Monday, April 23, 2012
Full-day Workshop
R for Predictive Modeling: A Hands-On Introduction
Click here for a detailed workshop description
Instructor: Max Kuhn, Director of nonclinical Statistics, Pfizer
Tuesday, April 24, 2012
Full-day Workshop
The Best & the Worst of Predictive Analytics: Predictive Modeling Methods & Common Data Mining Mistakes
Click here for a detailed workshop description
Instructor: John Elder, CEO & Founder, Elder Research, Inc.
Wednesday, April 25, 2012
7:30am - 7:30pmExhibit Hall Open
Registration & Breakfast
[ Top of this page ] [ Agenda overview ]
9:00-9:45am
Keynote
Persuasion by the Numbers: Optimize Marketing Influence by Predicting It
Speaker: Eric Siegel, Program Chair, Predictive Analytics World
[ Top of this page ] [ Agenda overview ]
Diamond Sponsor Presentation
[ Top of this page ] [ Agenda overview ]
Gold Sponsor Presentation
[ Top of this page ] [ Agenda overview ]
Breaks / Exhibits
[ Top of this page ] [ Agenda overview ]
Healthcare
Case Study: Pfizer
Right Medicine, Right Patient
As companies gather and collect more and more data across their organizations, the "last mile" of actionable insights based on that data has become increasingly crucial to success. Public contests provide a potent, rapidly expanded means to facilitate vastly better predictions for your companies, by accessing over 16,000 leading data scientists around the world who compete to produce the best results for any given data problem. By correcting the existing mismatch between companies needing better predictions and data scientists wanting access to the most challenging problems and real world data, public competition proves over and over to be a "win-win-win".
Speaker: Max Kuhn, Director of Nonclinical Statistics, Pfizer
[ Top of this page ] [ Agenda overview ]
11:10-11:30am
Sponsored Lab
Lab Session: Live Topical Demo
[ Top of this page ] [ Agenda overview ]
Special Featured Session
Multiple Case Studies: Anheuser-Busch, Disney, HSBC, Pfizer, and others
The High ROI of Data Mining for Innovative Organizations
Data mining and advanced analytics can enhance your bottom line in three basic ways, by 1) streamlining a process, 2) eliminating the bad, or 3) highlighting the good. In rare situations, a fourth way – creating something new – is possible. But modern organizations are so effective at their core tasks that data mining usually results in an iterative, rather than transformative, improvement. Still, the impact can be dramatic.
Dr. will share the story (problem, solution, and effect) of nine projects conducted over the last decade for some of America's most innovative agencies and corporations:
Streamline:
- Cross-selling for HSBC
- Image recognition for Anheuser-Busch
- Biometric identification for Lumidigm (for Disney)
- Optimal decisioning for a leading high-tech retailer
- Quick decisions for the Social Security Administration
Eliminate Bad:
- Tax fraud detection for the IRS
- Warranty Fraud detection for a leading high-tech retailer
- Highlight Good:
- Sector trading for WestWind Foundation
- Drug efficacy discovery for Pharmacia & Up John (now Pfizer)
Speaker: John Elder, CEO & Founder, Elder Research, Inc.
[ Top of this page ] [ Agenda overview ]
Lightning Round of 2-Minute Sponsor Presentations
[ Top of this page ] [ Agenda overview ]
Birds of a Feather Lunch / Exhibits
[ Top of this page ] [ Agenda overview ]
Keynote
The Five Myths of Predictive Analytics
Predictive Analytics is powerful, it can help you predict an event or a behavior at a an individual customer level. It can help you spot golden nuggets from the deep-wide-big data ocean; But is also one of the techniques which is not very well understood. With all the recent buzz about Predictive Analytics, it does seems like a new technique in the tool box. Is that so? In this keynote, we will ground ourselves in the reality of building and maintaining an impactful Predictive Model and explore questions like
- Is Predictive Analytics new?
- Is it a crystal ball?
- Is it perfect?
- Can it be built quickly and cheaply?
- Is it going to solve all my business problems?
- Does it always work?
- Can anybody learn how to build a model?
Speaker: Piyanka Jain, CEO, Aryng.com, Former PayPal Business Analytics Head
[ Top of this page ] [ Agenda overview ]
Gold Sponsor Presentation
[ Top of this page ] [ Agenda overview ]
Breaks/Exhibits
[ Top of this page ] [ Agenda overview ]
Financial Services
Case Study: CIBC
Creating Value Segmentation to Drive Business Strategies
Segmentation is used in a variety of ways to help organizations understand customer behavior and tailor their services. This case study presents how CIBC built a value based segmentation that focuses on customer's current and potential value to identify business opportunities and risks. Adoption and driving change in business strategy and processes are also covered. The concepts involved are not limited to banking and should be generally applicable to all industries.
