Big Data Use Cases

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Welcome to big data use case course. In this course we will talk about what is big data? Who are using it and at the end we will share the lessons learnt from the early adopters. Big Data is an umbrella term used to refer the technology behind collecting and analyzing large volume of data at a fast speed. In last few years, number of devices and services customers use, have increased multi fold. As customers are using more of every thing, they are creating more data. By inter connecting these data, you can know your customer better and provide a better service. Big Data helps you in storing and connecting these data.

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Big Data Use Cases

  1. 1. Big Data - Use Cases
  2. 2. What is Big Data More Devices More Consumption More Content New & Better Information Big Data encompasses not only the content itself, but how it’s consumed. *Source: IDC 2011  Every gigabyte of stored content can generate a petabyte or more of transient data*  The information about you is much greater than the information you create Big Data Drivers:  The high adoption of data capture and creation technologies  Increased “interconnectivity” drives consumption, creates more data  Inexpensive storage makes it possible to keep more data for longer period  Hadoop software and analysis tools turn data into information
  3. 3. Big Data Characteristics
  4. 4. Big Data Contexts
  5. 5. Data Growth Examples
  6. 6. Companies using Big Data Company : Etsy Category : On line retailer Big Data Attribute : Volume Revenue: $895 M Doing Large scale analysis of clickstream data, company is discovering important product attributes for the user like Materials, prices, textures etc. They use these attributes to rank the products in search results. Percentage of users making purchases increased
  7. 7. Companies using Big Data Company : Macy Category : Brick and Mortar retailer Big Data Attribute : Volume and Speed Revenue: $26 B Price check analysis of its 10,000 articles across 800 stores nationwide in less than 2 hours. When ever a neighboring competitor between New York and Los Angles goes for aggressive price reductions, Macy’s follows the suite. If there is no competition, price remains unchanged. There are around 270 million different prices across entire range of goods and locations. Just completing this analysis at this speed was unthinkable without Big Data. Reduce loss to local competition
  8. 8. Companies using Big Data Company : Canadian Pacific (Using GE System) Category : Brick and Mortar retailer Big Data Attribute : Volume and Velocity Revenue: $5.7 B “Trip Optimizer” is a fuel-saving system that GE has developed for freight trains. It takes into account data such as track conditions, weather, the speed of train, GPS data and “train physics”, and makes decisions about how and when the train should break. Reduced fuel usage by 4 - 14 %
  9. 9. Companies using Big Data Company : Sears Holdings (Sears and Kmart) Category : Brick and Mortar retailer Big Data Attribute : Volume and Velocity Revenue: $42 B Sears’ process for analyzing marketing campaigns for loyalty club members used to take six weeks on mainframe, Teradata, and SAS servers. The new process running on Hadoop can be completed weekly. For certain online and mobile commerce scenarios, Sears can now perform daily analyses. What's more, targeting is more granular, in some cases down to the individual customer. Whereas the old models made use of 10% of available data, the new models run on 100%. Part of a five-part strategy to get the company back on track
  10. 10. Big Data – Case Study Company : Salesforce dot com Category : Software Vendor Big Data Attribute : Volume and Varieties Revenue: $2.27 B What are they solving • Track feature usage/adoption across 130k+ customers examples: Accounts, Contacts, Visualforce, Apex,… • Track standard metrics across all features examples: #Requests, #UniqueOrgs, #UniqueUsers, AvgResponseTime,… • Track features and metrics across all channels • Example –API, UI, Mobile • Primary audience: Executives, Product Managers
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  18. 18. Use Cases by Verticals Financial Services Customers Insights – using Hadoop to improve customer profile analysis to help determine Eligibility for equity, capital, insurance, mortgage and credit Fraud detection – Hadoop provides the scalable method to easily detect many Types of fraud and loss prevention. Companies are also developing models to predict Future fraud events like PayPal. Micro Targeting – Banks have multiple silo systems for loans, mortgages, investments. Hadoop can be used to provide aggregated view on customer profitability. Healthcare and Life Science Gene Sequencing – The sequencing of DNA for organism holds huge promise for human kind. Health Information exchange – Hadoop can be used by providers to manage and share healthcare records from mixed data sources such as images, treatments, demographics and billing
  19. 19. Use Cases by Verticals Manufacturing Service Management – The availability of sensors and corresponding ability to effectively store and analyze large data feeds across customer locations and product SKUs, has resulted in more effective and efficient service. Operations – Hadoop can also improve the post sales maintenance process. The manufacturing industry is adding sensors to equipment to collect much more data on the operations of the equipment. Collecting and analyzing these data improves the maintenance process, increase productivity and reduces cost. Media and Entertainment Price analytics – Companies can use Hadoop to determine dynamic pricing for everything from game tickets, web bases games to music and videos. Customer Insights – Media companies need to better understand market segments and consumer personal preferences and behavior to better match each brand to its segment and help increase sales.
  20. 20. Big Data Lessons Sources 1. Survey of 752 corporate executives by SAS institute from a broad range of sectors and countries 2. Interviews of 17 data pioneers 3. Information Week Survey of CIOs Pros • Strong link between financial performance and effective use of big data • Social media analytics and web tracking technologies can transform the way business collect data about their customers Cons • Too many big data projects are structured like boil-the-ocean experiments • Hadoop-based techniques aren't enough to meet business needs for analysis
  21. 21. Contact: Phone: 408-647-3010 URL: http://www.aziksa.com For further training information contact us. Email : support@aziksa.com

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