Big Data, Big Rewards

Describe the kinds of big data collected by the organizations described in this case.

There are four kinds of big data collected by the organizations described in this case. First, IBM BigSheets 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. 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. IBM and New York City Police Department (NYPD) work together to create the warehouse, which contains data over 120 million criminal complaints, 31 million criminal crime records and 33 billion public records. Third, Vestas implemented a solution consisting of IBM InfoSphere BigInsights software running on high-performance IBM System x iDataPlex server. Fourth, Hertz using big data solution to analyze consumer sentiment from Web surveys, emails, text message. Web site traffic patterns and data generated at all of Hertz’s 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.

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.

Why did the companies described in this case need to maintain and analyze? What business benefits did they obtain?

a.    The British Library

It needed to maintain and analyze big data because traditional data management methods proved inadequate to archive billions of Web pages and legacy analytics tools could not extract useful knowledge from such quantities of data.

b.    New York Police Department (NYPD)

-          Allow the NYPD quickly respond on criminals occurred.

-          Help NYPD to obtain sources of the suspects, such as suspect’s photo, past offences or addresses with maps, can be visualized in seconds on a video wall.

c.    Vestas

-          Vestas is the world’s 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, Vestas relies on location-based data to determine the best spots to install their turbines.

-          Vestas’s Wind Library currently stores 2.8 petabytes of data.

d.    Hertz

-          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 Hertz’s performance and increased customer satisfaction.

The business benefits for maintaining and analyzing big data are as follows:

       i.        Competitive advantages

      ii.        Performance enhancement

     iii.        Increase customer satisfaction

    iv.        Attract more customers and generate more revenue

     v.        Improved decision making (faster & accurate)

    vi.        Excellence operational

   vii.        Reduced cost and time spent

Question 4

Identify three decisions that were improved by using big data.

a.    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 developments time of turbine site.

b.    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 effectively searches data from the British Library web sites. NYPD can make a faster decision to gather the suspect’s detail by using The Real Time Crime Center.

c.    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.

Question 5

What kinds of organizations are most likely to need big data management and analytical tools? Why?

-          Organizations which is responsible to store 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 as 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 big data about the weather and location because the weather and location data are very useful for the companies to accurately make a decision.

Therefore, Vestas need the data about location and wind to locate their turbines