3. 1. Describe the kinds of big data collected by the organizations described in the case. 2. List and describe the business intelligence technologies described in this case. 3. Why did the company described in this case need to maintain and analyze big data? What business benefits did they obtain?
4. Kind of Big Data Collected Business Intelligent Technology used Business Benefit Preserving website for historical purposes IBM Bigsheets -a cloud application used to perform ad- hoc analytics at web scale on structured and unstructured content -Help extract, annotate and visually analyze vase amount of data and delivering the result via a web browser -Laverages Apache Hadoop Framework and Map Reduce Technology help the organization to handle huge quantities of data quickly and efficiently and extract an useful knowledge Extract useful knowledge from such huge data which made responding quickly and efficient to users search and better services to their user thus increase customer
5. Kind of Big Data Collected Business Intelligent Technology used Business Benefit Hidden patterns in criminal activity (correlation between time, opportunity and organization) or non-obvious relationship between individuals and criminal organizations criminal complaints National crime records Public records Real Time Crime Center (RTCC) Centralized data hub that rapidly mines information from multiple crime databases and disseminates that information to officers in the field Crime Information Warehouse - contain data over 120 million criminal complaints, 31 million national crime record and 33 billion public records Support for more proactive policing tactics by virtue of an ability to see crime trends as they are happening Faster and higher rate of case-closing through more efficient gathering and analysis of crime- related data. Improved overall data integrity and speed of data access to optimize decision-making
6. Kind of Big Data Collected Business Intelligent Technology used Business Benefit Location Data Wind Library stores nearly 2.8 petabytes 178 parameter barometric pressure, humidity, wind direction, temperature, wind velocity, other company historical data IBM InfoSphere BigInsights running on IBM System x iDataPlex Server -Manage and analyze weather and location data - reduce the base resolution of its wind data grid from 27x27 km area down to a 3x3 km (90% reduction) immediate insight into potential location. reducing data processing time Quickly and accurately predict weather patterns at potential sites to increase turbine energy production. Save money avoid from spent on repairing and replacing damaged turbine by the wind. Save time the company forecast optimal turbine placement in 15 minutes instead of 3 week enable customers to achieve a return on investment more quickly
7. Kind of Big Data Collected Business Intelligent Technology used Business Benefit Customer related information (Web surveys, e-mails, text messages, website traffic patterns, and data location in 146 countries Consumer Sentiment Data Storing data centralized instead of within each branch Reducing time spent processing data Improving company response time to customer feedback able to determine that delays were occurring for returns in Phildelphia during specific time of the day Enhanced Hertzs performance and increased customer satisfaction.
8. Meeting 3 11/05/2015 CASE 1 : BIG DATA, BIG REWARDS Identify THREE DECISIONS that were improved by using big data
15. Meeting 3 11/05/2015 CASE 1 : BIG DATA, BIG REWARDS What KINDS OF ORGANIZATIONS are most likely to need big data management and analytical tools? Why?
16. Organizations which responsible to score that huge information such as national library, registration department, income tax, banking institutions 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 need the big data to predict the weather and location data, very useful for the companies to accurately make decision. Thus Vestas needed the data about location and wind to locate their turbines.