The Numbers Revolution and its effect on the Web Rich Miller Research Scientist, LexisNexis World Usability Day – 11/13/2008

What we will cover What the numbers revolution is and how it came about How it is affecting the world and the web How it is changing both the user experience and design approaches How it is being manifested in tools and applications

What the numbers revolution is and how it came about

How it is affecting the world and the web

How it is changing both the user experience and design approaches

How it is being manifested in tools and applications

The numbers revolution – what is it? Using data and statistics to better see and affect reality Revolutionary impact due to the maturation of the web and its enabling technologies More data and metadata available – more sensing/measuring Faster networks More powerful displays User interface rendering technologies – e.g. Flash Involves… Capturing and organizing data, often large amounts Crunching data to make predictions Slicing, dicing and visualizing data to aid decision-making and discovery Information overload revisited More information (in the form of metadata) needed to enable the consumption of large amounts of data

Using data and statistics to better see and affect reality

Revolutionary impact due to the maturation of the web and its enabling technologies

More data and metadata available – more sensing/measuring

Faster networks

More powerful displays

User interface rendering technologies – e.g. Flash

Involves…

Capturing and organizing data, often large amounts

Crunching data to make predictions

Slicing, dicing and visualizing data to aid decision-making and discovery

Information overload revisited

More information (in the form of metadata) needed to enable the consumption of large amounts of data

Process and related domains

Hans Rosling and trendalyzer http:// www.youtube.com/watch?v =hVimVzgtD6w Start at 2:15, goto 5:15 Sequel introducing predictive analytics demo

http:// www.youtube.com/watch?v =hVimVzgtD6w

Start at 2:15, goto 5:15

Sequel introducing predictive analytics

demo

How is it changing how people think? Focus thinking on what humans do well and let the computers handle the tough thinking jobs Less guessing and hypothesis-testing, more discovery Predicting based on data and sometimes relegating expertise to asking the right questions Analytics-powered thinking essentially amplifies the main advantage of the internet: information at your fingertips there is no reason that both information and analysis should not be at a user's fingertips

Focus thinking on what humans do well and let the computers handle the tough thinking jobs

Less guessing and hypothesis-testing, more discovery

Predicting based on data and sometimes relegating expertise to asking the right questions

Analytics-powered thinking essentially amplifies the main advantage of the internet: information at your fingertips

there is no reason that both information and analysis should not be at a user's fingertips

Where is it having the most impact? Organization decision-making and strategy Includes business intelligence and CRM Predictive modeling Inform general product/UI design and usability User behavior analytics Navigation Consumption Personalization Feedback and testing e.g. randomized web tests Enable more powerful, analytics-fueled applications Decision-support tools Recommendation systems Interactive visual interfaces to enhance… Consuming content Socializing and collaborating

Organization decision-making and strategy

Includes business intelligence and CRM

Predictive modeling

Inform general product/UI design and usability

User behavior analytics

Navigation

Consumption

Personalization

Feedback and testing

e.g. randomized web tests

Enable more powerful, analytics-fueled applications

Decision-support tools

Recommendation systems

Interactive visual interfaces to enhance…

Consuming content

Socializing and collaborating

Importance of the metadata layer As the amount of data available through the web is continuing to increase, a new layer of metadata is being created around it The metadata layer yields multiple benefits, e.g. framework for organizing data Fuel for UIs personalization individual user productivity and decision-support

As the amount of data available through the web is continuing to increase, a new layer of metadata is being created around it

The metadata layer yields multiple benefits, e.g.

framework for organizing data

Fuel for UIs

personalization

individual user productivity and decision-support

Numbers-related publications

Moneyball , Baseball Abstract Sports geeks as pioneers Evaluating talent Determining strategy 1980s - Bill James Challenged conventional wisdom 1990s – Fantasy Sports The Lonious Monks 2000s – Major League Baseball Moneyball – Oakland A’s Red Sox hire Epstein, James Today – part of the game NBA – e.g. Houston Rockets Science

Sports geeks as pioneers

Evaluating talent

Determining strategy

1980s - Bill James

Challenged conventional wisdom

1990s – Fantasy Sports

The Lonious Monks

2000s – Major League Baseball

Moneyball – Oakland A’s

Red Sox hire Epstein, James

Today – part of the game

NBA – e.g. Houston Rockets Science

Freakonomics Applying economic analysis to understand human behavior Like Bill James, Stephen Levitt challenges conventional wisdom Investigations include… Why do teachers and sumo wrestlers cheat? Why should you be suspicious of your real-estate agent? Why do most crack dealers live with their mothers? Is there a link between abortion and crime rate? Why does good parenting not really affect educational performance? How is a child’s name affected by his/her socioeconomic position? Why it is safer to own a gun than a swimming pool? Freakonomics blog

Applying economic analysis to understand human behavior

Like Bill James, Stephen Levitt challenges conventional wisdom

Investigations include…

Why do teachers and sumo wrestlers cheat?

