2011 SBS Singapore | Nicholas Gruen, The Coming Revolution in Data

The growing revolution in Data: A presentation to the Social Business Summit Nicholas Gruen Chairman, Kaggle Chairman, Government 2.0 Taskforce, E [email_address] T @nicholasgruen Singapore, 6 th April, 2011

Outline
The changing landscape
and what’s behind it
The ecology of data
Finding the people to find the value in your data
Kaggle
From data inside to data outside
Data and gamification
The Gruen Tender

Data can turn things upside down
Insurance
Retail
Banking
Telecommunications
Accommodation
Aviation and transport
From stand by to advance purchase
load optimisation, price discrimination and risk sharing
Medicine

All That Data… 3 years of historical data for comparison 10 x 750 x 50 x 52 x 3 = 58,500,000 data points 4 regions to segregate the data 10 x 750 x 50 x 52 x 3 x 7 x 4 = 1,638,000,000 data points 50 states to segregate the data 10 x 750 x 50 x 52 x 3 x 7 x 4 x 50 = 81,900,000,000 data points 7 types of data to monitor (POS, Inventory, Marketing, Syndicated, etc) 10 x 750 x 50 x 52 x 3 x 7 = 409,500,000 data points 8 categories to aggregate the data 10 x 750 x 50 x 52 x 3 x 7 x 4 x 50 x 8 = 655,200,000,000 data points 10 Retailers to monitor 10 data points 750 Stores per retailer to monitor 10 x 750 = 7500 data points 50 products per store to monitor 10 x 750 x 50 = 375,000 data points 52 weeks of data per year for trend analysis 10 x 750 x 50 x 52 = 19,500,000 data points 655 Billion+ data points involved with managing the retail sales channel Source: Marilyn and Terence Craig @ Strataconf

http://www.dlib.org/dlib/may09/mestl/05mestl.html

He who receives an idea from me, receives instruction himself without lessening mine; as he who lights his taper at mine, receives light without darkening me. That ideas should freely spread from one to another over the globe, for the moral and mutual instruction of man, and improvement of his condition, seems to have been peculiarly and benevolently designed by nature, when she made them, like fire, expansible over all space, without lessening their density in any point, and like the air in which we breathe, move, and have our physical being, incapable of confinement or exclusive appropriation.
Thomas Jefferson to Isaac McPherson, August, 1813
Jefferson’s enlightenment dream

Public goods Public goods – goods that no-one will supply if the government doesn’t Public goods . . . present serious problems in human organisation. Vincent and Elenor Ostrom - 1977

The Wealth of Nations (1776)
Private Goods
The Theory of
Moral Sentiments (1759)
The social preconditions of markets (Public Goods)
Language Adam Smith

Public Goods Private Goods [The public good of] Justice . . . is the main pillar that upholds the whole edifice. If it is removed, the great, the immense fabric of human society . . . must in a moment crumble into atoms. Adam Smith

From potential to actual public good

Web 2.0: explosion of emergent public goods
Web 2.0 platforms are public goods:
Google (1998)
Wikipedia (2001)
Blogs (early 2000s)
Facebook (2004)
Twitter (2006)
Government didn’t build any of them
These platforms generate data
By creating a context in which it means something
And so inducing us to produce it

The economics of abundance: a new birth of ‘free’dom Public goods . . . present serious problems in human organisation. Vincent and Elenor Ostrom - 1977 The freedom of ideas is the liberation of our species Public goods as a problem Public goods as an opportunity

The ecology of data Data Schema or Context Information An example from Web 2.0 …

Private goods => Public Goods: Software
Private Goods
Meeting private needs
Public Goods
Many eyeballs
Free code

Making sense of data
Release and the sense makers will come
Make sense of the data for your community
And you may be able to monetise it
Whole businesses being built on data exhaust
Find the people to analyse your data
Kaggle

FlightCaster
Predicts flight delays.
We use an advanced algorithm that scours data on every domestic flight for the past 10-years and matches it to real-time conditions. We help you evaluate alternative options and help connect you to the right person to make the change.
FlightCaster uses data from:
Bureau of Transportation Statistics
FAA Air Traffic Control System Command Center
FlightStats
National Weather Service

Private goods => Public Goods: Data
Private Goods
Meeting private needs
Linking to other websites
Public Goods
Google uses this information to rank sites
Everyone benefits
Google monetises with ads

Private Goods
Platform for recording data
Public Goods
PLM aggregates data and shares it back as public and private goods
Sales of data

Data exhaust

Where’s Wally?

Global Competitions State of the art 70% 1½ weeks 70.8% Competition closes 77% Predicting HIV viral load Accuracy of Prediction (1 – 100%)
Revenue or sales forecasts
Traffic forecasting
Energy demand
Predicting crime
Tax/social security fraud
Hospital casualty demand
Identifying great
Teachers
Schools
Hospitals
and their best practices
US$500

We could not be happier with the result. The Kaggle approach has set a new benchmark in Government for the development of successful predictive models, delivered quickly and very cost effectively. In particular, the flexibility of the winning predictive model will enable its application to other major transport routes to the CBD and allow for the addition of other factors such as weather and incident. Susan Calvert Director, Strategy and Project Delivery Unit

Forecast Eurovision Voting Dr. Derek Gatherer, UK Take on the Quants 1 & 2 John Blatz Baltimore Edmund & Adrian London & USA Jason Trigg Pennsylvania Chih-Li Sung & Roy Tseng Penghu & Taipei Jure Zbontar Ljubljana Thomas Mahony Canberra Emir Delic Australia Glen Maher Canberra Predict HIV Chris Raimondi Batimore Claudio Perlich USA Gzegorz Swiszcz Gera Edmund & Adrian London & USA Tourism Forecasting Part 1 Rajstennaj Barrabas USA Jason Trigg Pennsylvania Felipe Maia Uppsala University Lee Baker Las Cruces, New Mexico INFORMS Cole Harris Texas Nan Zhou Pittsburgh Chess Ratings Uri Blass Tel-Aviv Giuseppe Ragusa Rome Robert Warsaw Tourism Forecasting Part 2 R Package Recommendation Engine Ivan Russian Federation The top 3 competitors for: Philipp Emanuel Widmann Heidelberg, DE Dr. Christopher Hefele, New York Chris DuBois Portland Where’s Wally? Where’s Jeremy? Chris Raimondi Baltimore Felipe Maia Uppsala University Jeremy Howard

Where’s Wally from?

What are Wally’s Qualifications

Rebuilding an info-structure
Global CrisisCommons
Within 2 hours of #CCearthquake
Global volunteers parse 300,000 tweets.
“ Shell 58 Barrack Rd out of petrol – only diesel ”.
Agencies fussed, helped and obstructed.
Kaggle comp to triage tweets

New routines to generate data Real estate or other sales Indicated Service provider

Medical procedure Indicated Service provider

Litigation Indicated Service provider

Gruen Tenders
Forward looking data
Tailored to the specific case at hand
Enables innovation and data capture
Generate a mass of new data
Compares like with like
Minimises perverse incentives

E [email_address] T @nicholasgruen

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2011 SBS Singapore | Nicholas Gruen, The Coming Revolution in Data

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2011 SBS Singapore | Nicholas Gruen, The Coming Revolution in Data — Presentation Transcript

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