Speaker: Daymond Ling, Senior Director of Modelling & Analytics, CIBC
[ Top of this page ] [ Agenda overview ]
Crowdsourcing Data Mining
Case Study: Capital One Financial
Competitive Training of Predictive Modelers
Competition between teams for prizes and awards is a well known mechanism for unleashing the creative forces of predictive modelers to develop new modeling techniques, methodologies, and approaches - and, of course, build better models. The Netflix prize competition is a recent example of the power of such competition and coopetition. Capital One Canada set up a competition amongst students of predictive modeling at the University of Waterloo to see what they could do on the kinds of problems typically faced by a lending institution. The format was an integrated case study involving business strategy development around a core predictive model. Perhaps not surprisingly, student-built models didn't outperform models built in-house, but the students' creative approaches and outside-the-box thinking were truly inspiring. Most importantly however, the demand by students and faculty to work on real problems and data sets was almost overwhelming and the feedback by students about the importance of this competition for learning about predictive modeling was surprising. Since student learning is the primary value of this kind of competition, we suggest that multiple institutions could combine efforts to create excitement about predictive modeling and establish a mutually beneficial relationship between academia and industry.
[ Top of this page ] [ Agenda overview ]
Break / Exhibits
4:25-4:45pm
Case Study
Top 10 Data Mining Business Mistakes
Most analytics talks are technical – describing algorithms, data management, software options, etc. – that best extract value from data. And, great technology helps, in our experience, over 90% of projects to meet their technical goals. However, only about 65% of solutions seem to actually be deployed at the client organization. Astonishingly then, business risk has proven far greater than technical risk as an obstacle to realizing the huge ROI possible from predictive analytics.
This talk focuses on the business pitfalls of managing a data mining engagement, complementing John Elder's popular technical chapter on Top 10 Data Mining Mistakes (also covered during his pre-PAW workshop). We address organizational and management mistakes commonly made by either the client or the consulting firm, and illustrate select ones with real-world examples. Anyone who is considering or actively engaged in mining data will benefit from these cautionary tales!
Speaker: Jeff Deal, Vice President of Operations, Elder Research, Inc.
[ Top of this page ] [ Agenda overview ]
Case Study: Adobe
Speaker: John Bates, Adobe
[ Top of this page ] [ Agenda overview ]
Text Analytics
Case Study: Indian Customs Board
Text Mining in Advanced Government Compliance Set-ups
Commodities imported into a country are typically classified through an Internationally Harmonized Coding system up to 8 digits. It is often inadequate as the bundle of commodities at the lowest level are also quite varied in material, value, significance and therefore their pricing leading to inadequacy of reference bands. Text Mining has not only provided an in road into sub classifying the commodities on the basis of their descriptions but have also helped identify risk parameters that link well to assess evasions and mis-declarations. It is possible to evolve strings of risky key words and prevent the facilitation of suspicious consignments.
Speaker: Kamaljit Anand, Global Head of Client Delivery, KIE Square Consulting
[ Top of this page ] [ Agenda overview ]
5:40-6:00pm
Insurance
Case Study: Crawford & Company
Predictive Analytics in Property Insurance Claims: Findings and Lessons Learned
To gain advantage in the highly competitive insurance market, property insurers are increasingly looking at using predictive analytics to optimize claim processing. However, achieving sustainable value has not been easy because of variety of reasons. If used properly, predictive analytics in property claims can provide tremendous cost saving and quality improvement. We mined property claims 'descriptive, transactional, and unstructured data' to determine what works, what does not, how to use predictive models, what challenges to expect, and what benefits to expect. This presentation is a case study that walks the audience through the objectives, methods, findings, and lessons learned.
[ Top of this page ] [ Agenda overview ]
Reception / Exhibits
[ Top of this page ] [ Agenda overview ]
Thursday, April 26, 2012
7:30am - 7:30pmExhibit Hall Open
Registration & Breakfast
[ Top of this page ] [ Agenda overview ]
Keynote
Open Question Answering
Putting IBM Watson to Work
IBM's Watson captured the imagination of millions when it beat the all time champions of the US game show, Jeopardy!. To do so, it overcame traditional limitations of computers by communicating in natural human language, churning through 200 million pages of unstructured data to find answers in three seconds, and learning from each experience to improve performance over time. But as impressive as this accomplishment was, it was only the beginning. IBM is working closely with leading organizations in a variety of industries to put Watson to work. The possibilities are endless! Join Edward Nazarko, a leading IBM Architect, in an engaging discussion of ways that Watson is using predictive models to revolutionize expectations of how computers can help organizations in all industries live and work better.
