big data datasets that are too large to be gathered, stored, managed and analyzed by typical database software tools can generate plenty of value for organizations of all sizes and types. Organizations that are able to harness the power of big data can drive both operational efficiency and quality, leading to cost and labor savings and a competitive edge. Leveraging big data can also help companies streamline processes, fighting fraud and reducing errors.
  3. 3. Question 1 : Describe the kinds of big data collected by the organizations described in this case. There are mainly four kinds of big data collected by the organizations described in this case. :- 1. First, IBM Big sheets help the British Library to handle with huge quantities of data and extract the useful knowledge British Library responsible for preserving British Web sites that no longer exist but need to be preserved for historical purpose. IBM BigSheets helps the British Library to process large amounts of data quickly and efficiently 2. Second, State and federal law enforcement agencies are analyzing big data to discover hidden patterns in criminal activity. The Real Time Crime Center data warehouse contains millions of data points on city crime and criminals. State and federal law enforcement agencies are analyzing big data to discover hidden patterns in criminal activity. The Real Time Crime Center data warehouse contains millions of data points on city crime and criminals. IBM and New York City Police Department (NYPD) work together to create the warehouse, which contains data on over 120 million criminal complaints, 31 million criminal crime records and 33 billion public records.
  4. 4. CONTS 3.Third, Vestas implemented a solution consisting of IBM InfoSphere BigInsights software running on a high-performance IBM System x iDataPlex server. Vestas wind library currently stores data on perspective turbine location and global weather system. Vestas implemented a solution consisting of IBM InfoSphere BigInsights software running on a high-performance IBM System x iDataPlex server. 4.Forth, Hertz A car rental Hetrz using big data solution to analyze consumer sentiment from Web surveys, emails, text message, Web site traffic patterns and data generated at all of Hertzs 8300 locations in 146 countries. Hertz was able to reducing time spent processing data and improving company response time to customer feedback and changes in sentiment.
  5. 5. Question 2 : List and describe the business intelligence technologies described in this case. 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. 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 between individuals and criminal organizations that would be difficult to uncover in smaller data sets. The Real Time Crime Center data warehouse contains millions of data points on city crime and criminals. Vestas relies on location-based data to determine the best spots to install their turbines. It implemented a solution consisting of IBM InfoSphere BigInsights software running on a high-performance IBM System x iDataPlex server.
  6. 6. Question 3 : Why did the companies described in this case need to maintain and analyse ? What business benefits did they obtain? The British Library The British Library needed to maintain and analyze big data because traditional data management methods proved inadequate to archive billions of Web pages and legacy analytics tools couldnt extract useful knowledge from such quantities of data. New York Police Department (NYPD) NYPD need to maintain and analyze big data because: Allow the NYPD quickly respond on the criminals occurred. Help NYPD to obtain sources of the suspects, such as suspects photo, past offences or addresses with maps, can be visualized in seconds on a video wall.
  7. 7. Vestas Vestas need to maintain and analyze big data because : Vestas is the worlds largest wind energy company. Location data are important to Vestas so that can accurately place its turbines. Areas without enough wind will not generate the necessary power. Area with too much wind may damage the turbines. Therefore, Vesta relies on location-based data to determine the best spots to install their turbines. Vestas Wind Library currently stores 2.8 petabytes od data. CONTS
  8. 8. Hertz Car rental giant Hertz need to maintain and analyze big data because : Reducing time spent processing data. Improving company response time to customer feedback. Hertz was able to determine that delays were occurring for returns in Philadelphia during specific time of the day. Enhanced Hertzs performance and increased customer satisfaction. CONTS
  9. 9. What business benefits did they obtain? The business benefits for maintaining and analyzing big data are as follows: 1.Competitive advantages 2.Performance Enhancement 3.Increase customer satisfaction 4.Attract more customers and generate more revenue 5.Improved decision making (faster & accurate) 6.Excellence operational 7.Reduced cost and time spent CONTS
  10. 10. Question 4 : Identify three decisions that were improved by using big data. 1. Optimal uses of resources and operational time By using the big data, the companies can optimal uses of their resources to enhance performance. Vestas can forecast optimal turbine placement in 15 minutes instead of three weeks, saving a months of development time for turbine site. 2. Quick and effective decision making Decision making improves and can be quickly and effective by using big data. Visitor of The British Library and NYPD can quickly and effective searches data from the British Library Web sites. NYPD can make a faster decision to gather the suspects detail by using The Real Time Crime Center. 3. Reduce operational cost and other related cost Company quickly makes the right decision and hence will eliminate wrong decision. Example, Hertz was able quickly adjust staffing levels at its Philadelphia office during those peak times; ensuring a manager was present to resolve any issues.
  11. 11. Question 5: What kinds of organizations are most likely to need big data management and analytical tools? Why? Organizations which responsible to store the huge information such as national library, registration department, income tax and so on because these organizations typically be a sources for government and the public. Authorities Organization such a police department, custom, immigration because they need to store a big data about criminals and also public to use for safety of the society. Organization to go green need the big data about the weather and location because the weather and location data are very useful for the companies to accurately make a decision. In this case, Vestas needed the data about location and wind to locate their turbines
  12. 12. THANK YOU