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

by CEO at Metamarkets on May 25, 2011

  • 13,983 views

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


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2. The Big Data Opportunity
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* 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?


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3-4. Attack of the Exponentials
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* these are something that i call 'attack of exponentials'

* VCs like curves like

[transition]

* 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

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5. Intersection of Three Forces
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* ... 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.")

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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

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

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12. FAST
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* 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

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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

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  • acanhoto Ana Canhoto, Senior Lecturer in Marketing at Oxford Brookes University 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: http://www.slideshare.net/acanhoto/cim-2012-big-data]
    1 year ago
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Building Data Start-Ups:  Fast, Big, and Focused Building Data Start-Ups: Fast, Big, and Focused Presentation Transcript