case study 2 big data big rewards

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Case Study - Big Data, Big Rewards

Introduction

Big data datasets that are too large to be gathered, stored, managed and analyzed by typical database software tools – can generate plenty of value for organizations of all sizes and types. Organizations that are able to harness the power of big data can drive both operational efficiency and quality, leading to cost and labor savings and a competitive edge. Leveraging big data can also help companies streamline processes, fighting fraud and reducing errors.

Question 1: Describe the kinds of big data collected by the organizations described in this case.

There are mainly four kinds of big data collected by the organizations described in this case. First, IBM Big sheets help the British Library to handle with huge quantities of data and extract the useful knowledge. 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. 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 on 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 a high-performance IBM System x iDataPlex server. 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. Forth, Hertz 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.

Question 2: 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.

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

The British Library

The British Library 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.

New York Police Department (NYPD)

NYPD need to maintain and analyze big data because:

Allow the NYPD quickly respond on the 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.

Vestas need to maintain and analyze big data because:

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, 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.

Car rental giant Hertz need to maintain and analyze big 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 Philadelphia during specific time of the day.

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:

Competitive advantages

Performance Enhancement

Increase customer satisfaction

Attract more customers and generate more revenue

Improved decision making (faster & accurate)

Excellence operational

Reduced cost and time spent

Question 4: Identify three decisions that were improved by using big data.

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.

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.

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 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.

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 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.

Subhanallah

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• Case 8: Big Data, Big Rewards

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. First, IBM Bigsheets help the British Library to handle with huge quantities of data and extract the useful knowledge. 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. Third, Vestas implemented a solution consisting of IBM InfoSphere BigInsights software running on a high-performance IBM System x iDataPlex server. 2. 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. 3. Why did the companies described in this case need to maintain and analyze? What business benefits did they obtain? There are mainly three reasons. First, Traditional data management methods proved inadequate to archive millions of these Web pages, and legacy analytics tools couldn’t extract useful knowledge from such quantities of data. Secondly, Law enforcement agencies can become more proactive in its efforts to fight crime patterns since information of suspects can be visualized in seconds on a video wall instantly relayed to officers at a crime scene. Third and Finally, Vestas relies on location based data to determine the best spots to install their turbines. If there is no data available, it is difficult to accurately place its turbines for optimal wind power generation. What business benefits did they obtain? 1.      Sustainable Competitive advantages 2.      Improve quality and performance 3.      Maintain customer relationship with high level of satisfaction 4.      Gather more customers to generate more profit 5.      Improved decision making (faster & accurate) 6.      Excellence operational 7.      Reduced cost and time spent 4. Identify three decisions that were improved by using big data. •        Analyze consumer sentiment. For example, car rental giant Hertz gathers data •        Reduce cost and time •        Quick and efficient decision making 1.       What kinds of organizations are most likely to need big data management and analytical tools? Why? 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. Online Analytical Processing (OLAP) Supports multidimensional data analysis, enabling users to view the same data in different ways using multiple dimensions. Data mining provides insight into corporate data that cannot be obtained with OLAP by finding patterns and relationships in large databases and inferring rules from them to predict future behavior. Associations: occurrences to a single event Sequences: events over time Classification: description of group to which something belongs Clustering: grouping together, but no groups are defined. Forecasting: find patterns Text mining & Web mining tools used to extract key elements of unstructured data sets, discover patterns and relationships, and summarize the information.

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8 case studies and real world examples of how Big Data has helped keep on top of competition

Fast, data-informed decision-making can drive business success. Managing high customer expectations, navigating marketing challenges, and global competition – many organizations look to data analytics and business intelligence for a competitive advantage.

Using data to serve up personalized ads based on browsing history, providing contextual KPI data access for all employees and centralizing data from across the business into one digital ecosystem so processes can be more thoroughly reviewed are all examples of business intelligence.

Organizations invest in data science because it promises to bring competitive advantages.

Data is transforming into an actionable asset, and new tools are using that reality to move the needle with ML. As a result, organizations are on the brink of mobilizing data to not only predict the future but also to increase the likelihood of certain outcomes through prescriptive analytics.

Here are some case studies that show some ways BI is making a difference for companies around the world:

1) Starbucks:

With 90 million transactions a week in 25,000 stores worldwide the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions

Through its popular loyalty card program and mobile application, Starbucks owns individual purchase data from millions of customers. Using this information and BI tools, the company predicts purchases and sends individual offers of what customers will likely prefer via their app and email. This system draws existing customers into its stores more frequently and increases sales volumes.