Why should you be suspicious of your real-estate agent?

Why do most crack dealers live with their mothers?

Is there a link between abortion and crime rate?

Why does good parenting not really affect educational performance?

How is a child’s name affected by his/her socioeconomic position?

Why it is safer to own a gun than a swimming pool?

Freakonomics blog

Super Crunchers , Ian Ayres Ayres = Yale law professor Collaborates with Freakonomics guys Focus on very large datasets and multiple regression analysis Goes beyond BI by combining predictive models with randomized experiments How statistical evidence can supplement/replace human judgement. exposes experts’ limitations in predicting seeking decision-making help from computers should be normal part of business. Topics include medicine, education, business, sports, and winemaking.

Ayres = Yale law professor

Collaborates with Freakonomics guys

Focus on very large datasets and multiple regression analysis

Goes beyond BI by combining predictive models with randomized experiments

How statistical evidence can supplement/replace human judgement.

exposes experts’ limitations in predicting

seeking decision-making help from computers should be normal part of business.

Topics include medicine, education, business, sports, and winemaking.

The Wisdom of Crowds In this case, the numbers are the people in the crowd…the more the better Under certain conditions, the crowd’s decisions are superior to the individual’s 4 conditions necessary diversity of opinion independence of opinions decentralization of power aggregation into a group answer Again, experts exposed as inferior – to crowd in predicting … and “less-bright” folks are essential! Examples include auto traffic behavior disease tracking and treating navigating the internet

In this case, the numbers are the people in the crowd…the more the better

Under certain conditions, the crowd’s decisions are superior to the individual’s

4 conditions necessary

diversity of opinion

independence of opinions

decentralization of power

aggregation into a group answer

Again, experts exposed as inferior – to crowd in predicting

… and “less-bright” folks are essential!

Examples include

auto traffic behavior

disease tracking and treating

navigating the internet

Competing on Analytics How leading companies collect, analyze, and act on data It takes an investment, a plan,and the discipline to stick to it. Applications include… Supply chain, customer relations, pricing, HR, product quality, R&D. Companies include Marriot International Capital One Oakland A’s, Boston Red Sox Procter & Gamble Bottom line Make it a normal part of your business

How leading companies collect, analyze, and act on data

It takes an investment, a plan,and the discipline to stick to it.

Applications include…

Supply chain, customer relations, pricing, HR, product quality, R&D.

Companies include

Marriot International

Capital One

Oakland A’s, Boston Red Sox

Procter & Gamble

Bottom line

Make it a normal part of your business

WIRED: The End of Science “ All models are wrong, but some are useful” – George Box (statistician) “ All models are wrong, and you can do without them” – Peter Norvig (Google) A shift from traditional scientific models to ones based on collecting and analyzing large bodies of data – “the petabyte age” essentially discovering the truth as opposed to making predictions and testing them. Example applications include disease surveillance, farming, physics, legal discovery, news/event monitoring, astronomy, archaeology, airfare analysis, politics/elections, web data management, terrorism insurance. Counterpoint: “If the data wasn't collected to fit the model, the model is probably wrong” – Mark Wasson (LexisNexis) Also, we need a better measure of database size “ information objects” rather than storage space

“ All models are wrong, but some are useful” – George Box (statistician)

“ All models are wrong, and you can do without them” – Peter Norvig (Google)

A shift from traditional scientific models to ones based on collecting and analyzing large bodies of data – “the petabyte age”

essentially discovering the truth as opposed to making predictions and testing them.

Example applications include

disease surveillance, farming, physics, legal discovery, news/event monitoring, astronomy, archaeology, airfare analysis, politics/elections, web data management, terrorism insurance.

