QUESTION 1 : Describe the kinds of big data collected by the organizations described in this case.
There
are mainly three kinds of big data collected by the organizations described in
this case.
1. Sears Holding
·
It has customer databases up to 60 million which
is the most by any company in United States afer Boeing Corporation
·
It has the data of all its customers and in a
survey sears is known for the company holding big data more effectively
· In 2011 Sears launched a new program called
rewards loyalty program which enables them to collect 100% data of the
customers.
·
it started to invest in new technologies and
invested a lot in cloud and other technologies.
·
It turned on to a new technology known as Apache
Hadoop technology which can run on any plat form.
·
Using this all the processing of any item can be
done in a week
2. New York City Police Department (NYPD)
• City Crime and Criminal Data warehouse contains millions of data points on city crime and criminal
• U.S 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.
• The Real Time Crime Center contains data on over 120
million criminal complaints, 31 million criminal crime records and 33 billion
public records.
3. Vestas
• Location data, prospective turbine locations.
• Vesta’s 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. Hertz
• Data of consumer sentiment
• 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 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.
5. Autozone
- It uses big data to adjust their inventory across their stores.
- Any person couldn’t find his/her desired product can be found in all the other stores of the company.
- It has the database of the cars which are driven by the people surrounding their store
QUESTION
2 : List and describe the business intelligence technologies described in this
case.
1. IBM BigSheets
• IBM BigSheets is a cloud application used to
perform ad hoc analytical at web scale on unstructured and structured content.
• 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. For example, users can see
search results in a pie chart.
• 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.
• IBM BigSheets built atop the Hadoop
framework, so it can process large amounts of data quickly and efficiency.
2. Real Time Crime Center (RTTC)
• The Real Time Crime Center (RTCC) is a
centralized technology center for the New York (NYPD) and Houston Police
Departments.
• RTCC data warehouse contains millions of
data points on city crime and criminals and billion of public records.
• The systems search capabilities allow the
NYPD to quickly obtain data from any of these data sources.
• Information on criminals. Such as
suspect’s photo with details of past offences or addresses with maps, can be
visualized in seconds on a video wall or install relayed to officers at a crime
scene.
3. IBM InfoSphere BigInsights
• IBM InfoSphere BigInsights brings the
power of Hadoop to the enterprise. Apache Hadoop is the open source software framework, used to reliably managing
large volumes of structured and unstructured data.
• Vestas increased the size of its wind
library and is able manage and analyze location and weather data with models
that are much more powerful and precise.
• It implemented a solution consisting of
IBM InfoSphere BigInsights software running on a high-performance IBM System x
iDataPlex server.
QUESTION 3 : Why did the companies described in this case need to maintain and analyze? What business benefits did they obtain?
1. The British Library
The
British Library needed to maintain and analyze big data because :
i) Traditional data management methods
proved inadequate to archive billions of Web pages and legacy analytics tools
couldn’t extract useful knowledge from such quantities of data.
2. New York Police Department (NYPD)
NYPD
need to maintain and analyze big data because :
i) Allow the NYPD quickly respond on the
criminals occurred.
ii) 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.
3. Vestas
Vestas
need to maintain and analyze big data because :
Vestas is the world’s largest wind
energy company.
i) Location data are important to Vestas
so that can accurately place its turbines.
ii) Areas without enough wind will not
generate the necessary power.
iii)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.
Vesta’s Wind Library currently stores
2.8 petabytes od data.
4. Hertz
Car
rental giant Hertz need to maintain and analyze big data because :
i) Reducing time spent processing data.
ii) Improving company response time to
customer feed back.
iii)Hertz was able to determine that delays
were occurring for returns in Philadelphia during specific time of the day.
iv) Enhanced Hertz’s performance and
increased customer satisfaction.
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 customer and generate more
revenue
5. Improved decision making (faster &
accurate)
6. Excellence operational
7. Reduced cost and time spent
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
suspect’s detail by using The Real Time Crime Center.
3. Reduce operational cost and other related cost
Company
quickly make 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?
1. 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.
2. 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.
3. 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
