KDD-07 Invited Innovation Talk

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August 12, 2007KDD-07 Invited Innovation Talk

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Usama Fayyad, Ph.D.Chief Data Officer & Executive VP

Yahoo! Inc.

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Thanks and Gratitude

• My family: my wife Kristina and my 4 kids; my parents and my sisters

• My academic roots: The University of Michigan, Ann Arbor – my Ph.D. committee, including Ramasamy Uthurusamy (then at GM Research Labs), grad student colleagues (Jie Cheng), Internships at GM Research and at NASA’s JPL

• My Mentors and Collaborators– Caltech Astronomy (G. Djorgovski, Nick Weir), Pietro Perona and M.C. Burl

– JPLNASA Colleagues: Padhraic Smyth, Rich Doyle, Steve Chien, Paul Stolorz, Peter Cheeseman, David Atkinson, many others…

– Microsoft Colleagues: Decision Theory Group, Surajit Chadhuri, Jim Gray, Paul Bradley, Bassel Ojjeh, Nick Besbeas, Heikki Mannila, Rick Rashid, many others

– Fellows in KDD: Gregpry Piatetsky-Shapiro, Daryl Pregibon, Christos Faloutsos, Geoff Webb, Bob Grossman, Jiawei Han, Eric Tsui, Tharam Dillon, Chengqi Zhang, many, many colleagues

• My Business Partners– Bassel Ojjeh, Nick Besbeas, many VC’s, many advisers and strategic clients including

Microsoft SQL Server and sales teams

• My Yahoo! Colleagues:– Zod Nazem, Jerry Yang, David Filo, Yahoo! exec team, Prabhakar Raghavan, Pavel Berkhin,

Nick Weir, Hunter Madsen, Nitin Sharma, Raghu Ramakrishnan, Y! Research folks, many at Yahoo SDS and current and previous Yahoo! employees

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Personal Observations of a Data Mining Disciple

A Data Miner’s Story – Getting to Know the Grand Challenges

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Usama Fayyad, Ph.D.Chief Data Officer & Executive VP

Yahoo! Inc.

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Overview

• The setting

• Why data mining is a must?

• Why data mining is not happening?

• A Data Miner’s Story– Grand Challenges: Pragmatic

– Grand Challenges: Technical

– Some case studies

• Concluding Remarks

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The data gap…

• The Machinery Moves on:– Moore’s law: processing “capacity” doubles every 18 months : CPU,

cache, memory– It’s more aggressive cousin: Disk storage “capacity” doubles every 9

months

• The Demand is exploding:– Every business is an eBusiness

– Scientific Instruments and Moore’s law

– Government

• The Internet – the ubiquity of the Web

• The Talent Shortage

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What is Data Mining?

Finding interesting structure in data• Structure: refers to statistical patterns, predictive

models, hidden relationships

• Interesting: ?

• Examples of tasks addressed by Data Mining

– Predictive Modeling (classification, regression)

– Segmentation (Data Clustering )

– Affinity (Summarization)• relations between fields, associations, visualization

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Beyond Data Analysis

• Scaling analysis to large databases– How to deal with data without having to move it out?

– Are there abstract primitive accesses to the data, in database systems, that can provide mining algorithms with the information to drive the search for patterns?

– How do we minimize--or sometimes even avoid--having to scan the large database in its entirety?

• Automated search – Enumerate and create numerous hypotheses

– Fast search

– Useful data reductions

• More emphasis on understandable models– Finding patterns and models that are “interesting” or “novel” to

users.

• Scaling to high-dimensional data and models.

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Data Mining and Databases

Many interesting analysis queries are difficult to state precisely

• Examples:– which records represent fraudulent transactions?

– which households are likely to prefer a Ford over a Toyota?

– Who’s a good credit risk in my customer DB?

• Yet database contains the information – good/bad customer, profitability

– did/did not respond to mailout/survey/...

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ACME CORP ULTIMATE DATA MINING BROWSER

Data Mining Grand Vision

What’s New? What’s Interesting?

Predict for me

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The myths…

• Companies have built up some large and impressive data warehouses

• Data mining is pervasive nowadays

• Large corporations know how to do it

• There are tools and applications that discover valuable information in enterprise databases

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The truths…

• Data is a shambles, – most data mining efforts end up not benefiting

from existing data infra-structure

• Corporations care a lot about data, and are obsessed with customer behavior and understanding it

• They talk a lot about it…

• An extremely small number of businesses are successfully mining data

• The successful efforts are “one-of”, “lucky strikes”

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• Data navigation, exploration, & exploitation technology is fairly primitive:– we know how to build massive data stores

– we do not know how to exploit them

– we do the book-keeping really well (OLTP)

– Inadequate basic understanding of navigation /systems

• many large data stores are write-only (= data tomb)

Ancient Egypt

Current state of Databases

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A Data Miner’s Story

• Started out in pure research– Professional student

– Math and algorithms

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Researcher view

Algorithms andTheory

Database

Systems

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Practitioner view

Systems and integration

Database

Algorithms

Customer

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Business view

Systems

Database

Algorithms

Customer

$$$’s

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• Started out in pure research

• At NASA-JPL did basic research and applied techniques to Science Data Analysis problems– Worked with top scientists is several fields: astronomy,

planetary geology, atmospherics, space science, remote sensing imagery

– Great results, strong group, lots of funding, high demand…

• So why move to Microsoft Research?

