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INFORMATION SYSTEM
& ELECTRONIC COMMERCE
GROUP 1 :
DATO’ NABIL ABD KADIR
SAYNUL ISLAM
MOHAMMAD GHAZALI MOHD DAUD
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TOPIC
BIG DATA,
BIG REWARDS
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BACKGROUND
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BACKGROUND
 In 2012, the amount of digital information is expected to
reach 988 exa bytes
 Big Data refers to the massive amounts of data that collect
over time that are difficult to analyze and handle using
common database management tools.
 Big Data includes business transactions, e-mail, messages,
photos, surveillance videos and activity logs.
 Scientific data from sensors can reach mammoth
proportions over time, and Big Data also includes
unstructured text posted on the Web, such as blogs and
social media.
Q1. DESCRIBE THE KINDS OF BIG DATA
COLLECTED BY THE ORGANIZATIONS DESCRIBED
IN THIS CASE
 Historical data – British Library
Responsible for preserving British Websites that
no longer exist but need to be preserving for
historical purposes.
Eg : Website for past politicians
IBM BigSheets helps the British Library to
process large amounts of data quickly and
efficiently
Cont’s
 City Crime & Criminals Data - NYPD
Real Time Crime Centre in New York City data
warehouse collected 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 onover 120 million criminal
complaints, 31 million criminal crime records
and 33 billion public records.
Cont’s
 Turbine Locations and Wind Data - Vestas
Vesta’s wind library currently stores data on
perspective turbine location and global weather
system.
Vesta implemented IBM Info Sphere Biglnsight
software running on a high perfomance IBM
System xiData Plex server.
Cont’s
 Consumer Sentiment Data - Hertz
A car rental Hertz using Big Data solution to
analyze consumer sentiment from Web surveys,
emails, text messages, Website traffic patterns
and data generated at all of Hertz’s 8,300
locations on 146 countries.
Hertz was able to reduce time spent processing
data and improve response time to customer
feedback and changes in sentiment.
Q2. LIST AND DESCRIBE THE BUSINESS
INTELLIGENCE TECHNOLOGIES DESCRIBED IN THIS
CASE
IBM BigSheets
 IBM BigSheets is a cloud application used to perform ad
hoc analytics at web-scale on unstructured and
structured content.
 IBM BigSheets 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.
 IBM BigSheets built stop the Hadoop framework so it can
process large amounts of data quickly and efficiency.
Cont’s
Real Time Crime Centre (RTCC)
 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 billions 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 instantly
relayed to officers at a crime scene.
Cont’s
IBM InfoSphere BigInsights
 IBM InfoSphere BigInsights brings the power of Hadoop
to the enterprise. Apache TM Hadoop® is the open
source software framework used to reliably managing
large volumes of structured and unstructured data.
 Vestas increased the size of 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.
Q3. WHY DID THE COMPANIES DESCRIBED IN THIS
CASE NEED TO MAINTAIN AND ANALYZE BIG DATA?
WHAT BUSINESS BENEFITS DID THEY OBTAIN?
The British Library
 They needed to maintain and analyze big data because
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.
Cont’s
New York Police Department (NYPD)
 NYPD need to maintain and analyze big data because :-
Allow the NYPD quickly respond on the criminal
occurred.
- Help NYPD to obtain sources of the suspects such as
suspects photo, past offences or addresses with
maps which can be visualized in seconds on a video
wall.
Cont’s
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.
 Areas 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.
Cont’s
HERTZ
 Car rental giant Hertz need to maintain and analyze
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 Phildelphia during specific
time of the day
- Enhanced Hertz’s performance and increased
customer satisfaction.
Cont’s
The business benefits for
analyzing data are as follows:







maintaining
Performance enhancement
Increase customer satisfaction
Attract more customers and generate more revenue
Improved decision making faster and accurate
Excellence operation
Reduced cost and time spent
Competitive advantage
and
Q4. IDENTIFY THREE DECISIONS THAT WERE
IMPROVED BY USING BIG DATA?
1.Optimal Uses Of Resources and Operational Time
Companies can optimal uses their resources to
enhance performance. Vestas can forecast optimal
turbine placement in 15 minutes instead of there
weeks saving a months of development time for
turbine site
Cont’s
2. Quick And Effective Decision Making
Decision making improves and can be quickly and
effective by using big data. Visitors of The British
Library and NYPC can quickly and effectively
searches data from the British Library Web sited.
NYPD can make a faster decision to gather the
suspects detail by using The Real Time Crime
Center.
Cont’s
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.
Q5. WHAT KINDS OF ORGANIZATIONS ARE MORE
LIKELY TO NEED BIG DATA MANAGEMENT AND
ANALYTICAL TOOL? WHY?
 Organizations which responsible to score that 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 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.
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FOR BEING PATIENT