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Analytics Industry Overview:To Big Data and Beyond !

Gregory Piatetskywww.KDnuggets.com/gps.html

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My Data Path

• PhD in applying Machine Learning to databases• Researcher at GTE Labs – started first project

on Knowledge Discovery in Databases in 1989• Organized first 3 KDD workshops (1989-93),

cofounded KDD conferences and ACM SIGKDD• Chief Scientist at analytics startup 1998-2001• Chair, SIGKDD, 2005-2009• Analytics/Data Mining Consultant, 2001-

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KDnuggets

• Stands for Knowledge Discovery Nuggets• 1993 - started KDnuggets News email newsletter (~

12,000 email subscribers now)• early website in 1994, www.KDnuggets.com in 1997

– 2011 best year, 45-50,000 unique visitors/month• twitter.com/kdnuggets ~3,000 followers• facebook.com/kdnuggets page• group: KDnuggets Analytics & Data Mining • Recently featured on CNN

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KDnuggets mission

Cover Analytics and Data Mining field : • News, Jobs, Software, Data (most popular)• Also Academic positions, CFP, Companies,

Consulting, Courses, Meetings, Polls, Publications, Solutions, Webcasts

• Subscribe to bi-weekly KDnuggets News at www.kdnuggets.com/subscribe.html

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Analyzing Data or …

• Statistics• Data mining• Knowledge Discovery in Data • KDD• Analytics• Data Science• …?

Core:

Finding Useful Patterns in Data

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History

• Statistics: 1800 - • Data dredging, data “fishing” : 1960s• Data Mining: 1980 –• Database Mining ~ 1985 (was HNC trademark, not used)

• Knowledge Discovery in Data: 1989 –– KDD workshop in 1989

• Analytics : 2006 – – Google Analytics, “Competing on Analytics” book

• Data Science: 2010 –

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Pre-history

From Google Ngram viewer – English language booksNote: Our analysis uses only English language data. Other languages, especially Chinese , need to be considered for full picture

Statistics is the biggest term in 20th century, but data mining and analytics appears in late 1990s

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Recent History: Analytics, Data Mining, Knowledge Discovery

Analytics has been used since 1800, but started to rise in 2005Data Mining jumps around 1996 (soon after first KDD conference) but declines after 2003 (TIA controversy, associated with gov. invasion of privacy).Knowledge Discovery appears in 1989, jumps in 1996, and plateaus after 2000

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Google N-gram Results case sensitive

Different capitalizations changes counts, but using lowercase is probably appropriate to measure general popularity.

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Earliest use of “data mining” 1962?

Source: Google Books

After eliminating many “following data. Mining cost is ” exampleswhich refer to Mining of minerals, and books from “1958” that have a CD attached (errors in book year)

The earliest “data mining” reference I found is

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Google Trends: After 2006, Data Mining < Analytics

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Google Trends: Analytics observations

Google Analytics introduced,Dec 2005

Competing on Analytics book, Apr 2007 December vacation drop

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Half of “Analytics” searches are for “Google Analytics”

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Excluding Google Analytics

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Google Insights: searches for data mining, analytics -googleare most popular in India, US

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Data Mining >> Predictive Analytics

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Business, Predictive, Text Analytics

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Analytics > Data Mining > Data Science

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Data Science, Big Data

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Analytics Today

KDnuggets Polls Findings

www.KDnuggets.com/polls/

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avg 2.4 industries

CRM/ consumer analytics Banking

Health care/ HR Fraud Detection

Direct Marketing/ Fundraising Finance

Telecom / Cable Science

Insurance Advertising

Education Web usage mining

Credit Scoring Retail

Medical/ Pharma Manufacturing

e-Commerce Social Networks

Search / Web content mining Government/Military

Biotech/Genomics Investment / Stocks

Entertainment/ Music Security / Anti-terrorism

Travel / Hospitality Social Policy/Survey analysis

Junk email / Anti-spam Other

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%

Where did you apply analytics/data mining?

www.KDnuggets.com/polls/2010/analytics-data-mining-industries-applications.html

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Data Types Analyzed/Mined

www.KDnuggets.com/polls/2011/data-types-analyzed-mined.html

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Data Types w. Most Growth in 2011

• location/geo/mobile data

• music / audio • time series

• Genomics, according to John Mattison

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Largest Dataset Analyzed?2011 median dataset size ~10-20 GB, vs 8-10 GB in 2010.

