5th
March
2009
Continuing some posts on next generation warranty systems in the build up to speaking at the Warranty Chain management conference I thought I would contrast how current generation warranty systems handle critical decisions with how next generation systems do so.
Decision
Today
Next Generation
Is Claim Valid?
Data validation rules are coded into the user interface that captures [...]
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posted by James Taylor in Analytics, Business Rules, Data Mining, Decision Management |
4th
March
2009
Syndicated from ebizQ
John Reynolds over on the Thoughtful Programmer had a great post a little while back – 19th Century BPMS. In it he said
I sometime find it useful to describe a BPMS in terms of things and people that you probably would have found in an office or factory in the 1890s
This struck me as [...]
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posted by James Taylor in Analytics, Business Rules, Data Mining, Decision Management |
4th
March
2009
Syndicated from BeyeNetwork
I recently had the chance to talk with Karl Rexer, President of Rexer Analytics, about their recently launched Data Miner Survey. This is the 3rd year they have run it and I blogged about the results from last year’s back in October.
JAMES: This is your third year conducting this survey, what new [...]
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posted by James Taylor in Analytics, Data Mining |
26th
February
2009
Some weeks ago I got a chance to review the SAS Warranty Analysis product. I was doing some due-diligence before my speech on “Next Generation Warranty Systems” to the Warranty Chain Management Conference in April. The folks from SAS began with an Aberdeen quote from 2006:
Warranty analytics is the number one differentiator between Best in [...]
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posted by James Taylor in Analytics, BI, Data Mining |
24th
February
2009
Syndicated from b-eye network
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 [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |
20th
February
2009
Syndicated from Smart Data Collective
This session was a panel discussion on the cross-industry challenges and solutions in predictive analytics. Panel sessions are tough to blog so here are some highlights.
More and more analysts are having to do their own extract, transform, load work to access databases so having modeling tools that handle this, rather than [...]
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posted by James Taylor in Analytics, Business Rules, Data Mining, Decision Management |
20th
February
2009
Syndicated from Smart Data Collective
Khosrow Hassibi of KXEN and Will Tangalos of Wells Fargo presented together on the challenges of predictive analytics in the real world and on Wells Fargo as an example of how some of these challenges can be met. Khosrow began with an overview of the basic predictive analytic tasks:
Understand the business [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |
19th
February
2009
Syndicated from Smart Data Collective
Natasha Balac from UC San Diego and Michael Zeller from Zementis (their product was blogged here and their support for the amazon.com compute cloud was discussed here) presented on the use of Medicare and Medicaid data to detect and prevent fraud. The high computing center at UC San Diego (San Diego [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |
19th
February
2009
Syndicated from Smart Data Collective
John Elder presented a collection of case studies to showcase the ROI of data mining. John started by making the point that many of his case studies had technical success but not business success – an interesting statistic. John sees three major ways that predictive analytics can help – streamlining, eliminating [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |
19th
February
2009
Syndicated from Smart Data Collective
Anne Milley from SAS, one of the sponsors of the show, spoke on the art and science of better. Data is often messy and the enterprise is not a lab. Nevertheless, she says, we can still bring science to bear. We can observe, define, measure, experiment, learn and ACT. Anne had [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |