adding value to information

© Copyright IBM Corporation 2008

Adding Value to Information via Analytics. Perspective from BA&MS Research and Projects

May 2008

Document Title | Date 2

IBM Business Analytics and Mathematical Sciences


Outline

Historical perspective. When can analytics enhance value of information? Using analytics to utilize information.

- Supply chain- Workforce management- Carbon management

Using analytics to extract information.- Collaborative filtering, Netflix challenge- ASCOT- BANTER

Using analytics to collect information.- Prediction markets- Peer-to-peer services- Personal benchmarking




Information / Analytic services start up when a new sector of economic activity begins to take-off

Information / Analytic Service Starting Points

2000199019801970196019501940193019201900

IMS Health

Brand Pharmaceutical market begins to take off

R.L. Polk meets with

Alfred Sloan to discuss information

needs in growing auto

market

Polk Auto Registry

Database

A.C. Nielsen

Network TV advertising opens up

Early Mover position in an emerging market is critical

Getty Images

Digital Photography takes over

Navteq

GPS becomes

commercially usable

Stock market crash of

1907

Moody’s

aQuantive

Internet advertising begins to

grow

Morningstar

Take-off in

individual mutual fund

investing

Fair-Isaac

Consumer credit goes

mass market




Outline








Utilizing Information

We consider situations where information is already available From ERP or other business process automation tools

Historical data

Some enterprise generated view of the future

May be combined with purchased data from information services

Most examples now are within an enterprise or an enterprise driven value net

We focus on the case where analytics are applied to the information with the goal of optimizing the use of resources

Examples:- Supply Chain- Workforce management- Carbon management




Supply Chain Collaboration: IBM Buy Analysis Tool (iBAT)Improve Inventory Cost in IBM's Extended Supply Chain

Business Problem

Solution Business Value

A significant percentage of IBM’s hardware sales in high-velocity servers are sold through major channel partners such as Arrow, Ingram, and Tech Data.

Lack of alignment between procurement, manufacturing, and channel sales resulted in significant price protection and sales incentive costs for IBM and high inventory-related costs for our channel partners

Web-based collaboration platform for IBM’s channel replenishment planning that combines innovative forecasting and inventory analytics with up-to-date visibility of channel sales and inventory data

Optimized buy recommendations for channel partners based on statistical forecasting techniques and risk-optimized inventory replenishment models

Proactive review system that initiates demand shaping based on supply and demand imbalances

Standard SOA-based solution design which can easily be adapted to specific ERP environments

Patent-pending methodology

Cornerstone of IBM Server Group’s Business Partner Transformation Initiative

Fully deployed with IBM’s largest channel partners across the United States, Canada and Europe

Solution enables business partners to carry 15-25% less inventory without negatively impacting their delivery performance

Lower channel inventory resulted in lower price protection expenses for IBM, improved cash flow, and higher operating margins




Available to Sell (ATS)Find saleable product recommendations to consume excess inventory

Business Problem

Solution Business Value

With shrinking product lifecycles, component supply overages can quickly lead to obsolescence requiring costly inventory writeoffs. One way to avoid this costs is to find products to build and sell that would consume the excess supply.

In a complex product environment such as IBM Servers, product build-out typically requires additional procurement of non-excess parts to “square” with the excess supplies. With part commonality across many possible product configurations, this leads to an enormous number of potential build-out strategies to choose from. Additional factors such as part substitution, re-work costs, and marketing constraints make this a difficult optimization problem.

ATS Engine uses IBM’s Watson Implosion Technology to find optimal sales recommendation portfolio given: excess part supplies, bill of material, procurement and value-add costs, product demand upper bounds, and product pricing.

Pegging module assigns excess consumption additional costs to each product in the sales recommendation allowing users to pick which build-outs to execute and promote in market.

What-if capability enables users to cost a targeted build-out plan, supporting end-of-life processes.

ATS Engine and Process fully deployed in IBM’s Systems Technology Group since 2002.

Solution drove build-outs and sales recommendations which consumed $200 million worth of excess inventory in 2002.

Ongoing usage of the tool keeps excess supply from becoming obsolete.

System is integrated with IBM’s Central Planning Engine with Web-based, on-demand availability within IBM STG.