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Example: Cataloging Sky Objects

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Data Mining Based Solution

• 94% accuracy in recognizing sky objects

• Speed up catalog generation by one to two orders of magnitude (unrealistic to perform manually).

• Classify objects that are at least one magnitude fainter than catalogs to-date.

• Tripled the “data yield”

• Generate sky catalogs with much richer content:

– on order of billions of objects:

> 2x107 galaxies > 2x108 stars, 105 quasars

• Discovered new quasars 40 times more efficiently

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• At NASA-JPL

• At Microsoft Research– Basic research in algorithms and scalability

– Began to worry about building products and integrating with database server

– Two groups established: research and product

• So why move out to a start-up?

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Working with Large Databases

• One scan (or less) of the database– terminate early if appropriate

• Work within confines of a given limited RAM buffer– Cluster a Gigabyte or Terabyte in, say 10 or 100

Megabytes RAM

• “Anytime” algorithm– best answer always handy

• Pause/resume enabled, incremental• Operate on forward-only cursor over a view

(essentially a data stream)

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Business Challenges

Conversion

Retention

Acquisition

Loyalty

Average Order

Technologies

Segmentation

Logistic Regressions

Genetic Algorithms

Decision Trees

Chaid

CART

OLAP

Bayesian Networks

Neural Networks

Business Results Gap

Business Challenges

Conversion

Retention

Acquisition

Loyalty

Average Order

Technical Tools

Business users are unable to apply the power of existing data mining tools to achieve results

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Business Challenges

Conversion

Retention

Acquisition

Loyalty

Average Order

TechnologiesSpecialists

Statisticians

DBAs

Consultants

Data Mining PhDsSegmentation

Logistic Regressions

Genetic Algorithms

Decision Trees

Chaid

CART

OLAP

Bayesian Networks

Neural Networks

Business Results Gap

Business Challenges

Conversion

Retention

Acquisition

Loyalty

Average Order

Technical Tools

Business users are unable to apply the power of existing data mining tools to achieve results

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Evolving Data Mining

• Evolution on the technical front: – New algorithms

– Embedded applications

– Make the analyst life easier

• Evolution on the usability front– New metaphors

– Vertical applications embedding

– Used by the business user

• In both cases, success means invisibility…

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Grand Challenges

• Pragmatic:– Achieving integration and invisibility

• Research/Technical:– Solving some serious unaddressed problems

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Pragmatic Grand Challenge 1

Where is the data?• There is a glut of stored data

• Very little of that data is ready for mining

• Data warehousing has proven that it will not solve the problem for us

• Solution: – integration with operational systems

– Take a serious database approach to solving the storage management problem

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digiMine Background

Started as Venture Capital-funded company: digiMine, Inc. in March 2000.

Built, operated and hosted data warehouses with built-in data mining apps

• Headquartered in Bellevue, Washington

• $45 million in funding – Mayfield, Mohr Davidow, American Express, Deutsche Bank

• Grew to over 120 employees

• 50 patents+ in technology and processes

• Both technology and services

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Sample Customers

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• At NASA-JPL

• At Microsoft Research

• At digiMine– Lots of VC funding, great team, great press coverage,

and fast moving

– great customers

• So why move to a DMX Group?

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Why DMX Group?

• At digiMine, we grew a large “Professional Services” organization

• We learned a lot from these engagements

• VC-funded companies cannot do much consulting

• A fork in the road appeared…– digiMine re-focused on a market vertical: behavioral targeting

for media and publishers

– Renamed to Revenue Science, Inc.

• Formed DMX Group… which was eventually acquired by Yahoo!

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DMX Group Mission

• Make enterprise data a working asset in the enterprise:– Data strategy for the business

– Implementation of Business Intelligence and data mining capabilities

– Business issues around data• What is possible?

• How to expose it to business users

• How to train people and change processes

– Integration with operational systems

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Data Strategy

• How can your data influence your revenues?

• How do you optimize operations based on data?

• How do you increase customer retention based on data?