Speaker: Edward Nazarko, Client Technical Advisor, IBM
[ Top of this page ] [ Agenda overview ]
Diamond Sponsor Presentation
[ Top of this page ] [ Agenda overview ]
Breaks / Exhibits
[ Top of this page ] [ Agenda overview ]
HR Analytics
Case Study: U.S. Special Forces
Hiring and Selecting Key Personnel Using Predictive Analytics
Hiring and selection of personnel in specialized work environments incurs huge direct and opportunity costs for organizations. One of the largest challenges is that the selection process is often left in the hands of those with either high experience in the domain area but little experience in selection or vice versa. Predictive Analytics and statistics can play a critical role in formalizing and automating much of the selection process. This session provides an overview of the selection processes using both measures of skills and psychological measures to quantify IQ, domain knowledge, grit, and determination. Examples will be drawn from hiring practices for Special Forces (such as Army Rangers and Navy SEALs) and predictive analytics teams.
Speaker: Dean Abbott, President, Abbott Analytics
[ Top of this page ] [ Agenda overview ]
11:30am-12:15pm
Insurance
Case Study: Alberta Motor Association
Insurance Pricing Models using Predictive Analytics
The use of predictive analytics solutions as a pricing tool for insurance is a very recent phenomenon amongst actuaries. This case study examines what tools were used in the past and what has led to the adoption of predictive analytics solutions within the actuarial discipline. Particular emphasis is devoted to the significant data challenges which are unique to the insurance pricing sector. At the same time, attendees will learn the process that was adopted in building these tools. More importantly, attendees will understand how to demonstrate the value or benefit of predictive analytics solutions over existing actuarial tools.
Speaker: Richard Boire, Partner, Boire Filler Group
[ Top of this page ] [ Agenda overview ]
Birds of a Feather Lunch / Exhibits
[ Top of this page ] [ Agenda overview ]
Expert Panel
Wise Enterprise: Best Practices for Managing Predictive Analytics
Your company is trigger-happy for predictive analytics, and there's plenty of excitement, momentum and public case studies fueling the flames. Are you destined for success or disappointment? Is it a sure-fire win to gain buy-in for a promising analytics initiative, equip your most talented practitioners with a leading solution, and pull the trigger?
This panel of leading experts will address the holistic view. What are the most poignant and telling failures in the repertoire, and where is the remedy? Beyond the management of individual analytics projects, what enterprise-wide communication processes and other best processes provide best security against project pitfalls? Stay tuned for big answers to these big questions.
Expert Panelist: TBD
[ Top of this page ] [ Agenda overview ]
Gold Sponsor Presentation
[ Top of this page ] [ Agenda overview ]
Gold Sponsor Presentation
[ Top of this page ] [ Agenda overview ]
2:20-3:00pm
Breaks / Exhibits
[ Top of this page ] [ Agenda overview ]
Demand Generation
Case Study: IBM
If We Host It; Will They Come? Predictive Modeling for Event Marketing
Every year IBM (like all large companies) spends millions of dollars on hundreds of Marketing events, from conference participation through seminars to briefing and education events. The 'gold' questions for Marketing are: 1) Are we engaging in the right events, 2) Which events are likely to bring in the most success (measured in terms of revenue), and; 3) Given a finite Marketing budget, how should we spend that budget to optimize the value we are receiving. To answer these questions, IBM's Event Marketing engaged in an exercise developing predictive models to enhance their insight around events activities. This presentation will review the approach, predictive models and will offer some next steps discussion looking into the future of Event Marketing predictive model development.
[ Top of this page ] [ Agenda overview ]
3:25-3:45pm
Ensemble Models; Software Dev Cost Estimation
Case Study: Galorath
Revenge of the Clueless: Combining Many Poor Estimates into an Expert Forecast
Speaker: Mike Kimel, Principal Consultant, Analytic Economics
[ Top of this page ] [ Agenda overview ]
3:45-4:30pm
Breaks / Exhibits
[ Top of this page ] [ Agenda overview ]
4:30-5:10pm
Case Study: Verizon Wireless & BHP Billiton (Australian mining company)
Visualizing Forecast Models with Interactive Scenario Analysis to Optimize Profitability
Speaker: Richard Brath, Partner, Oculus
[ Top of this page ] [ Agenda overview ]
Workshop sponsored by:
Friday, April 27, 2012
Full-day Workshop
Advanced Methods Hands-on: Predictive Modeling Techniques
Click here for a detailed workshop description
Instructor: Dean Abbott, President, Abbott Analytics
[ Top of this page ] [ Agenda overview ]