The same intel that helps Starbucks suggest new products to try also helps the company send personalized offers and discounts that go far beyond a special birthday discount. Additionally, a customized email goes out to any customer who hasn’t visited a Starbucks recently with enticing offers—built from that individual’s purchase history—to re-engage them.

2) Netflix:

The online entertainment company’s 148 million subscribers give it a massive BI advantage.

Netflix has digitized its interactions with its 151 million subscribers. It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns. It then leverages that information to recommend movies and TV shows customized as per the subscriber’s choice and preferences.

As per Netflix, around 80% of the viewer’s activity is triggered by personalized algorithmic recommendations. Where Netflix gains an edge over its peers is that by collecting different data points, it creates detailed profiles of its subscribers which helps them engage with them better.

The recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers which has helped Netflix earn a whopping one billion via customer retention. Due to this reason, Netflix doesn’t have to invest too much on advertising and marketing their shows. They precisely know an estimate of the people who would be interested in watching a show.

3) Coca-Cola:

Coca Cola is the world’s largest beverage company, with over 500 soft drink brands sold in more than 200 countries. Given the size of its operations, Coca Cola generates a substantial amount of data across its value chain – including sourcing, production, distribution, sales and customer feedback which they can leverage to drive successful business decisions.

Coca Cola has been investing extensively in research and development, especially in AI, to better leverage the mountain of data it collects from customers all around the world. This initiative has helped them better understand consumer trends in terms of price, flavors, packaging, and consumer’ preference for healthier options in certain regions.

With 35 million Twitter followers and a whopping 105 million Facebook fans, Coca-Cola benefits from its social media data. Using AI-powered image-recognition technology, they can track when photographs of its drinks are posted online. This data, paired with the power of BI, gives the company important insights into who is drinking their beverages, where they are and why they mention the brand online. The information helps serve consumers more targeted advertising, which is four times more likely than a regular ad to result in a click.

Coca Cola is increasingly betting on BI, data analytics and AI to drive its strategic business decisions. From its innovative free style fountain machine to finding new ways to engage with customers, Coca Cola is well-equipped to remain at the top of the competition in the future. In a new digital world that is increasingly dynamic, with changing customer behavior, Coca Cola is relying on Big Data to gain and maintain their competitive advantage.

4) American Express GBT

The American Express Global Business Travel company, popularly known as Amex GBT, is an American multinational travel and meetings programs management corporation which operates in over 120 countries and has over 14,000 employees.

Challenges:

Scalability – Creating a single portal for around 945 separate data files from internal and customer systems using the current BI tool would require over 6 months to complete. The earlier tool was used for internal purposes and scaling the solution to such a large population while keeping the costs optimum was a major challenge

Performance – Their existing system had limitations shifting to Cloud. The amount of time and manual effort required was immense

Data Governance – Maintaining user data security and privacy was of utmost importance for Amex GBT

The company was looking to protect and increase its market share by differentiating its core services and was seeking a resource to manage and drive their online travel program capabilities forward. Amex GBT decided to make a strategic investment in creating smart analytics around their booking software.

The solution equipped users to view their travel ROI by categorizing it into three categories cost, time and value. Each category has individual KPIs that are measured to evaluate the performance of a travel plan.

Reducing travel expenses by 30%

Time to Value – Initially it took a week for new users to be on-boarded onto the platform. With Premier Insights that time had now been reduced to a single day and the process had become much simpler and more effective.

Savings on Spends – The product notifies users of any available booking offers that can help them save on their expenditure. It recommends users of possible saving potential such as flight timings, date of the booking, date of travel, etc.

Adoption – Ease of use of the product, quick scale-up, real-time implementation of reports, and interactive dashboards of Premier Insights increased the global online adoption for Amex GBT

5) Airline Solutions Company: BI Accelerates Business Insights

Airline Solutions provides booking tools, revenue management, web, and mobile itinerary tools, as well as other technology, for airlines, hotels and other companies in the travel industry.

Challenge: The travel industry is remarkably dynamic and fast paced. And the airline solution provider’s clients needed advanced tools that could provide real-time data on customer behavior and actions.