• How do you utilize enterprise data assets to spot new opportunities:– Cross-sell to existing customers

– Grow new markets

– Avoid problems such as fraud, abuse, churn, etc?

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• At NASA-JPL


• At digiMine/Revenue Science Inc.

• At DMX Group…

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Embedding within Operational Systems• We all worry about algorithms, they are fascinating

• Most of us know that data mining in practice is mostly data prep work

• Go where the data is when the data does not come to you

• But how much of the problem is “data mining”?

• facts: – The effort in embedding an application is huge, and often not

discussed

– Without it, all the algorithms are useless

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Churn Modelling and PredictionCase Study – Wireless Telco

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Modeling Process

Customer Interactio

n Base11

SMS WAP CDR Billing

Sample Databas

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Build Churn Model

22 33 High RiskScore

Database

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Assign Customer Value

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Med Risk

Low Risk

High Val

Med Val

Low Val

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High Val High Risk

High Val Med Risk

High Val Low Risk

Med Val High Risk

Med Val Med Risk

Med Val Low Risk

Low Val High Risk

Low Val Med Risk

Low Val Low Risk

Risk

Valu e

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LTV and Its Application

• A customer’s life-time value (LTV) is the net value that a customer brings in to a business by the end of their service. I.e. their profit contribution.

• LTV allows decisions for individual customers that optimize the return-on-investment (ROI). Examples:– Aggressive retention programs, such as equipment

upgrade and contract renewal for high LTV.

– Differentiated customer care treatment for reactivations by customer with low LTV

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What is the Required?

• Detailed data– Integration of CDR, WIG, SMS, Billing – Maintained at detailed level

• Integrated data mining – Algorithms tuned to model thousands of variables and millions of

rows– Accurate Forecasts

• System Robustness– Massively scalable back end system – Flexible architecture to create new variables quickly and easily

• Collaborative Service Model– Service model which guarantees success– Combined IQ Model to optimize science and business knowledge– Low cost to create and maintain models

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Map Segments to Actions

High

Low

Low High

Nurture /Maintain

Aggressively

Defend

CautiouslyDefend

Grow Margin

ChangeBad

Behavior

Let them go

Equipment

UpgradeFeature Add

ContractRenewal

Save Program

Elite Program

Loyalty ProgramsFeature Use

PlanMigration

Cost ReducingPrograms

ChurnProbability

Forecasted LTVNegativ

e

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Cost Rules Applied…

Cost Rules are introduced to define scoring

For Example:

– Network System Usage Cost

– Mobile to Land Connections Costs

– Technical Operations/Support Costs

– Long Distance Costs

– Inter-Carrier /International subsidy costs

– Roaming Costs

– Bad Debt Allocation

– Many others…

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Cost Rules for a Bank?

Cost Rules are introduced to define value

For Example:

– Deposit Value

– Product mix

– Average. daily balance

– Monthly service fees

– Technical operations/Support costs

– Branch/teller usage

– Late payment/Overdraft history

– Interest rate

– Contract term

– Credit Score

– Employment history/Income

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Integrating domain knowledge• Data mining algorithms are knowledge free

• There is no notion of “common sense reasoning”

• Do we have to solve an AI-hard problem?

• Robust and deep domain knowledge utilization

• solution: – Very deep and very narrow integration

– Ability to “model” business strategy

– Reasoning capability just evolves (c.f. chess players)

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Cross-Sell / Up-Sell Example

Any Related

Products

Complete the Assortment

Help Me Decide

Customer looking for pants

Recommendations

CollaborativeFiltering

Impulse BuyComplement Add-on

Alternates Up Sells

Context SensitiveApproach

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Managing and maintaining models• When was the last time you thought about the lifetime of a

mining model

• What happens when a model is changed

• Have you tried to merge the results of two different clustering models over time?

• How many “data droppings” (aka temp files, quick transformations, quick fixes) do you generate in an analysis session?

• A framework for managing, updating, and retiring mining models

• solution: use techniques that have been invented for this, databases, systems mngmt, s/w engr, etc…

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Effectiveness Measurement• How do we measure [honestly] the effectiveness of a model in a

context?

• Return on Investment (ROI) measurement

• Evaluation in the context of the application

• A framework and methodology for measurement and evaluation– Build the measurement method as part of the design of the

model

– An engineering recipe for measurements, and a set of metrics

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Technical Challenges

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0. Public benchmark data sets• As a field we have failed to define a common data collection• Very difficult to judge research and systems advances• Not an easy task, but not impossible• A mix of

– synthetic (but realistic) data sets – and real datasets

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1. How does the data grow?• A theory for how large data sets get to be large

• Definitely not IID sampling from a static distribution

• Inappropriateness of a “single-population” model

2. Complexity/understandability tradeoff• Explaining how, when and why a model works• Explaining when a model fails• A “Tuning Dial” for reducing the complex into the

understandable

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3. Interestingness• What is an “interesting” pattern or summary?

