Big data v1.0

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Overview of Big Data

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Big data v1.0

  1. 1. Big Data Analytics: Source of Competitive Advantage and Enabler for Blue Ocean Business Models Suresh Arora suresh_arora@hotmail.com
  2. 2. Outline• Objectives• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 2
  3. 3. Objectives • To identify ways by which Big Data can become source of competitive advantage for businesses  Shortcomings of Legacy Business Intelligence (BI) applications  Identification of industries/sectors which can benefit from Big data analytics • To identify strategies to capture and create value from Big Data  Business drivers for Big Data  Identification of business models around Big Data • To identify various technologies for capturing and analyzing Big Data  Identifies different approaches to store Big Data (e.g., HDFS, Cassandra, MangoDB etc. )  Big Data Platform (Apache Hadoop project) • To identify markets for Big Data products 3
  4. 4. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 4
  5. 5. What Is Big Data? Big Data is a term that describes large volumes of High Velocity, Complex and Variable data that require advanced techniques and technologies to enable the Capture, Storage, Distribution, Management, and Analysis of the information Big Data analytics is the process of examining and interrogating big data assets to derive insights of value for decision making 1 Kilobyte 1,000 bits/byte 1 megabyte 1,000,000 1 gigabyte 1,000,000,000 1 terabyte 1,000,000,000,000 1 petabyte 1,000,000,000,000,000 1 exabyte 1,000,000,000,000,000,000 1 zettabyte 1,000,000,000,000,000,000,000 5
  6. 6. Characteristics of Big Data The “BIG” in big data isn’t just about volume 6
  7. 7. How Is Big Data Different?1) Automatically generated by a machine (e.g. Sensor embedded in an engine)2) Typically an entirely new source of data (e.g. Use of the internet)3) Not designed to be friendly (e.g. Text streams)4) May not have much values – Need to focus on the important part 7
  8. 8. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 8
  9. 9. Challenges with Legacy BI applications • Management of unstructured data is a very large problem • Performance of conventional databases (RDBMS) degrades with increase in data volume 9
  10. 10. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 10
  11. 11. Sectors which can benefit from Big Data 11
  12. 12. Common Big Data Customer ScenariosGain competitive advantage by moving first and fast in your industry IT infrastructur Legal Social network Traffic flow opt Web app opti e optimization discovery analysis imization mization Churn Natural resource Weather foreca Healthcare out analysis exploration sting comes Life sciences re Advertising an Equipment mo Smart meter m Fraud search alysis nitoring onitoring detection 12
  13. 13. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 13
  14. 14. Big Data Framework Big Data is Big BusinessSentiment Analysis, Network Analysis CrowdsourcingCluster Analysis, Mult Predictive Modelingidimensional Analysis 14
  15. 15. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 15
  16. 16. Technology Driven mainly by Open Source initiatives  Apache™ Hadoop project  Apache™ Cassandra project  Apache™ HBase project  Apache™ Hive project  Apache™ Solr project 16
  17. 17. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 17
  18. 18. Market for Big Data Growth rate of Big Data industry is much higher than average growth rate of IT industry 18
  19. 19. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 19
  20. 20. Risks of Big Data Will be so overwhelmed – Need the right people and solve the right problems Technological considerations – Open source – Scalability & Performance issues Many sources of big data is privacy – Self-regulation – Legal regulation 20
  21. 21. The Need for Standards Become more structured over time Fine-tune to be friendlier for analysis Standardize enough to make life much easier 21
  22. 22. Outline• Objective and Methodology• What Is Big Data?• Challenges with Legacy BI applications• Applications of Big Data• Business Models• Technology• Market for Big Data• Risks & Limitations of Big Data• Conclusions 22
  23. 23. Conclusions Big Data is a large and fast growing market Leveraging Big Data for insights can enhance productivity and competitiveness for companies Harnessing Big Data will enable businesses to improve market intelligence For IT professionals it means lot of new job opportunities in the area of data analytics 23
  24. 24. Thank you

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