They developed an enterprise travel data warehouse (ETDW) to hold its enormous amounts of data. The executive dashboards provide near real-time insights in user-friendly environments with a 360-degree overview of business health, reservations, operational performance and ticketing.

Results: The scalable infrastructure, graphic user interface, data aggregation and ability to work collaboratively have led to more revenue and increased client satisfaction.

6) A specialty US Retail Provider: Leveraging prescriptive analytics

Challenge/Objective: A specialty US Retail provider wanted to modernize its data platform which could help the business make real-time decisions while also leveraging prescriptive analytics. They wanted to discover true value of data being generated from its multiple systems and understand the patterns (both known and unknown) of sales, operations, and omni-channel retail performance.

We helped build a modern data solution that consolidated their data in a data lake and data warehouse, making it easier to extract the value in real-time. We integrated our solution with their OMS, CRM, Google Analytics, Salesforce, and inventory management system. The data was modeled in such a way that it could be fed into Machine Learning algorithms; so that we can leverage this easily in the future.

The customer had visibility into their data from day 1, which is something they had been wanting for some time. In addition to this, they were able to build more reports, dashboards, and charts to understand and interpret the data. In some cases, they were able to get real-time visibility and analysis on instore purchases based on geography!

7) Logistics startup with an objective to become the “Uber of the Trucking Sector” with the help of data analytics

Challenge: A startup specializing in analyzing vehicle and/or driver performance by collecting data from sensors within the vehicle (a.k.a. vehicle telemetry) and Order patterns with an objective to become the “Uber of the Trucking Sector”

Solution: We developed a customized backend of the client’s trucking platform so that they could monetize empty return trips of transporters by creating a marketplace for them. The approach used a combination of AWS Data Lake, AWS microservices, machine learning and analytics.

• Reduced fuel costs
• Optimized Reloads
• More accurate driver / truck schedule planning
• Smarter Routing
• Fewer empty return trips
• Deeper analysis of driver patterns, breaks, routes, etc.

8) Challenge/Objective: A niche segment customer competing against market behemoths looking to become a “Niche Segment Leader”

Solution: We developed a customized analytics platform that can ingest CRM, OMS, Ecommerce, and Inventory data and produce real time and batch driven analytics and AI platform. The approach used a combination of AWS microservices, machine learning and analytics.

• Reduce Customer Churn
• Optimized Order Fulfillment
• More accurate demand schedule planning
• Improve Product Recommendation
• Improved Last Mile Delivery

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My Case Study

Saturday, 14 june 2014, week 3 (a)- big data, big rewards.

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Can you tell me which book ies this version of the case study? urgent help needed. :)

It is in Laudon Management Information systems 13th edition

Big Data isn't new, but it is becoming more and more popular as it becomes easier to capture and store information. The volume of data created every year is increasing at an astonishing rate. By some estimates, 90% of the world's data has been created in the last two years alone! The result is that there is simply too much data to comprehend or process with human intelligence. That's where big data technologies come in.

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Question: CASE STUDY || BIG DATA, BIG REWARDS Today's companies are dealing with an avalanche of data from social media, search, and sensors as well as from traditional sources. In 2012, the amount of digital information generated is expected to reach 988 exabytes, which is the equivalent to a stack of books from the sun to the planet Pluto and back. Making sense of

