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James Taylor

I will use this blog to discuss business challenges and how technologies like analytics, optimization and business rules can meet those challenges.

About the author >

James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.

February 2009 Archives

Last week I was at Predictive Analytics World, a brand new show on the business value of predictive analytics. The show was a great success, I think, as it attracted a decent audience in very tough times and succeeded in bringing together not just those building predictive analytic models, but also those on the business side trying to learn how to use those models more effectively. All sorts of companies were represented with people using predictive analytics for marketing, retention, workforce management, risk management and much more.

I thought I would share a few thoughts based on the event as a whole:
  • Clearly the ROI of successful predictive analytics projects is good. All the case studies were positive (obviously) but many of them had dramatically improved outcomes.
  • In the current economic climate companies are very focused on reducing costs and one of the great things about the use of predictive analytics is its ability to reduce costs while reducing value by much less. For instance, a marketing campaign that uses predictive analytics to target the best prospects saves money by sending out less mail and yet still targets most of the people who would have responded.
  • Risk management relies on predictive analytics and, particularly, on embedding predictive analytics in operational systems to measure the risk of each customer, each transaction. After all most organizations don't acquire risk in big chunks but one customer at a time. The Federal government, of course, does acquire risk in big chunks as it bails out banks but that's a different story...
  • Organizational issues like a business sponsor and a willingness to effect some organizational change are critical to success with predictive analytics. Most of the negative stories - where something failed - were due to changes in business sponsorship or a lack of ability/will power when it came to making a necessary organizational change.
  • Predictive analytics can give you the power to focus on a niche and so compete with companies who could overpower you if you took them on across the board.
  • Open source products and standards (like R and PMML) are making good progress and support from the commercial vendors for these is likewise growing helping to reduce lock-in and improve interoperability.
  • The technology for developing and managing models continues to improve, not only adding new and better algorithms but more importantly increasing the degree of automated support for development and deployment. Modelers and even business users can do more modeling, more effectively.
  • More and more of the success stories are doing decision management - embedding the predictive analytic models into operational decisions to maximize the reach of the models.
I understand that they plan to run the show in the fall on the East Coast and, if they do, I highly recommend it. Here's the complete set of posts I wrote for the show:

  1. 5 ways to reduce cost with predictive analytics
  2. SAS and the art and science of better
  3. The High ROI of Data Mining for Innovative Organizations
  4. High-Performance Scoring of Healthcare Data
  5. Completing the visitor targeting cycle
  6. New Challenges for creating predictive analytic models
  7. Predictive modeling and today's growing data challenges
  8. The unrealized power of data
  9. Expert Panel on Challenges and Solutions
  10. Predictive Modeling for E-Mail Marketing
  11. Analyzing and predicting user satisfaction with sponsored search

Posted February 23, 2009 10:46 AM
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