Albert Wang
awang@mba2013.hbs.edu
Enterprise Big Data Trends & Investment
Opportunities (sample)
Harvard Business School
I...
Executive Summary
Agenda
 Enterprise Infrastructure Software Trends
 Big Data Applications Overview
 Business Intelligence Landscape
 Ap...
2013 enterprise IT spending continues to grow
Source:
http://online.wsj.com/article/SB100014241278873234196045785716841350...
NoSQL offers some compelling value
propositions for startups.
 Performance: “I started with MySQL, but had
a hard time sc...
Although some leaders have emerged, NoSQL
continues to advance at a rapid pace. Trends include
analytic capabilities and l...
Investors perceive enterprise software
infrastructure to be a risky
investment, particularly at the seed stage.
 Is this ...
Agenda
 Big Data Infrastructure Trends
 Big Data Applications Overview
 Business Intelligence Landscape
 Appendix
The increasing maturity of Big Data Infrastructure
will lead to a rise of Big Data Applications.
 BDAs
 “Closed feedback...
Recent innovations in scale-out storage technology
make it relatively inexpensive for companies to
capture massive amounts...
Agenda
 Big Data Infrastructure Trends
 Big Data Applications Overview
 Business Intelligence Landscape
 Appendix
The increasing maturity of Big Data Infrastructure
will lead to a rise of Big Data Applications.
 BDAs
 “Closed feedback...
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Big Data Trends and Investment Opportunities (sample)

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  • MapReduce, Pig, HIVE, Tenzing, and Impala are all MapReduce-based systemsHIVE, Pig, and Impala are all related to HadoopDremel is a low-latency query Spanner: supportssqlSpark/Shark: mostly in-memory operations, supports SQL
  • MongoDB has gained traction, but is poor for analytics. Lots of hype, then lots of complaints a year ago. MapReduce does not scale well for updates: (optimized for batch processing, ie at MOST once every 30 minutes).
  • Amazon Redshift is a white-label ParAccel (acquired April ‘13 by PE-owned Actian)
  • Big Data Trends and Investment Opportunities (sample)

    1. 1. Albert Wang awang@mba2013.hbs.edu Enterprise Big Data Trends & Investment Opportunities (sample) Harvard Business School Independent Student Research
    2. 2. Executive Summary
    3. 3. Agenda  Enterprise Infrastructure Software Trends  Big Data Applications Overview  Business Intelligence Landscape  Appendix
    4. 4. 2013 enterprise IT spending continues to grow Source: http://online.wsj.com/article/SB10001424127887323419604578571684135006800.html
    5. 5. NoSQL offers some compelling value propositions for startups.  Performance: “I started with MySQL, but had a hard time scaling it out in a distributed environment”  Flexibility: “My data doesn’t conform to a rigid schema”  Ease of Use: “It works right out of the box” Source: https://news.ycombinator.com/item?id=4969614 – Includes discussion of production enterprise customers
    6. 6. Although some leaders have emerged, NoSQL continues to advance at a rapid pace. Trends include analytic capabilities and low-latency micro-updates. NoSQL and related systems, by feature Source: Bill Howe, University of Washington. https://class.coursera.org/datasci-001/lecture/index
    7. 7. Investors perceive enterprise software infrastructure to be a risky investment, particularly at the seed stage.  Is this a winner-take-all market?  How many companies can capture meaningful market share?  Is the market still in a pre-paradigmatic state, or has it stabilized?  How can companies generate sufficient escape velocity to avoid being gobbled up by the likes of Oracle, EMC, and Salesforce?  Criticisms of NoSQL  Most customers are startups. Enterprises have more stringent requirements:  ACID for mission-critical requirements  High-level Query Language  Standards compliance: enterprises often have 10,000 disparate databases which need support tools  MongoDB has gained traction, but is poor for analytics.  MapReduce does not scale well for updates  Industry shift back towards real-time, SQL-like interfaces (Cloudera Impala) Source: VC and entrepreneur conversations http://cacm.acm.org/blogs/blog-cacm/99512-why-enterprises-are-uninterested-in-nosql/fulltext
    8. 8. Agenda  Big Data Infrastructure Trends  Big Data Applications Overview  Business Intelligence Landscape  Appendix
    9. 9. The increasing maturity of Big Data Infrastructure will lead to a rise of Big Data Applications.  BDAs  “Closed feedback loops”: Each acquired customer adds data to the engine, improving the service for all customers, lowering long-term customer acquisition costs and raising customer lifetime values  Teams with focus on large-scale systems, machine learning, and data mining  Consumerization of the Enterprise  Employee productivity matters! Why do I have to “learn” to use enterprise software?  Explosion of devices, device fragmentation  SaaS tool adoption: pay with a credit card  Use of freemium model as a sales channel  Fees smaller to avoid triggering approval process  Finally, the person paying is also the person using the tool! Source: http://techcrunch.com/2012/05/04/the-rise-of-big-data-apps-and-the-fall-of-saas/ http://assets.accel.com/51c09ae9f40fc_the_last_mile_in_big_data.pdf
    10. 10. Recent innovations in scale-out storage technology make it relatively inexpensive for companies to capture massive amounts of data.  Increased data digitization: mobile, web logs  Cheaper, faster storage: Amazon Redshift, Hapyrus  IT managers comfortable with cloud-based storage  Most Fortune 500 companies have piloted Hadoop  Companies such as Cloudera, Mapr, Vertica (acquired by HP) and Datastax are doing a great job of delivering the infrastructure required to hold and manage big data in a Source: http://gigaom.com/2012/10/14/big-data-is-useless-unless-its-also-fast-diverse/
    11. 11. Agenda  Big Data Infrastructure Trends  Big Data Applications Overview  Business Intelligence Landscape  Appendix
    12. 12. The increasing maturity of Big Data Infrastructure will lead to a rise of Big Data Applications.  BDAs  “Closed feedback loops”: Each acquired customer adds data to the engine, improving the service for all customers, lowering long-term customer acquisition costs and raising customer lifetime values  Teams with focus on large-scale systems, machine learning, and data mining  Consumerization of the Enterprise  Employee productivity matters! Why do I have to “learn” to use enterprise software?  Explosion of devices, device fragmentation  SaaS tool adoption: pay with a credit card  Use of freemium model as a sales channel  Fees smaller to avoid triggering approval process  Finally, the person paying is also the person using the tool! Source: http://techcrunch.com/2012/05/04/the-rise-of-big-data-apps-and-the-fall-of-saas/ http://assets.accel.com/51c09ae9f40fc_the_last_mile_in_big_data.pdf

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