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The Business of  BIG DATA,[object Object],General Assembly,[object Object], September 14th, 2011,[object Object]
The Business of Big Data (IA Ventures)
What we’ll cover,[object Object]
What we’ll cover,[object Object],What is Big Data?,[object Object],Characteristics of a Big Data business,[object Object],Creating competitive barriers in Big Data,[object Object]
What we’ll cover,[object Object],What is Big Data?,[object Object],Characteristics of a Big Data business,[object Object],Creating competitive barriers in Big Data,[object Object]
What we’ll cover,[object Object],What is Big Data?,[object Object],Characteristics of a Big Data business,[object Object],Creating competitive barriers in Big Data,[object Object]
What we’ll cover,[object Object],What is Big Data?,[object Object],Characteristics of a Big Data business,[object Object],Creating competitive barriers in Big Data,[object Object],The Big Data team,[object Object]
Why now?,[object Object]
Why now?,[object Object],There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days, and the pace is increasing,[object Object],Eric Schmidt, Google CEO, Techonomy Conference, August 4, 2010,[object Object]
Why now?,[object Object],There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days, and the pace is increasing,[object Object],Eric Schmidt, Google CEO, Techonomy Conference, August 4, 2010,[object Object],Data is becoming the new raw material of business: an economic input almost on a par with capital and labour. “Every day I wake up and ask, ‘how can I flow data better, manage data better, analyse data better?” says Rollin Ford, the CIO of Wal-Mart. ,[object Object],Source: Data, Data Everywhere, The Economist, February 25, 2010,[object Object]
Source: Mike Driscoll, CTO Metamarkets: The Three Sexy Skills of Data Scientists (& Data Driven Startups),[object Object]
Source: Mike Driscoll, CTO Metamarkets: The Three Sexy Skills of Data Scientists (& Data Driven Startups),[object Object]
What is Big Data?,[object Object]
Big Data = Complex Data,[object Object],Large,[object Object],Real-time,[object Object]
Large,[object Object]
The Business of Big Data (IA Ventures)
Real-time,[object Object]
So what is a Big Data company?,[object Object]
A Big Data company unlocks intelligence from complex data,[object Object]
IA Ventures invests in early-stage companies unlocking intelligence from complex data,[object Object]
history of big data,[object Object]
The Business of Big Data (IA Ventures)
The Business of Big Data (IA Ventures)
Search Engine,[object Object]
Search Engine,[object Object],Crawl the web,[object Object],Query,[object Object]
Search Engine,[object Object],Crawl the web,[object Object],Computation,[object Object],Query,[object Object],Storage,[object Object]
Storage,[object Object],Computation,[object Object]
Storage,[object Object],Computation,[object Object]
Distributed,[object Object],Storage,[object Object],Computation,[object Object]
Distributed Storage,[object Object],Distributed Computation,[object Object],Map Reduce,[object Object],Big Table,[object Object]
Distributed Storage,[object Object],Distributed Computation,[object Object],Map Reduce,[object Object],Big Table,[object Object],Hadoop,[object Object],HBase,[object Object]
Distributed Storage,[object Object],Distributed Computation,[object Object]
Distributed Storage,[object Object],Distributed Computation,[object Object]
The Business of Big Data (IA Ventures)
So you want to start a big data company?,[object Object]
11101000101001000100101111010101,[object Object],What is a Big Data Company,[object Object],Source Data,[object Object],00101010001010000101010,[object Object],1001011010010100010111,[object Object],Do Smart Stuff,[object Object],Product,[object Object],010100001010010101011111010101010010,[object Object]
Others Data,[object Object],Your Data,[object Object],Data-driven Product,[object Object],Landscape,[object Object],Source of Data,[object Object],DataProduct,[object Object],Final Product,[object Object]
Data Only From Others Platforms,[object Object],Others Data,[object Object],Landscape,[object Object],Source of Data,[object Object],Hybrid,[object Object],Your Data,[object Object],Data Only From Your Platform,[object Object],Data-driven Product,[object Object],DataProduct,[object Object],Final Product,[object Object]
Others Data,[object Object],Landscape,[object Object],Source of Data,[object Object],Your Data,[object Object],Sell Data Directly,[object Object],Sell Insight,[object Object],Sell Product,[object Object],Data-driven Product,[object Object],DataProduct,[object Object],Final Product,[object Object]
Others Data,[object Object],Your Data,[object Object],Data-driven Product,[object Object],Landscape,[object Object],Source of Data,[object Object],DataProduct,[object Object],Final Product,[object Object]
Moats,[object Object]
Competitive Barriers,[object Object]
Competitive Barriers,[object Object],Data Network Effects,[object Object]
Competitive Barriers,[object Object],Data Network Effects,[object Object],Data Economies of Scale,[object Object]
Network Effects,[object Object],Traditionally think of….,[object Object],‘Network graphs’,[object Object]
Network Effects,[object Object],Traditionally think of….,[object Object],‘Network graphs’,[object Object],Marketplaces,[object Object]
Network Effects,[object Object],Within the data eco-system…,[object Object],No network effects,[object Object]
Data Network Effects,[object Object],Within the data eco-system…,[object Object]
Data Network Effects,[object Object],Within the data eco-system…,[object Object]
Economies of Scale,[object Object],Traditionally think of….,[object Object]
Data Economies of Scale,[object Object],Within the data eco-system…,[object Object],5. Increased user engagement and more data,[object Object],1. Product,[object Object],4. Product evolves,[object Object],2. Users,[object Object],3. Data,[object Object]
Data Economies of Scale,[object Object],Within the data eco-system…,[object Object],5. Increased user engagement and more data,[object Object],1. Product,[object Object],4. Product evolves,[object Object],2. Users,[object Object],3. Data,[object Object]
Big Data Team,[object Object]
Big Data Team,[object Object],Data science,[object Object],Scalable distributed architectures ,[object Object]
Big Data Team,[object Object],Data DNA,[object Object]
Thank you!,[object Object],Ben Siscovick,[object Object],@bsiscovick,[object Object],ben@iaventures.com,[object Object],Brad Gillespie,[object Object],@bradgillespie,[object Object],brad@iaventures.com,[object Object]