Application Areas in Workforce Management

JAN APR JUL DEC

DEMAND FORECASTING CAPACITY PLANNING

STRATEGIC PLANNING TRAINING AND LEARNING

SKILL&ENGAGEMENT ANALYTICS

MATCHING & SCHEDULING

?

x

Now Target

Many opportunities to improve workforce management through utilization of information




Workforce challenges - The DATA is distributed in many enterprise applications

There is no single “Enterprise Resource Planning” tool for labor management Supply (given in terms of roles or skills)

- Traditional HR systems contain information about the current job Structured: Position code, salary, location, shift, etc Unstructured: Education, IBM courses, dept history, awards

- New Job Role/Skill Set with job taxonomy and skill list Full Text Resumes

Demand (given in terms of engagements or contracts)- Past and Current Contracts (and history of deal closure) - New opportunities: Sales Opportunity Database

Missing link- Bill of resources = set of skills required to deliver an engagement- But billing database includes detail (by individual) on employees participation in

engagements- And additional sources include contractor/engagement data




0.00

20.00

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60.00

10/15/2004 10/29/2004 11/12/2004

MOT LIBERTYVLLE

PLM.Engineering &Design BusinessTransformationConsultant 9

Can range from one month, one skill set…..

….to more than 10 months, 16K hours, and wide range of job roles/skill sets

CAT MOSSV AC

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100.00

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6/11/2004

7/11/2004

8/11/2004

9/11/2004

10/11/2004

11/11/2004

12/11/2004

1/11/2005

2/11/2005

3/11/2005

4/11/2005

WebSphere ApplicationServer ApplicationArchitect 7Procurement ProjectManager 9

Procurement ProjectManager 10

Procurement BusinessTransformationConsultant 8Procurement BusinessTransformationConsultant 7Procurement BusinessTransformationConsultant 6 Partner BusinessDevelopment Executive

Operations StrategyEngagement Manager

Matrix One PackagedSolution IntegrationConsultant Client Facing ProjectAdministrator

Ariba PackagedSolution IntegrationConsultant

Business Consulting Examples

Weekly variations appear to be driven by calendar effects, vacation schedules, and resource availability

Weekly variations appear to be driven by calendar effects, vacation schedules, and resource availability

Supply Chain-PLM Engagements




Several different sources of data High level account information, such as

Client name Account description Offering information Billing (Fixed price, best estimate)

- Ledger information Project cost, revenue

- Labor claiming information Hours claimed per week by each

employee on a project

- Employee information Line of Business, Job Role, Skill Set,

global resource, etc.

For US contracts over past 18 months- Approximately 10K accounts- More than 2M labor claim records

Data Issues - Can’t tell if individual is deployed in

primary Job Role/Skill Set - JR/SS table has current state only

Beginning to collect longitudinal data

- High % of missing JR/SS informationJR/SS not tracked consistently at subcontractor or global resource level

No information for consultants no longer with IBM

Over 400 valid JR/SS combinations Account descriptions give little to no

indication of scope of work

History reflects what actually happened, not

necessarily “best practice”

Analysis of Data to estimate Bill Of Resources




Engagement Profiling

Service offerings/opportunities are typically specified in terms of revenue and solution-Using statistical analysis and clustering, develop template staffing structure for offerings, which can be used to translate offering revenue forecasts and opportunity revenue into staffing resource requirements-Semi-automated and parameterized process for generating staffing templates and supporting software

Value-Standardized project templates allow for planning of staffing decisions at earlier stages of the engagement process, more reliable forecasting of resource needs and better workforce planning-Enables partners/project managers to quickly develop staffing plans early in the opportunity cycle-Predictive accuracy of 70-80% at engagement level and 90-95% at aggregate level for major job roles-Deployed by GBS in the Demand Capture Tool 2.1 released in December 2006

Client Name ABC Sector IndustrialService Supply Chain Management ISV SAP Modules SAP.SCMProject Type Package Configuration and Implementation

Start Date 1/2/2004End Date 12/31/2004Estimated Revenue 4700000Linked to other projects? No Plan Names




Allows development of capacity plans according to business strategy. The best solution will be based on a combination of expected revenues/costs/profits, allowed risk tolerances with respect to revenue loss, and other business concerns, such as market-share and growth