• How do you measure “novelty”?

• What is “unusual”? When is it worthy of attention?

• Is it low probability events? High summarization ability? Outliers? Good fits? Bad fits?

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4. ScalabilityBeyond just dealing with a large data set: • Principled feature reduction: what is SVD equivalent? Graceful degradation with dimensionality• Uncovering graphical structure in data

– Communities, relations, link analysis, …• Dealing with multiple data types:

– Structured, sparse, dense, text, images, video, audio, sequence data, etc.– I have yet to see an algorithm that deals with more than one type.

• Integration with DBMS– Appropriate sampling– Appropriate operator abstractions

• Taking care of “minor details”– Initialization?– Determining k

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5. A theory for what we do• What are the fundamental abstractions?

• What are the basics operations? What are the basic components of an algorithm?

• What is it that we are optimizing?

• What is hard? What is doable? Why?

• What is a “data summary”?

• When are two attributes “similar”? Can you measure efficiently?

• How do we extract the right representation?

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A new theory is needed

• What are the fundamental problems?

• What do partial models or summaries of data really mean?

• What are the implications of post hoc data analysis? When is it/is it not reasonable to conclude a task is appropriate?

• A new algebra for dealing with highly-summarized views of the world

• Effect of sparse spaces on dimensionality. What is the true dimensionality of data? What are the limits?

• A theory for adaptive sampling

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Pragmatic and Technical Grand ChallengesSummary

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Challenges

1. Where’s the Data?

2. In Situ mining

3. Domain knowledge

4. Life-cycle maintenance

5. Metrics

1. Understanding “large”

2. Simplicity knob

3. Interestingness

4. Scalability

5. Theory of what we do

0. Public and challenging benchmark data sets

A Scorecard for the field: At least 2 advances in the next 10 years!!!

Pragmatic Technical

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ACME CORP ULTIMATE DATA MINING BROWSER

Data Mining Grand Vision

What’s New? What’s Interesting?

Predict for me

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In the meantime, there is an understanding gap

• The technical community speaks of tech problems

• The business strategic thinking hit an “understandability wall”

• Traditionally, the thinking of business strategy never included data

• A new generation of business challenges are born

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Data Strategy

• Is the mapping of the capabilities enabled by data in driving the business

• The Integration of data-driven capabilities in revenue-driving activities

• The Integration of data-derived metrics to feedback into the measurement of the success of the business

• Evolving to an operational state where planning includes data, measurability, and data-driven feedback loops

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• At NASA-JPL


• At digiMine/Revenue Science Inc.

• At DMX Group

• So why join Yahoo! ?

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Evolving the Data Strategy as Chief Data OfficerYahoo! Case Study

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Yahoo! is the #1 Destination on the Web

73% of the U.S. Internet population uses Yahoo!

– About 500 million users per month globally!

• Global network of content, commerce, media, search and access products

• 100+ properties including mail, TV, news, shopping, finance, autos, travel, games, movies, health, etc.

• 25 terabytes of data collected each day… and growing

• Representing thousands of cataloged consumer behaviors

More people visited Yahoo! in the past month than:

• Use coupons• Vote• Recycle• Exercise regularly• Have children living at

home• Wear sunscreen regularly

Sources: Mediamark Research, Spring 2004 and comScore Media Metrix, February 2005.

Data is used to develop content, consumer, category and campaign insights for our key content partners and large advertisers

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Yahoo! Data – A league of its own…

Terrabytes of Warehoused Data

25 49 94 100500

1,000

5,000

Am

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n

Ko

rea

Te

leco

m

AT

&T

Y!

Liv

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tor

Y!

Pa

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ma

Wa

reh

ou

se

Wa

lma

rt

Y!

Ma

in

wa

reh

ou

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GRAND CHALLENGE PROBLEMS OF DATA PROCESSING

TRAVEL, CREDIT CARD PROCESSING, STOCK EXCHANGE, RETAIL, INTERNET

Y! PROBLEM EXCEEDS OTHERS BY 2 ORDERS OF MAGNITUDE

Millions of Events Processed Per Day

50 120 2252,000

14,000

SABRE VISA NYSE Y! Panama Y! DataHighway

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To be continued…

• Will cover the Yahoo! case study on Tuesday’s Invited talk

• Will include– Strategic Importance of Data

– Evolving the data strategy

– Evolving towards the need to invent the new sciences of the Internet

Hope the Data Miner’s Story continues… Perhaps to a happy ending?

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[email protected] You! & Questions?

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KDD-07 Invited Innovation Talk

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