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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

Big data big rewards meeting 3

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Page 1: Big data big rewards meeting 3

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

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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 skills.

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Data Volume Trend

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

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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.

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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 thereby saving time and

money.

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.

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

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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.

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4. Identify three

decisions that were improved by using “big

data.”

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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.

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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.

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Data Information Knowledge