$9.4 $9.4 $9.1 $8.3

$4.4

$35.4 $35.8 $37.1$39.0

$40.8

$26.0 $26.5 $28.0$30.7

$36.4

$-

$5

$10

$15

$20

$25

$30

$35

$40

$45

$5.56M (optimal profit)

$5.2M (all risks < 20%)

$3.9M (all risks < 10%)

$2.0M (all risks < 5%)

$0.2M (all risks < 0.5%)

Expected Profit Expected Revenue Expected Labor Cost

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Revenue at Risk ($M)

Revenue

curve

Labor Cost curve

Gross Profit curve

251 266 292 346247Capacity

Risk Based Capacity Planning




Workforce Does Not Happen Overnight

The use of analytics and optimization in workforce management applications requires significant maturity levels in terms of data, process and business understanding

Job taxonomies“How to describe skills and activities”

View of supply“Infrastructure and process to capture available resources”

Bills of materials“Templates to describe projects/tasks to be performed”

View of demand“Infrastructure, process and analytics to forecast demand”

Analytics & Optimization




Carbon as a New Variable in Supply Chain Decisions

Typical supply chain optimization only considers the direct monetary costs

Inventory and supply policies can be significantly different with the inclusion of broader environmental costs, and constraints

A good model can quantify both the cost and the carbon impact of various supply chain policies.

A comprehensive model can identify areas where carbon and cost reduction can be achieved simultaneously (e.g. minimization of wastage, rework etc)

Transportati

on Options

Transportati

on Options

InventoryPolicy

Options

InventoryPolicy

Options

QualityQuality

COCO22

CostCost

ServiceService

Supply Chain Trade-offs

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gn

Opti

ons

Desi

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Opti

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ions

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Packaging

Options

Packaging

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Process Options

Process Options

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Any Supply Chain Carbon View must be Multi-Dimensional

Shrinkage ($, CO2 cost)

Breakage ($, CO2 cost)

Real Estate ($ cost)

Handling ($, CO2 cost)

Transportation ($, CO2 cost)

Utilities ($, CO2 cost)

Manufacturing ($, CO2 cost)

Component Supply ($, CO2 cost)

Packaging Options

Transportation Options

Energy Options

Inventory Policy Options

Process Options

Supply Options

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Green SigmaTM – Carbon Management Dashboard




Outline








Extracting Information

We consider situations when vast amount of data is available. Typically a mix of structured and unstructured dataOften incomplete and/or noisy data

Data may come from multiple sources, but typically includes at least some “private” data.

The data owner wants to use the data to improve some aspect of the business operations, but a specific business objective is typically not fully articulated.

Analysis (and pre-analysis data preparation) need to be automated.

Examples: KDD cup and Netflix Challenge ASCOT BANTER




October 2006 Announcement of the NETFLIX Competition

USAToday headline:

“Netflix offers $1 million prize for better movie recommendations”

Details: Beat NETFLIX current recommender model ‘Cinematch’ by 10% based on

absolute rating error prior to 2011 $50.000 for the annual progress price (relative to baseline) Data contains a subset of 100 million movie ratings from NETFLIX including

480,189 users and 17,770 movies Performance is evaluated on holdout movies-users pairs NETFLIX competition has attracted 24,396 contestants on 19,799 teams from

155 different countries 25115 valid submissions from 3335 different teams current best result is 9.08% better than baseline (from 6.7% as of March 2007)




KDD-Cup 2007

The 2007 KDD-Cup was based on a subset of the Netflix prize data- The Netflix grand prize competition (a different task on the same data) attracts

24396 contestants on 19799 teams from 155 different countries (no IBM participants due to IP issues)

- The data contains a subset of 100 million movie ratings from Netflix.com including 480,189 users and 17,770 movies

- Ratings of users and movies were collected from Nov-1999 until Dec-2005

Task 1: Who Rated what in 2006- Given a list of 100,000 pairs of users and movies, predict for each pair the

probability that the user rated the movie in 2006

Task 2: Number of ratings per movie in 2006- Given a list of 8863 movie, predict the number of additional reviews that all

existing users will give in 2006




Task 1: Probability of a member rating a movie Extracted features:

- Movie-based features Graph topology: # of ratings per movie (across different years), adjacent scores between

movies calculated using SVD on the graph matrix Movie content: similarity of two movies calculated using Latent Semantic Indexing based on

bag of words from (1) plots of the movie and (2) other information, such as directory, actors

- User profile Graph topology: #rating per user (across different years), adjacent scores between users in

the graph calculated using SVD User content: user preference based on the movies being rated: key word match count

Learning Algorithm:- Single classifiers: logistic regression, Ridge regression, decision tree, support vector machines

(best run: RMSE = 0.2647)

- Naïve Ensemble: combining sub-classifiers built on different types of features with pre-set weights (best run: RMSE = 0.2642)

- Ensemble classifiers: combining sub-classifiers with weight learnt from the development set (best run: RMSE = 0.2629)




Task 2: Number of additional ratings per movie

Perform in depth analysis of the domainAll movies and users were in the NETFLIX database already in Dec 2005 Model the “aging” process of movies

Understand the way the specific data for the competition was createdThe new ratings in 2006 were split into two sets by random sampling of moviesThe ratings for Task 1 were sampled according to the MARGINAL distribution of ratings in 2006We can use the “test” set for Task 1 as a surrogate training set for Task 2

short of a scaling factor that is unknown, and modeled separately

Estimate Poisson regression on the marginal as found in test set for task 1Variables: Lagged reviews, genre, age, director, actor, …Correct for missing duplicates based on the estimated rating marginal of the users

Estimate the Scalar to rescale from marginal to total4 Poisson regression models: 1, 2, 3 and 4 quarter ahead prediction of the number of ratings for

all moviesCorrect for decreasing user base by creating lagged datasets with removed users after deadline

Key point: Understanding the data domain and how the sampling was done was critical factor in accuracy of prediction




ASCOT (Automated Search for Collaboration Opportunities by Text-mining) We currently build OnTARGET models to predict purchase probability for existing IBM clients

as well as “Whitespace” -- e.g. will they purchase an IBM Rational software product?- These models use historical IBM transactional data joined with D&B data- What if we added indexed content crawled from each company’s website?

We apply Active Feature Acquisition to minimize number of web sites we need to crawlWe find interesting terms on a company website that increases likelihood of a Rational SW purchase

And the resulting model is more accurate than our existing OnTARGET model …

Chi-squared score Stemmed word100.4 interfac89.4 enabl89.1 deploi79.5 scalabl78.7 integr76.9 deploy74.5 simplifi70.1 autom68.8 multipl68.7 platform65.9 configur64.9 sophist64.6 workflow63.2 leverag62.2 interoper61.8 enterpris61.5 proposit60.2 softwar

Percent of Websites Processed

Ac

cu

rac

y (

AU

C) Active Feature Acquisition

Random Acquisition

With Web Content

Existing OnTARGET model (Without Web Content)

0 5 10 15 20 25

Improvement due to web content




BANTER (Blog Analysis of Network Topology and Evolving Responses)

77M Blogs

Technology BlogsEnterprise

Software Blogs

1. How do we identify the relevant sub-universe of blogs? We submit set of relevant keywords to Technorati, include out-

linked blogs, and then refine this sub-universe via active learning

2. How do we determine “authorities” in this sub-universe? We use page-rank-like algorithms against cross-reference

structure, combined with SNA concepts (e.g. Information Flow)

3. How do we detect emerging topics and themes in this sub-universe?

One approach is to predict link (cross-reference) formation using network evolution and content (keywords) at the nodes (blogs)

4. How do we detect sentiment associated with specific posts? One approach is to learn a model using text features against

labeled product ratings (1-5 stars) scraped from Amazon

5 10 15 20

050

100

15

0200

OpenID Buzz in January

days

Num

ber

of

Occurr

ence

OpenID Buzz in January

OBJECTIVE: Apply machine-learning to extract business insight from technology-based blogs




Outline








Outline




Using information and analytics to collect more information.- Prediction markets- Peer-to-peer services- Personal benchmarking




Collecting (more) Information

Can available data be made more useful through the addition of a small amount of additional data? What to collect?How to collect? Where (from whom) to collect? Given what you have, how do you determine what else do you need?

