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© 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
© Copyright IBM Corporation 2008
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
Document Title | Date 3
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 4
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 5
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 6
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 7
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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.
Document Title | Date 8
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 9
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 10
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
0.00
20.00
40.00
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
0.00
100.00
200.00
300.00
400.00
500.00
600.00
6/11
/200
4
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/200
4
8/11
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4
9/11
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10/1
1/20
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11/1
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12/1
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1/11
/200
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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
Supply Chain-PLM Engagements
Document Title | Date 11
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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 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”
Analysis of Data to estimate Bill Of Resources
Document Title | Date 12
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
Engagement ProfilingService 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 ImplementationStart Date 1/2/2004End Date 12/31/2004Estimated Revenue 4700000Linked to other projects? No Plan Names
Document Title | Date 13
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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%)
$0.2M (all risks < 0.5%)
Expected Profit Expected Revenue Expected Labor Cost
TEC
HN
OLO
GY
AD
OPT
ION
PR
OD
UC
T SE
RVI
CES
, US,
3Q
05
Revenue at Risk ($M)
Revenue
curve
Labor Cost curve
Gross Profit curve
251 266 292 346247Capacity
Risk Based Capacity Planning
Document Title | Date 14
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 15
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
InventoryPolicy
Options
QualityQuality
COCO22
CostCost
ServiceService
Supply Chain Trade-offs
Desi
gn
Optio
ns
Ener
gy
Option
s
Packaging
OptionsProcess Options
Component
Options
Document Title | Date 16
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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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Document Title | Date 17
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
Green SigmaTM – Carbon Management Dashboard
Document Title | Date 18
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 19
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
Extracting InformationWe 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
Document Title | Date 20
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
Document Title | Date 21
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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)
Document Title | Date 22
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 23
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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)
Document Title | Date 24
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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 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
Document Title | Date 25
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Acc
urac
y (A
UC
) 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
Document Title | Date 26
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
BANTER (Blog Analysis of Network Topology and Evolving Responses)
77M Blogs
Technology Blogs Enterprise 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
150
200
OpenID Buzz in January
days
Num
ber
of O
ccur
renc
e
OpenID Buzz in January
OBJECTIVE: Apply machine-learning to extract business insight from technology-based blogs
Document Title | Date 27
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 28
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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.- Prediction markets- Peer-to-peer services- Personal benchmarking
Document Title | Date 29
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 30
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 31
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
Political Examples…
Document Title | Date 32
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
Public prediction markets?
Document Title | Date 33
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 34
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 35
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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.
Document Title | Date 36
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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?
Document Title | Date 37
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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?
Document Title | Date 38
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
http://www.slideshare.net/JeanChristopheCapelli/20080329-social-lendingbar-camp-bank-sf
Document Title | Date 39
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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.
Document Title | Date 40
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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?
Document Title | Date 41
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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.
Document Title | Date 42
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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
Document Title | Date 43
IBM Business Analytics and Mathematical Sciences
© Copyright IBM Corporation 2008
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