Adding Value to Information via
Analytics. Perspective from BA&MS
Research and Projects
May 2008
© Copyright IBM Corporation 2008
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.
-
2
Prediction markets
Peer-to-peer services
Personal benchmarking
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Information / Analytic services start up when a new sector of
economic activity begins to take-off
Information / Analytic Service Starting Points
1900
1920
1930
R.L. Polk
meets with
Alfred Sloan
to discuss
information
needs in
growing auto
market
Stock
market
crash of
1907
Moody’s
Polk Auto
Registry
Database
Network TV
advertising
opens up
1940
1950
Brand
Pharmaceuti
cal market
begins to
take off
1960
IMS
Health
1970
Take-off
in
individual
mutual
fund
investing
1980
FairIsaac
2000
Digital
Photography
takes over
Getty
Images
Morningstar
Internet
advertising
begins to
grow
A.C.
Nielsen
Consumer
credit goes
mass
market
1990
GPS
becomes
commercially
usable
aQuantive
Navteq
Early Mover position in an emerging market is critical
3
Document Title | Date
© Copyright IBM Corporation 2008
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.
-
4
Prediction markets
Peer-to-peer services
Personal benchmarking
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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:
-
5
Supply Chain
Workforce management
Carbon management
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Supply Chain Collaboration: IBM Buy Analysis Tool (iBAT)
Improve Inventory Cost in IBM's Extended Supply Chain
Business Problem
 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
6
Solution
Business Value
 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 riskoptimized 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
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Available to Sell (ATS)
Find saleable product recommendations to consume excess inventory
Business Problem
 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.
7
Solution
Business Value
 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 buildouts 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.
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Application Areas in Workforce Management
Many opportunities to improve workforce management through utilization of information
JAN
SKILL&ENGAGEMENT ANALYTICS
APR
JUL
DEC
DEMAND FORECASTING
Now
?
CAPACITY PLANNING
Target
x
MATCHING & SCHEDULING
8
Document Title | Date
STRATEGIC PLANNING
TRAINING AND LEARNING
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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
-
-
9
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
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Business Consulting Examples
Supply Chain-PLM Engagements
Can range from one month, one skill set…..
Weekly variations appear to be
driven by calendar effects,
vacation schedules, and
resource availability
MOT LIBERTYVLLE
60.00
40.00
PLM.Engineering &
Design Business
Transformation
Consultant 9
20.00
0.00
10/15/2004
10/29/2004
11/12/2004
….to more than 10 months, 16K hours, and wide range of job roles/skill sets
CAT MOSSV
AC
600.00
W ebSphere Application
Server Application
Architect 7
Procurement Proj ect
Manager 9
Procurement Proj ect
Manager 10
500.00
Procurement Business
Transformation
Consultant 8
Procurement Business
Transformation
Consultant 7
Procurement Business
Transformation
Consultant 6
Partner Business
Development Executive
400.00
300.00
200.00
100.00
Operations Strategy
Engagement Manager
4/11/2005
3/11/2005
2/11/2005
1/11/2005
12/11/2004
11/11/2004
10/11/2004
9/11/2004
8/11/2004
7/11/2004
6/11/2004
0.00
Matrix One Packaged
Solution Integration
Consultant
Client Facing Proj ect
Administrator
Ariba Packaged
Solution Integration
Consultant
10
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Analysis of Data to estimate Bill Of Resources

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
-
11
Approximately 10K accounts
More than 2M labor claim records
Document Title | Date
 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 information
JR/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”
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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
12
Client Name
Sector
Service
ISV
Project Type
ABC
Industrial
Supply Chain Management
SAP
Modules
Package Configuration and Implementation
Start Date
End Date
Estimated Revenue
Linked to other projects?
1/2/2004
12/31/2004
4700000
No
Document Title | Date
SAP.SCM
Plan Names
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Risk Based Capacity Planning
TECHNOLOGY ADOPTION PRODUCT SERVICES, US, 3Q05
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
Revenue at Risk ($M)
Capacity
13
$45
Expected Profit
Expected Revenue
Expected Labor Cost
$40
$35
$30
$25
$20
$35.8
$35.4
$40.8
$39.0
$37.1
$36.4
$30.7
$15
$28.0
$26.5
$26.0
$10
$5
$9.4
$9.4
$9.1
$8.3
$4.4
$$5.56M
(optimal profit)
247
Document Title | Date
$5.2M
(all risks < 20%)
251
$3.9M
(all risks < 10%)
266
$2.0M
(all risks < 5%)
292
$0.2M
(all risks < 0.5%)
346
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Workforce Does Not Happen Overnight
The use of analytics and optimization in workforce management applications requires significant maturity
levels in terms of data, process and business understanding
Analytics & Optimization
View of demand
“Infrastructure, process and analytics to forecast demand”
Bills of materials
“Templates to describe projects/tasks to be performed”
View of supply
“Infrastructure and process to capture available resources”
Job taxonomies
“How to describe skills and activities”
14
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Carbon as a New Variable in Supply Chain Decisions
 Typical supply chain optimization only
considers the direct monetary costs
 Inventory and supply policies can be
significantly different with the inclusion of
broader environmental costs, and
constraints
Service
 A good model can quantify both the cost
and the carbon impact of various supply
chain policies.