What additional data is becoming available?How can it be effectively utilized?

Examples: Prediction markets: collective prediction of event probabilities, ranking bets in

prediction markets to figure out “experts”. Peer-to-peer services: information exchange to establish “reputation”, common

interests, groups of similar peers. Personal benchmarking




What is a Prediction Market?

An online forum, usually in a stock market format, that gathers collective wisdom for decision-making and forecasting

One method of ‘Crowdsourcing’ or using the ‘wisdom of crowds’Considered an emerging ‘Enterprise 2.0’ technology

Concept is decades old, but until recently was not used within enterprises Questions are posed regarding future events, and participants vote by ‘investing’ in their forecast using virtual currency

i.e., “IBM stock price will hit $120 by January 1st”, or “Proposition 123 will pass into law before YE 2008”

Different markets for different topics, events or decisionsNo specific knowledge or expertise is required, regardless of the topic

Stock Prices are interpreted as event probability, while analysis of trading behavior provides valuable data on how information flowsParticipants are recognized for their prediction accuracy, providing motivation to share valuable knowledge - truthfullyContains algorithms for aggregating diverse opinions Often used as sole prediction method, but also used to complement other forecasting mechanismsSynonyms include: Predictive markets, information markets, decision markets, idea futures, event derivatives, virtual markets




Political Examples…




Public prediction markets?




Collective intelligence harnessed from prediction markets yields myriad benefits for enterprises and employees

Strategic foresight into emerging issues from large, diverse and global population

Quick, efficient aggregation of employee knowledge Insight which even the best Business Intelligence

solution could not provide Real-time analytics on social networking, social

capitalMore effective and more accurate than polls, surveys,

ratingsCircumvention of bureaucracy impeding flow of

information Elimination of personal biases in decision-making Improved innovation culture and employee morale

Participants given a voice in decision-making and/or forecasting

Sponsors provide non-monetary incentives for employees to disclose valuable information and often untapped knowledge

Increase in visibility and opportunities for participants by building a reputation for good decision-making and foresight




Do they work? Properly executed prediction markets are more accurate than teams of experts, or any other traditional forecasting method

Examples of Market Accuracy The Iowa Electronic Markets (IEM) predictions for the presidential

elections between 1988 and 2000 were off by an average of 1.37%; more accurate than any exit polls

InTrade Markets correctly forecast the 2004 presidential race in all 50 states and 49:50 State Senate races

HP’s internal prediction market, over a three year period, outperformed HP’s official printer sales forecasts 75% of the time

Intel established a prediction market to allocate manufacturing capacity, which yielded a 100% efficiency improvement

Siemens’ prediction market, to assess their ability to meet a project deadline, correctly forecast the missed deadline; management had predicted success

Hollywood Stock Exchange (HSX) correctly predicted 32:39 Oscar nominees and 7:8 Oscar winners in 2006

Farmer’s Almanac has long been a trusted source for weather predictions because of its surprising accuracy




Peer-to-peer services Governments and large institutions are becoming less effective and efficient at providing affordable and reliable basic services (retirement benefits, health care, insurance, education) for individuals.

Individuals need to become increasingly self-sufficient in these regards- Individuals are turning to other individuals in a peer-to-peer fashion, to tap into the

collective knowledge and financial pockets of communities (both virtual and physical).

In developing countries self-sufficiency may be only practical solution.

As peer-to-peer networks progress from serving ‘lighter’ (e.g., entertainment) needs to serving these long-term, basic needs, a more robust set of IT, communications and business services is required

- manage new peer-to-peer applications- provide high-quality information and analytics services to individuals.




Needs and Opportunities Peer-to-peer ‘services’ (e.g., social/micro lending, peer-to-peer insurance,

homeschooling) are growing There are risks and sources of uncertainty associated with peer-to-peer service:

- Reliability and accuracy of web-based data- Fraud & Reputation (how do you know who you are really dealing with?)- Security of personal information- Reliability of web-based IT infrastructure

These risk factors are not new. However, the models required to adequately capture the characteristics of uncertainty in a peer-to-peer services environment may be different from traditional models used in more centralized business environments.