Counterpoint: “If the data wasn't collected to fit the model, the model is probably wrong” – Mark Wasson (LexisNexis)

Also, we need a better measure of database size

“ information objects” rather than storage space

Related (but unread by presenter) Similar Wikinomics : How Mass Collaboration Changes Everything The Long Tail : Why the Future of Business is Selling Less of More Differing perspectives Nassim Nicholas Taleb The Black Swan : The Impact of the Highly Improbable Fooled by Randomness : The Hidden Role of Chance in Life and in the Markets

Similar

Wikinomics : How Mass Collaboration Changes Everything

The Long Tail : Why the Future of Business is Selling Less of More

Differing perspectives

Nassim Nicholas Taleb

The Black Swan : The Impact of the Highly Improbable

Fooled by Randomness : The Hidden Role of Chance in Life and in the Markets

How is it affecting business/web? Using numbers to get smarter is quickly becoming a competency necessary to effectively compete Numbers used for improving both… Internal decision-making Quality of products for customers Provides fuel for, and often demands existence of, visualization Giving rise to new wave of tools and apps Business intelligence Charting and social/sharing/collaboration Decision-support Information-seeking and research

Using numbers to get smarter is quickly becoming a competency necessary to effectively compete

Numbers used for improving both…

Internal decision-making

Quality of products for customers

Provides fuel for, and often demands existence of, visualization

Giving rise to new wave of tools and apps

Business intelligence

Charting and social/sharing/collaboration

Decision-support

Information-seeking and research

Implications for product/UI design Opportunities bridging the gap between normal users and complex statistical analysis tools allowing human brain to do what is does best and let the computer play an augmenting role Creating compelling visual information spaces to explore Challenges adding power while preserving UI simplicity Screen space allocation and management avoiding misleading conclusions or data views…pitfalls include small sample sizes inappropriate mapping of data to charts managing data clean-up issues data normalization – consistent label per entity label abbreviation – so they can fit into charts facilitate learning by integrating new tools with familiar tools

Opportunities

bridging the gap between normal users and complex statistical analysis tools

allowing human brain to do what is does best and let the computer play an augmenting role

Creating compelling visual information spaces to explore

Challenges

adding power while preserving UI simplicity

Screen space allocation and management

avoiding misleading conclusions or data views…pitfalls include

small sample sizes

inappropriate mapping of data to charts

managing data clean-up issues

data normalization – consistent label per entity

label abbreviation – so they can fit into charts

facilitate learning by integrating new tools with familiar tools

Best answer vs. best picture The information-seeking UX lies along a continuum from a single answer to an information space Continuum anchored by different views/interfaces Providing best answer – crunching and analyzing data and coming up with the best answer e.g. how old is a person expected to live? Providing a view – organizing and rendering data in a way for users to best understand it and use it. e.g. voting patterns by county in Ohio The continuum reflects the degree of interaction a user has with an application i.e. the broader the answer space, the more the user needs control over manipulating a view of the space

The information-seeking UX lies along a continuum from a single answer to an information space

Continuum anchored by different views/interfaces

Providing best answer – crunching and analyzing data and coming up with the best answer

e.g. how old is a person expected to live?

Providing a view – organizing and rendering data in a way for users to best understand it and use it.

e.g. voting patterns by county in Ohio

The continuum reflects the degree of interaction a user has with an application

i.e. the broader the answer space, the more the user needs control over manipulating a view of the space

UI-related technologies/approaches Advances in visualization and infographics 2 fields merging as computer graphics improve New York Times setting the pace Rich internet applications (RIAs) Provide virtually unlimited rendering of graphics and visualization Large displays and new pointing devices

Advances in visualization and infographics

2 fields merging as computer graphics improve

New York Times setting the pace

Rich internet applications (RIAs)

Provide virtually unlimited rendering of graphics and visualization

Large displays and new pointing devices

Application landscape

BI visualization and dashboards Example = Tableau Desktop

Ian Ayres prediction tools Featured in Super Crunchers Predict the Value of Bordeaux (Ayres - may require free JAVA download ) Personal/Family Predict how long your marriage will last (Political Calculations) Predict Your Child's Adult Height (University of Saskatchewan) Consumer Applications Predict the Market Value of Your Home (Zillow) Fun/Sports (and gambling?) Predict NFL game (NFL Picker) Politics/Government Predict Where Your Tax Money Is Spent (Tax Break Down) Media Predict Demographics of Who Will Use a Webpage (Microsoft adCenter) Health Predict Your Liklihood of Illness or Disability (Northwestern Mutual) Money/Business Predict whether a publicly traded company will file for bankruptcy (Political Calculations) Macroeconomic Predict the odds of a U.S. recession in the next 12 months (Political Calculations)

Featured in Super Crunchers

Predict the Value of Bordeaux (Ayres - may require free JAVA download )

Personal/Family

Predict how long your marriage will last (Political Calculations)

Predict Your Child's Adult Height (University of Saskatchewan)

Consumer Applications

Predict the Market Value of Your Home (Zillow)

Fun/Sports (and gambling?)