CASE STUDY || BIG DATA, BIG REWARDS Today's companies are dealing with an avalanche of data from social media, search, and sensors as well as from traditional sources. In 2012, the amount of digital information generated is expected to reach 988 exabytes, which is the equivalent to a stack of books from the sun to the planet Pluto and back. Making sense of "big data" has become one of the primary challenges for corporations of all shapes and sizes, but it also represents new opportunities. How are companies currently taking advantage of big data opportunities? The British Library had to adapt to handle big data. Every year visitors to the British Library Web site perform over 6 billion searches, and the library is also responsible for preserving British Web sites that no longer exist but need to be preserved for historical purposes, such as the Web sites for past politicians. Traditional data management methods proved inadequate to archive millions of these Web pages, and legacy analytics tools couldn't extract useful knowledge from such quantities of data. So the British Library partnered with IBM to implement a big data solution to these challenges. 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. IBM BigSheets is built atop the Hadoop framework, so it can process large amounts of data quickly and efficiently 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 (see Chapter 4) between individuals and criminal organizations that would be difficult to uncover in smaller data sets. 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. This means that law enforcement can become more proactive in its efforts to fight crime and stop it before it occurs. In New York City, the Real Time Crime Center data warehouse contains millions of data points on city crime and criminals. IBM and the New York City Police Department (NYPD) worked together to create the warehouse, which contains data on over 120 million criminal complaints, 31 million national crime records, and 33 billion public records. The system's search capabilities allow the NYPD to quickly obtain data from any of these data sources. Information on criminals, such as a suspect's photo with details of past offenses or addresses with maps, can be visualized in Seconds on a video wall or instantly relayed to officers at a crime scene. Other organizations are using the data to go green, or, in the case of Vestas, to go even greener. Headquartered in Denmark, Vestas is the world's largest wind energy company, with over 43,000 wind turbines across 66 countries. Location data are important to Vestas so that it can accurately place its turbines for optimal wind power generation Areas without enough wind will not generate the necessary power, but areas with too much wind may damage the turbines, Vestas relies on location-based data to determine the best spots to install their turbines To gather data on prospective turbine locations, Vestas's wind library combines data from global weather systems along with data from existing turbines. The company's previous wind library provided information in a grid pattern, with each grid measuring 27 27 kilometers (17 x 17 miles). Vestas engineers were able to bring the resolution down to about 10 x 10 meters (32 x 32 feet) to establish the exact wind flow pattern at a particular location. To further increase the accuracy of its turbine placement models, Vestas needed to shrink the grid area even more, and this required 10 times as much data as the previous system and a more powerful data management platform The company implemented a solution consisting of IBM InfoSphere Biginsights software running on a highperformance IBM System xData Plex server. (InfoSphere Bigtraights is a set of software tools for big data analysis and visualization, and is powered by Apache Hadoop. Using these technologies, Vestas increased the size of its wind library and is able manager and analyze location and weather data with models that are much more powerful and precise. Vestas's wind library 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. Vestas plans to add global deforestation metrics, satellite images, geospatial data, and data on phases of the moon and tides The company can now reduce the resolution of its wind data grids by nearly 90 percent, down to a 3 x 3 kilometer area (about 1.8% 18 miles). This capability enables Vestas to forecast optimal turbine placement in 15 minutes instead of three weeks, saving a month of development time for a turbine site and enabling Vestas customers to achieve a return on investment much more quickly Companies are also using big data solutions to analyse consumer sentiment. For example, car-rental giant Hertz gathers data from Web surveys, e-mails, text messages, Web site traffic patterns, and data generated at all of Hertz's 8,300 locations in 146 countries. The company now stores all of that data centrally Instead of within each branch, reducing time spent processing data and improving company response time to customer feedback and changes in sentiment. For example, by analyzing data generated from multiple sources, Hertz was able to determine that delays were occurring for returns in Philadelphia during specific times of the day After investigating this anomaly, the company was able to quickly adjust staffing levels at its Philadelphia office during those peak times, ensuring a manager was present to resolve any issues. This enhanced Hertz's performance and increased customer satisfaction There are limits to using big data. Swimming in numbers doesn't necessarily mean that the right information is being collected or that people will make smarter decisions. Last year, a McKinsey Global Institute report cautioned there is a shortage of specialists who can make sense of all the information being generated. Nevertheless, the trend towards big data shows no sign of slowing down; in fact, it's much more likely that big data is only going to get bigger Sewe Sonul Green Date Unlocks Buse Value Bulwary 2018, Burhan Duta What Ewry CO Netto now." Ciny, lawry 12, 2012 IM Corporation bumi Cimte te Capital Duta 2013, Corporation. Etending and enhancing low enforcement Cowbel Ger Advantage and its Library and Start Team Dato Archive the Web. 2010 1. Describe the kinds of big data collected by the organizations described in this case. 2. List and describe the business intelligence technologies described in this case. 3. Why did the companies described in this case need to maintain and analyze big data? What business benefits did they obtain? 4. Identify three decisions that were improved b using big data. 5. What kinds of organizations are most likely to need big data management and analytical tools? Why?

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Start by identifying and describing the four types of data collected by the organizations mentioned in the case study to understand the scope and variety of the data they are handling.

Answer - 1) There are generally four types of data that are gathered by the organizations mentioned in this article. The first is that IBM Big Sheets help British Library to handle massive amounts of data and to extract valuable information.  IBM …

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