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Building Data Start-Ups: Fast, Big, and Focused


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1. Building Data Start-ups: Fast, Big, and Focused

* 2 parts today:

(i) forces behind big data opportunity
(ii) big data stack and how to compete with in

* building a data start-up is a bit like Sumo Wrestling

* data is heavy, has weight - we need agile strategies to succeed

* today: talk about opportunities for data, strategies for success

* in a nutshell: data start-ups must be fast, big, and focused

2. The Big Data Opportunity

* it's a cliche by now: there is a mountain of data in this world

* understanding these forces is critical to data start-up's strategy

<transition>: what are some of the tectonic forces at work?

3-4. Attack of the Exponentials

* these are something that i call 'attack of exponentials'

* VCs like curves like


* in the past few decades, the cost of storage, CPU, and bandwidth has been exponentially dropping, while network access has shot up

* in 1980, a terabyte of storage cost $14 MILLION - today it's $47 dollars

<transition>: exponential economics, together with two other forces

5. Intersection of Three Forces

* ... form the inputs to this massive increase in data, the data singularity

* sensor networks the phones, GPS devices, laptops, and instrumented spimes

* cloud computing has democratized and made computing power & storage a utility

( "even if it turns out that the cloud is actually just some place in Virginia.")

6-7. Data Value Must Exceed Data Cost

* the laws of economics have not changed: value must exceed cost

* the upper left side of this graph shows data whose value exceeded
its cost of collecting, storing, and computing over a decade ago

* the human genome data cost $3 billion (in 2000)

[shift slide]

* but as the tide shifts, new classes of data are revealed as being valuable

* the dog genome cost only $30 million (in 2005)

* web log data used to be tossed; now it's cheap enough to collect,
store, and compute over

* i encourage all of you, think of a data source that was previously
not collected, or not kept around, and mull the possibilities

<transition>: with that, i would like to now talk about the emerging stack,
and the strategies for being successful within it

8-9, 10-11. Success on the Data Stack

* here is my vision of the emerging big data stack

* at bottom is data - persistence layer - databases - the brawn

* in the middle is analytics - the intelligence layer

* at the top - services, what you all the brains and brawn

[ transitions in quite succession ]

* I argue that data start-ups, to succeed, must have

== FAST data, BIG analytics, and FOCUSED services ==

* let's take each of these in turn,
exploring the competitive axes at each layer
starting from the bottom of the stack, data

12. FAST

* as I said before, data is heavy

* being able to move big data quickly is key

* let's pull the data layer out of the stack & examine it

13. Fast Data

* so we have the two competitive axes on the data layer

* the first axis is scale: for data, the scaling issue has been solved.

* Hadoop

Published in: Technology
  • Thank you for sharing this presentation - I like that you went beyond saying what big data is and what it is used for, and talked about why we have big data and why it is useful.

    FYI, I cited this slide set in a presentation I prepared - an introduction to big data for marketers [available here:]
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Building Data Start-Ups: Fast, Big, and Focused

  1. 1. Building Data Start-ups:<br />Fast, Big, and Focused<br />Michael E. Driscoll, CTO, Metamarkets<br />@medriscoll<br />O’Reilly Strata Online | May 25, 2011<br />
  2. 2. The Big Data <br />Opportunity<br />
  3. 3. The Attack of the Exponentials<br />
  4. 4. The Attack of the Exponentials<br />
  5. 5. The Intersection of Three Forces<br />Yields Higher Volume & Velocity of Data<br />exponential economics<br />sensor networks<br />cloud computing<br />
  6. 6. Data Value Must Exceed Data Cost<br />
  7. 7. Data Value Must Exceed Data Cost<br />... New Classes of Data are Now Valuable<br />
  8. 8. Success on the Data Stack<br />Services<br />Analytics<br />Data<br />
  9. 9. Success on the Data Stack<br />Fast<br />Services<br />Analytics<br />Fast<br />Data<br />
  10. 10. Success on the Data Stack<br />Fast, Big<br />Services<br />Big<br />Analytics<br />Fast<br />Data<br />
  11. 11. Success on the Data Stack<br />Fast, Big, and Focused<br />Focused<br />Services<br />Big<br />Analytics<br />Fast<br />Data<br />
  12. 12. #1: Fast<br />
  13. 13. Success on the Data Stack<br />Fast Data<br />real-time<br />Kdb<br />Netezza<br />Esper<br />Vertica<br />MongoDB<br />speed<br />InfoBright<br />Aster<br />MySQL<br />MapR<br />Greenplum<br />Postgres<br />batch<br />Hadoop<br />Services<br />megabytes<br />petabytes<br />scale<br />Analytics<br />free, open-source<br />Data<br />commercial<br />
  14. 14. Fast Data With Cheap Memory<br />1964 – Univac 2k<br />$51 million/MB<br />2011 – DDR 1GB<br />1 cent/MB<br />data sources: &, plotted with ggplot2<br />
  15. 15. #2: Big<br />
  16. 16. Success on the Data Stack<br />Big Analytics<br />custom<br />(hardware)<br />real-time<br />speed<br />Revolution R<br />R<br />custom <br />distributed<br />SAP<br />SAS<br />SciPy<br />SPSS<br />batch<br />Services<br />megabytes<br />petabytes<br />scale<br />Analytics<br />free, open-source<br />Data<br />commercial<br />
  17. 17. The Promise ofAnalytics<br />extract<br />learn<br />predict<br />DATA<br />FEATURES<br />MODELS<br />“More data usually beats better algorithms.”<br />
  18. 18. #3: Focused<br />
  19. 19. Success on the Data Stack<br />Focused Services<br />Focused<br />Services<br />Analytics<br />Data<br />
  20. 20. “Real-time, large-scale analytics in a focused vertical.”<br />credit: Joe Reisinger, Metamarkets<br />
  21. 21. Success on the Data Stack<br />Fast, Big, and Focused<br />Focused<br />Services<br />Big<br />Analytics<br />Fast<br />Data<br />
  22. 22. Thank You. Questions?<br />Michael E. Driscoll, CTO, Metamarkets<br />@medriscoll<br />O’Reilly Strata Online | May 25, 2011<br />