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The Business of Big Data (IA Ventures)

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Editor's Notes

  1. We’re going to talk about the BUSINESS of big data. We’ll briefly touch upon some of the key technology trends enabling Big Data businesses, but this discussion is really focused on three things:Note: this is in not meant to be the definitive *only* way to think about and conceptualize Big Data, but it is how *we* think about itAlso – we’ve prepared some material, but we’d love for this to be an interactive discussion and not a lecture.
  2. We’re going to talk about the BUSINESS of big data. We’ll briefly touch upon some of the key technology trends enabling Big Data businesses, but this discussion is really focused on three things:Note: this is in not meant to be the definitive *only* way to think about and conceptualize Big Data, but it is how *we* think about itAlso – we’ve prepared some material, but we’d love for this to be an interactive discussion and not a lecture.
  3. We’re going to talk about the BUSINESS of big data. We’ll briefly touch upon some of the key technology trends enabling Big Data businesses, but this discussion is really focused on three things:Note: this is in not meant to be the definitive *only* way to think about and conceptualize Big Data, but it is how *we* think about itAlso – we’ve prepared some material, but we’d love for this to be an interactive discussion and not a lecture.
  4. We’re going to talk about the BUSINESS of big data. We’ll briefly touch upon some of the key technology trends enabling Big Data businesses, but this discussion is really focused on three things:Note: this is in not meant to be the definitive *only* way to think about and conceptualize Big Data, but it is how *we* think about itAlso – we’ve prepared some material, but we’d love for this to be an interactive discussion and not a lecture.
  5. We’re going to talk about the BUSINESS of big data. We’ll briefly touch upon some of the key technology trends enabling Big Data businesses, but this discussion is really focused on three things:Note: this is in not meant to be the definitive *only* way to think about and conceptualize Big Data, but it is how *we* think about itAlso – we’ve prepared some material, but we’d love for this to be an interactive discussion and not a lecture.
  6. We’re going to talk about the BUSINESS of big data. We’ll briefly touch upon some of the key technology trends enabling Big Data businesses, but this discussion is really focused on three things:Note: this is in not meant to be the definitive *only* way to think about and conceptualize Big Data, but it is how *we* think about itAlso – we’ve prepared some material, but we’d love for this to be an interactive discussion and not a lecture.
  7. There is a shit ton of data being created every day and the tools for data input/capture and storage are now ubiquitous and *cheaply* accessible to the masses
  8. Organizations everywhere now realize that there is immense insight and value locked inside of the data, and new infrastructure and approaches to data analysis allow us to unlock that value
  9. This is what’s happened in the last four decades.
  10. These four factors also happen to be inputs for data generation processes.
  11. Sizes that were unimaginable a few years ago are now commonplaceJust storing and accessing the data can be difficultSIZE – MANAGED WITH – STOREDSmall :: Excel, R :: fits in memory on one machineMedium :: indexed files, monolithic DB :: fits on disk on one machineBig :: Hadoop, Distributed DB :: stored across many machinesGenerally - data too big to fit on a disk :: ‘data-center’ scale
  12. Data that is difficult for computers to understand Principal example being natural langauagetext, Images, Video and moreValuable info locked up inside this data (e.g. twitter)
  13. More data coming in fasterDecision windows getting smallerValuable to worthless in a matter of minutes. (seconds … no milliseconds)EX: trading, ad serving
  14. More data coming in fasterDecision windows getting smallerValuable to worthless in a matter of minutes. (seconds … no milliseconds)EX: trading, ad serving
  15. Our thesis:
  16. IA Ventures funding companies that are powering this Big Data revolutionTools & Technologies for managing Big Data
  17. Two primary axis to think about when categorizing Big Data companies
  18. Now that we’ve talked about what a Big Data company is, we can begin to discuss the characteristics of what makes a Big Data company massively successful?The key to almost any massively successful company is creating some sort of sustainable competitive advantage / barrier to entry of competition.When competitive advantages exist, it is *very* difficult for new entrants to provide the same product or service that you provide. This means that your company will enjoy unique position in the market for years to come. By contrast, companies that do not have barriers to entry are susceptible to competitive threats by new entrants that simply out execute them.
  19. There is much discussion about what constitutes sustainable competitive advantage, but I postulate that there are two key advantages to consider with Big Data companies
  20. There are others, but I don’t view them as strong:IP – an exogenous dynamic imposed via legal structure, not something intrinsic to the product or market dynamicSwitching costs – may be a barrier for existing customers, but has little impact on new customers
  21. Network effect = a system where the value to the incremental customer is a direct function of the customers already in the systemNetworks effects are so powerful because they introduce a dynamic that tips towards a winner take all marketMsft, ebay, skype, facebook
  22. data is put into some analytic framework, analyzed and returned. There is no sharing of insight, no making the system smarter
  23. Data is put into some shared framework. Every node in the system benefits by being in the same system. The system gets smarterSome amount insight shared across nodes
  24. Ex. Metamarkets – contributory data modelEx. Billguard – leverages flags across the network
  25. Average unit cost declines as quantity produced increases
  26. Best way to explain is via example (-> go to next slide)Key (with EoS and NE) is it starts with out-execution and then the competitive advantage dynamic takes hold and creates a Virtuous spiral
  27. (Goback to previous slide to explain virtuous spiral)