Additionally, the types of services that participants in the P2P environment require may also be different (e.g., more personalized uncertainty analytics services, mobile web).

Core technologies are available and gaining adopters (P2P, electronic health records, social networking sites, business integrity, business intelligence)

Will we see an emergence of companies whose business is to support P2P services networks?




Example: Peer-to-Peer Lending

Potentially transformative financial business - Prosper, a peer-to-peer borrowing and lending system.

The system lets anybody make a case for why they need to borrow money.

Lenders can select which cases they want to take on and easily put a little money to work in dozens or even hundreds of them, diversifying their risk.

Since launch, over 200,000 consumers around the world have become Zopa members, as they seek the innovative loans and returns on investments that Zopa offers.

- More recently growth has been boosted by the global credit crunch which is driving unprecedented demand for P2P loans as banks become less competitive and tighten their lending criteria.

Online peer-to-peer lending services, Prosper, Zopa and CircleLending all have significant lead time and lots of venture backing;

- Zopa, for example, has raised around $34 million.

Lending Club is the first of its kind to integrate its services into a social network.

These services are generating a huge number of lending transactions - How can this transaction data be utilized to provide new information to government and/or industry?




http://www.slideshare.net/JeanChristopheCapelli/20080329-social-lendingbar-camp-bank-sf




Peer-to-Peer Insurance

Peer-to-Peer Insurance is preparing to launch a new type of insurance product, is based on pooling people together to insure each other at rates cheaper than they currently pay, without automatically losing the money they pay as premium.

The Peer-to-Peer Insurance Project:- Peer-to-Peer Auto Insurance (safe drivers pooled together to insure each other) - Peer-to-Peer Home Insurance (categories of homeowners pooled together to insure each

other)

Value Proposition:- Participants will not automatically, and permanently, lose all the money paid for

coverage. - Incentive for safe driving (personal, and social good) - Credit score will not be used to set premium. - No age discrimination - No fine print. None of that sleek legal lingo buried in the middle of a thousand pages of

policy.

-What information is used to create pools? What information about pool is provided to participants? New methods for calculating risk may be required.




Personal benchmarking

Log onto your favorite web browser and you'll likely be offered a chance to do some personal benchmarking.

There are opportunities to compare everything from body mass index to the trade-in value of your car or how your local school district ranks.

Beyond a chance to feed any competitive streak, benchmarking can motivate change and help monitor progress.

But what else can the information be used for?




Examples

Carbon Footprint - Calculate, Reduce and Offset.- www.carbonfootprint.com calculates, compares to national average and proposes

products to reduce or offset the footprint (like donating money for reforestation)- Enter information about your car make and model and miles you travel. Energy

bills, flights you take, number of people in household, state of residence. - Can use for targeted marketing of alternative energy sources, hybrid cars, even travel

packages.

Health and Fitness - www.revolutionhealth.com builds your profile, enables members to create webpages

on topics interesting to them, supports blogs and communities, helps people find communities with similar health related interests.

- Enter information such as age, interests, health history, fitness routine, etc.- Can use for health insurance marketing, drug marketing, weight loss programs, etc.




Examples Diving community

- www.padi.com allows members to create a profile and log diving information.- Enter information about where and when you dive, how long, how deep, with

whom, what equipment you bought for how much and when.- Can use for profiling travel preferences, frequency, destinations. Independent travel

vs. large resorts, consumer profile, level of risk averseness.

Knitting community- http://www.ravelry.com, a members only knitting community, launched in May 2007

By February 2008 had over 80,000 members. Adds 800+ per day, but waiting list is consistently over 5000

- Includes “stash” and project management tools, connections to flickr for images of finished items, pattern repository, forum, groups (2 IBM groups, 4 math groups)

- Enter information about what you own, finished and current projects, etc- Used for event and product promotions, pattern and material sales, social networking

and assorted competitive events




Conclusions

Amount of information is growing- IT automation - Instrumentation- End users

There are established analytics methods for extracting addition value from data- For standard automated business processes

There are new analytic methods being developed- To support new business processes and business models- To leverage combinations of public and private data

Scalability will continue to be an issue Personalization of analytics is an opportunity Early Mover position in an emerging market is critical