Quality
 A comprehensive model can identify
areas where carbon and cost reduction
can be achieved simultaneously (e.g.
minimization of wastage, rework etc)
15
Document Title | Date
CO2
Supply Chain
Trade-offs
Cost
Inventory
Policy Options
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Any Supply Chain Carbon View must be Multi-Dimensional
Packaging
Options
Transportation
Options
Energy
Options
Inventory
Policy Options
Shrinkage ($, CO2 cost)
♦
♦
♦
Breakage ($, CO2 cost)
♦
♦
♦
Real Estate ($ cost)
♦
Handling ($, CO2 cost)
♦
Transportation ($, CO2 cost)
♦
Process
Options
Supply
Options
♦
♦
♦
♦
♦
♦
♦
♦
♦
Manufacturing ($, CO2 cost)
♦
♦
Component Supply ($, CO2 cost)
♦
♦
♦
Utilities ($, CO2 cost)
16
Document Title | Date
♦
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Green SigmaTM – Carbon Management Dashboard
17
Document Title | Date
© Copyright IBM Corporation 2008
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.
-
18
Prediction markets
Peer-to-peer services
Personal benchmarking
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Extracting Information
We consider situations when vast amount of data is available.
Typically a mix of structured and unstructured data
Often 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
19
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
20
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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)
21
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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
-
22
Given a list of 8863 movie, predict the number of additional reviews that all
existing users will give in 2006
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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:
23
-
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)
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Task 2: Number of additional ratings per movie
Perform in depth analysis of the domain
All 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 created
The new ratings in 2006 were split into two sets by random sampling of movies
The ratings for Task 1 were sampled according to the MARGINAL distribution of ratings in 2006
We 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 1
Variables: 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 total
4 Poisson regression models: 1, 2, 3 and 4 quarter ahead prediction of the number of ratings for
all movies
Correct 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
24
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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 crawl
We find interesting terms on a company website
that increases likelihood of a Rational SW purchase
Stemmed word
interfac
enabl
deploi
scalabl
integr
deploy
simplifi
autom
multipl
platform
configur
sophist
workflow
leverag
interoper
enterpris
proposit
softwar
With Web Content
Active Feature Acquisition
Accuracy (AUC)
Chi-squared score
100.4
89.4
89.1
79.5
78.7
76.9
74.5
70.1
68.8
68.7
65.9
64.9
64.6
63.2
62.2
61.8
61.5
60.2
And the resulting model is more accurate than our
existing OnTARGET model …
Improvement due
to web content
Random Acquisition
Existing OnTARGET model (Without Web Content)
0
5
10
15
20
Percent of Websites Processed
25
Document Title | Date
© Copyright IBM Corporation 2008
25
IBM Business Analytics and Mathematical Sciences
BANTER (Blog Analysis of Network Topology and Evolving Responses)
Enterprise
Software Blogs
Technology Blogs
OBJECTIVE: Apply machine-learning to extract
business insight from technology-based blogs
1. How do we identify the relevant sub-universe of blogs?
 We submit set of relevant keywords to Technorati, include outlinked blogs, and then refine this sub-universe via active learning
77M Blogs
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)
OpenID Buzz in January
OpenID Buzz in January
150
100
50
0
Number of Occurrence
200
3. How do we detect emerging topics and themes in this subuniverse?
 One approach is to predict link (cross-reference) formation using
network evolution and content (keywords) at the nodes (blogs)
5
10
15
20
days
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
26
Document Title | Date
© Copyright IBM Corporation 2008
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.
-
27
Prediction markets
Peer-to-peer services
Personal benchmarking
Document Title | Date
© Copyright IBM Corporation 2008
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 information and analytics to collect more information.
-
28
Prediction markets
Peer-to-peer services
Personal benchmarking
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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
29
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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 decisions
No 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 flows
Participants are recognized for their prediction accuracy, providing
motivation to share valuable knowledge - truthfully
Contains algorithms for aggregating diverse opinions
Often used as sole prediction method, but also used to complement
other forecasting mechanisms
Synonyms include: Predictive markets, information markets, decision
markets, idea futures, event derivatives, virtual markets
30
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Political Examples…
31
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Public prediction markets?
32
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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
capital
 More effective and more accurate than polls, surveys,
ratings
 Circumvention of bureaucracy impeding flow of
information
 Elimination of personal biases in decision-making
 Improved innovation culture and employee morale



33
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
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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
34
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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.
-
35
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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?
36
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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.
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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;
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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
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How can this transaction data be utilized to provide new information to government and/or industry?
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
http://www.slideshare.net/JeanChristopheCapelli/20080329-social-lendingbar-camp-bank-sf
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Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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)
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Value Proposition:
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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.
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Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
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?
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Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Examples
 Carbon Footprint - Calculate, Reduce and Offset.
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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
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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.
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Examples
 Diving community
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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
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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
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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
Document Title | Date
© Copyright IBM Corporation 2008
IBM Business Analytics and Mathematical Sciences
Conclusions
 Amount of information is growing
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IT automation
Instrumentation
End users
 There are established analytics methods for extracting addition value
from data
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For standard automated business processes
 There are new analytic methods being developed
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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
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Document Title | Date
© Copyright IBM Corporation 2008