Predict NFL game (NFL Picker)

Politics/Government

Predict Where Your Tax Money Is Spent (Tax Break Down)

Media

Predict Demographics of Who Will Use a Webpage (Microsoft adCenter)

Health

Predict Your Liklihood of Illness or Disability (Northwestern Mutual)

Money/Business

Predict whether a publicly traded company will file for bankruptcy (Political Calculations)

Macroeconomic

Predict the odds of a U.S. recession in the next 12 months (Political Calculations)

Predictive Analytics - SAS

Small-scale decision-support - visual i|o Should the pitcher be replaced?

Fantasy sports decision-support CBS Sportsline Fantasy Basketball The outcome of my decisions Who should I start? Who should I add to my team? What moves are others making?

Infographics - Catalogtree.net Candidate for a geo view

Infographics – weighted electoral map

Infographics-visualization convergence New York Times

Hans Rosling and trendalyzer http:// www.youtube.com/watch?v =hVimVzgtD6w Start at 2:15, goto 5:15 sequel demo

http:// www.youtube.com/watch?v =hVimVzgtD6w

Start at 2:15, goto 5:15

sequel

demo

Info space browsers - Bestiario

Data+viz sharing - Many-eyes Powers New York Times Visualization Lab example

Data+viz sharing - Swivel

Data+viz sharing - lastfm

Professional info-seeking - LexisNexis LNIS - volume of European news coverage of US Banks over time Total Patent # of patents over time by authority/country Courtlink Strategic Profiles - types of matters handled by law firm

Pro info-seeking - ILOG/Elixir CIA World Factbook

Consumer info-seeking – Stamen viz trulia See document: trulia oreilly post on trulia map See document: stamens-map- for.html adobe tour of california RIA See document: adobe digg labs See document: digg global business network See document: gbn root markets See document: root

trulia

See document: trulia

oreilly post on trulia map

See document: stamens-map- for.html

adobe

tour of california RIA

See document: adobe

digg labs

See document: digg

global business network

See document: gbn

root markets

See document: root

What about usability in transportation? A ripe area for using numbers to better view and affect reality Applications Traffic analysis – optimizing traffic efficiency Public transportation planning Trip planning Monitoring vessels/vehicles – intelisea Geo-maps with info layers GPS location trackers – e.g. where is the family car?

A ripe area for using numbers to better view and affect reality

Applications

Traffic analysis – optimizing traffic efficiency

Public transportation planning

Trip planning

Monitoring vessels/vehicles – intelisea

Geo-maps with info layers

GPS location trackers – e.g. where is the family car?

Recommendations for UX pros Acknowledge and discover your newly expanded solution space Seek out and use number-fueled apps Understand user cognitive limits … and provide tools for tasks they need help with View visualization design as an extension of UI design Get a handle on data cleanup and formatting Embrace RIAs and the tools to make them

Acknowledge and discover your newly expanded solution space

Seek out and use number-fueled apps

Understand user cognitive limits

… and provide tools for tasks they need help with

View visualization design as an extension of UI design

Get a handle on data cleanup and formatting

Embrace RIAs and the tools to make them

Predictions for 2009 and beyond Mining and analyzing data for intelligence becomes common practice for more companies RIA technologies gain momentum Visualization continues to proliferate The analytics shakeout begins as users discover which numbers are not as useful Broken Social Scene – social apps will get hit the hardest Decision-support apps hit the mainstream Increased emphasis on data cleanup Companies with nimble data better compete The world becomes smarter, more objective

Mining and analyzing data for intelligence becomes common practice for more companies

RIA technologies gain momentum

Visualization continues to proliferate

The analytics shakeout begins as users discover which numbers are not as useful

Broken Social Scene – social apps will get hit the hardest

Decision-support apps hit the mainstream

Increased emphasis on data cleanup

Companies with nimble data better compete

The world becomes smarter, more objective

Questions and related resources If we run out of time for questions, feel free to contact me… in person later today [email_address] [email_address] Previous public talks WUD 2007 talk on web applications MVCS talk on Web 2.0 SOASIS talk on Visual Thinking Miami-IMS talk on Trends in HCI

If we run out of time for questions, feel free to contact me…

in person later today

[email_address]

[email_address]

Previous public talks

WUD 2007 talk on web applications

MVCS talk on Web 2.0

SOASIS talk on Visual Thinking

Miami-IMS talk on Trends in HCI