Increase in10 GB to 1 PB range

www.KDnuggets.com/polls/2011/largest-dataset-analyzed-data-mined.html

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Largest Dataset Analyzed by Region

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Which methods/algorithms did you use for data analysis in 2011

Decision Trees

Regression

Clustering

Statistics

Visualization

Time series/Sequence analysis

Support Vector (SVM)

Association rules

Ensemble methods

Text Mining

Neural Nets

Boosting

Bayesian

Bagging

Factor Analysis

Anomaly/Deviation detection

Social Network Analysis

Survival Analysis

Genetic algorithms

Uplift modeling

0% 10% 20% 30% 40% 50% 60% 70%

% analysts who used it

www.KDnuggets.com/polls/2011/algorithms-analytics-data-mining.html

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Algorithms with highest Industry Affinity

www.KDnuggets.com/polls/2011/algorithms-analytics-data-mining.html

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“Academic” algorithmslowest Industry affinity

www.KDnuggets.com/polls/2011/algorithms-analytics-data-mining.html

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Cloud Analytics is not common (yet)

www.KDnuggets.com/polls/2011/algorithms-analytics-data-mining.html

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JOBS AND SKILLS

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Shortage of Skills

• McKinsey: shortage by 2018 in the US of– 140-190,000 people with deep analytical skills

– 1.5 M managers/analysts with the know-how to use the analysis of big data to make effective decisions.

Source: www.mckinsey.com/mgi/publications/big_data/

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Job data: Data Scientist

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Jobs: Data Mining >> Data Scientist

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“Ground” Analytics (LinkedIn Skills)

~ 75,000 with Data Mining skill

~ 7,000 with Predictive Modeling

Also ~ 20,000 with Predictive Analytics(not related with Predictive Modeling ??

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Cloud (Big Data) Analytics Skills

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Analytics LinkedIn Skills

Machine LearningPredictive Analytics

Text Mining MapReduce

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Data Tsunami

• In 2010 enterprises stored 7 exabytes =7,000,000,000 GB

of new data (McKinsey)• 90 percent of the

world's data has been generated in the past two years (IBM)

Image with apologies to KDD-2011

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Big Data Aspects?

• Volume– Terabytes to Petabytes …

• V e l o c i t y – online streaming

• Variety – numbers, text, links, images, audio, video, …

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Volume + Velocity => No consistency

• CAP Theorem (Eric Brewer, 2000)For highly scalable distributed systems, you can only have

two of following: – 1) consistency, – 2) high availability, and – 3) (network) partition tolerance (network failure tolerance)

http://www.julianbrowne.com/article/viewer/brewers-cap-theorem

Implication: Big data solutions must stop worrying about consistency if they want high availability

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Big Data

• 2nd Industrial Revolution

• Do old activities better

• Create new activities/businesses

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Application areas

• Doing old things better– Churn prediction – Direct marketing/Customer modeling– Recommendations– Fraud detection– Security/Intelligence – …

• Competition will level companies

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Limit to Predicting Customer Behavior?

• There is fundamental randomness in human behavior and once we find 1-level effects, more data or better algorithms will give diminishing returns in most cases

• Example: Netflix Prize: the most advanced algorithms were only a few percentages better than basic algorithms

Direct Marketing: Random and Model-sorted Lists

0102030405060708090100

5 15 25 35 45 55 65 75 85 95

RandomModel

5% of random list have 5% of hits5% of model-score ranked list have 21% of hits. Lift(5%) = 21%/5% = 4.2

Pct list

CPH: Cum

ulative Pct Hits

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Most lift curves are surprising similarStudy of lift curves in banking,

telecom

Best lift curves are similar

Special point T=Target percentage

Lift(T) ~ sqrt (1/T)

G. Piatetsky-Shapiro, B. Masand, Estimating

Campaign Benefits and Modeling Lift, in Proceedings of KDD-99 Conference, ACM Press, 1999.

0

2

4

6

8

10

12

14

0 5 10 15 20 25

100*T%

Lift

Actual lift(T) Est. lift(T)

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Big Data Enables New Things !

– Google – first big success of big data – Social networks (facebook, Twitter, LinkedIn, …)

success depends on network size, i.e. big data

– Location analytics– Health-care

• Personalized medicine

– Semantics and AI ?• Imagine IBM Watson, Siri in 2020 ?

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Big Data Growth By Industry

Source: http://www.mckinsey.com/mgi/publications/big_data/

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Research and Industry Disconnect?

• Uplift modeling – needs more research• Association rules need less papers• Data Mining with Privacy research – industry

use?

• KDD conference aims to bring researchers and industry people together

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Hot Growth Areas

• Social Analytics– Klout– many twitter micro-analytics (twitalyzer,

TweetEffect, TweetStats)

• Mobile Analytics– Privacy and data tracks (KDD Lab, Pisa)

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Big Data Bubble?

Copyright © 2011 KDnuggets

Gartner Hype Cycle

Big Data