The document discusses how various organizations are leveraging big data and analytics technologies. It provides examples of the British Library, law enforcement agencies, Vestas Wind Energy, and Hertz collecting large amounts of structured and unstructured data from sources like websites, sensors, surveys, and customer interactions. These organizations use technologies like Hadoop, sentiment analysis, and web mining to help extract meaningful insights from their big data. For instance, Vestas can now optimize wind turbine placement in 15 minutes using location and weather data analytics compared to three weeks previously. The document also notes that any organizations actively involved in web/social media or that generate large customer data can benefit from big data management and analytical tools to help make better, timely decisions.

1. Big Data, Big Rewards
Prepared by:
Ismail Bin Mahedin (P13D122P)
Samat Haron Bin Joll (P13D123P)
Hjh Sulzarina Bt Hj. Mohamed (P13D119P)
Dayang Suhana Bt Awang Bujang (P13D152P
- CASE STUDY

2. What is Big
Data?
Big data is being generated by everything around us at
all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile
devices transmit it. Big data is arriving from multiple
sources at an alarming velocity, volume and variety.
To extract meaningful value from big data, you need
optimal processing power, analytics capabilities and

4. Data Volume Trend

5. 1.Describe the kinds of “big
data” collected by the
organizations described in
this case.

6. British Library: It collects data from typical library resources like books,
periodicals, and newspapers. In addition, it must store and collect data
from Web sites that no longer exist but must be preserved for historical
purposes. Data from over 6 billion searches must also be stored.
Law enforcement agencies: Collect data on criminal complaints,
national crime records, and public records.
Vestas Wind Energy: Collects data from 43,000 turbines in 66 countries;
collects location-based data to help determine the best location for
turbines; currently stores 2.8 petabytes of data and includes
approximately 178 parameters, such as barometric pressure, humidity,
wind direction, temperature, wind velocity, and other company
historical data; plans to add global deforestation metrics, satellite
images, geospatial data, and data on phases of the moon and tides.
Hertz: Gathers data from Web surveys, emails, text messages, Web site
traffic patterns, and data generated at all of Hertz’s 8300 locations in
146 countries.

7. New text-mining software described in the case can
shorten data analysis to hours or minutes and produce
better results. Businesses can react faster to solve problems,
satisfy customers, and change work processes. Managers
can detect emerging issues and pinpoint troubled areas of
the business at many different managerial levels. Managers
can discover patterns and relationships in the data and
summarize the information more quickly and more easily
The British Library and Vestas use Hadoop so it can process
large amounts of data quickly and efficiently. Hertz uses
sentiment analysis to determine customer satisfaction. Law
enforcement agencies use Web mining techniques to help
determine potential criminal acts. They also use analytics to
predict future crime patterns.
2. List and describe the business intelligence
technologies described in this case.

9. The British Library is able to
maintain historical records of
events and provide users
with more information about
its past. It can now process
information requests more
quickly and easily. The
technology it uses provides
an insight engine that helps
extract, annotate, ad
visually analyze vast
amounts of unstructured
Web data, delivering the
results via a Web browser.
Criminals and criminal
organizations are
increasingly using the
Internet to coordinate and
perpetrate their crimes. New
tools allow agencies to
analyze data from a wide
array of sources and apply
analytics to predict future
crime patterns.
Vestas is able to collect more
data that can reduce the
resolution of its grid patterns
from 17 x 17 miles to 32 x 32
feet to establish exact wind
flow patterns at particular
locations. That further
increases the accuracy of its
turbine placement models.
Hertz stores all of its data
centrally instead of within
each branch, reducing time
spent processing data and
improving company
response time to customer
feedback and changes in
sentiment.

11. Vestas used its big data to help find the best places to
install its wind turbines. It is able to manage and
analyze location and weather data with models that
are much more powerful and precise. The new
technology enables the company to forecast optimal
turbine placement in 15 minutes instead of three
weeks, saving a month of development time for a
turbine site and enabling customers to achieve a return
on investment much more quickly.
Hertz used it data analysis generated from different
sources to determine the cause of delays at its
Philadelphia locations and adjusted staffing levels
during peak times and ensuring a manager was
present to resolve any issues.
Law enforcement agencies use their data analysis to
predict future crime patterns and become more
proactive in its efforts to fight crime and stop it before it
occurs.

12. 5. What kinds
of
organizations
are most likely
to need “big
data”
management
and
analytical
tools? Why?
Organizations that have an
active presence on the Web
or on social media sites need
to use big data management
and analytical tools to process
the numerous unstructured
data that can help them
make better, more timely
decisions. Businesses that
generate big data from
manufacturing, retailing, and
customer service need the
tools that the technology can
provide.

14. ThankYou
Data Information Knowledge