This document discusses how big data analytics can provide competitive advantages and enable new business models. It outlines the objectives, challenges with legacy business intelligence applications, applications and sectors that benefit from big data, common business models, relevant technologies like Apache Hadoop, the growing big data market, risks and limitations, and concludes that leveraging big data can enhance competitiveness for companies and create new jobs.

1. Big Data Analytics: Source of Competitive Advantage
and Enabler for Blue Ocean Business Models
Suresh Arora
suresh_arora@hotmail.com

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
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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
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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
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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
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6. Characteristics of Big Data
 The “BIG” in big data isn’t just about volume
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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
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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
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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
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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
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11. Sectors which can benefit from Big Data
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12. Common Big Data Customer Scenarios
Gain 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
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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
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14. Big Data Framework
 Big Data is Big Business
Sentiment Analysis
, Network Analysis Crowdsourcing
Cluster Analysis, Mult Predictive Modeling
idimensional Analysis
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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
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16. Technology
 Driven mainly by Open Source initiatives
 Apache™ Hadoop project
 Apache™ Cassandra project
 Apache™ HBase project
 Apache™ Hive project
 Apache™ Solr project
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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
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18. Market for Big Data
 Growth rate of Big Data industry is much higher than average
growth rate of IT industry
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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
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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
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21. The Need for Standards
 Become more structured over time
 Fine-tune to be friendlier for analysis
 Standardize enough to make life much easier
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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
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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
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24. Thank you