8th
June
2009
Syndicated from ebizQ
Robert Grossman had a nice post – Five Common Mistakes in Analytic Projects that made me think about the role of decision management in putting predictive analytics to work. Of Robert’s 5 mistakes, 3 are directly addressed by decision management:
There is not a good plan for deploying the model
Because decision management is focused [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |
13th
May
2009
Syndicated from BeyeNetwork
In Is Data a new defensibility? Abhishek Tiwari argues that even data is not defensible any more. He argues that data integration and the use of new sources of data are key skills but that companies cannot use unique data to differentiate because there is too much data and too much of it [...]
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posted by James Taylor in Analytics, BI, Data Mining, Decision Management |
1st
May
2009
SPSS Inc. likes to say they focus on helping customers capture all the information they need, predict outcomes and then, using their Decision Management products, act on these insights by embedding analytic results into business processes. Within this family, the PASW Decision Management tools add actions, business rules, to analytics to enable action to be [...]
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posted by James Taylor in Analytics, Data Mining, Decision Management |
6th
April
2009
Today SPSS Inc. announced new product releases and new naming for its product families. Predictive Analytics Software (PASW) is the new umbrella and the four families are layered below this. The four families are Data Collection, Statistics, Modeling, and Deployment. This week’s enhancements are to the modeling family. In addition, Clementine becomes PASW Modeler 13 [...]
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posted by James Taylor in Analytics, Data Mining, Product News |
1st
April
2009
Syndicated from BeyeNetwork
Last week I posted a couple of times about my impressions from the SAS Global Forum. In one post I said that “SAS customers talk about the great results they get when they put their predictive analytics to work in operational systems” so I thought I should expand on that a little, using [...]
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posted by James Taylor in Analytics, BI, Decision Management, Optimization |
24th
January
2009
Jerry Cuomo has been talking about WebSphere in 2009 and he published his top 10 list on his blog WebSphere: Into the wild BLUE yonder!.
Business Mash-ups
Business Rules
Middleware-as-a-Service
Rainmaker
Extreme Scale
WAS.NEXT
Restful – Agile
DataPower-lution
POWERful Middleware
Industry-savvy Middleware
He expanded this list with some additional thoughts in an article on InfoQ. Serveral of these – business mash=ups, business rules, Middleware-as-a-Service and Agile [...]
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posted by James Taylor in BPM, Business Rules, Decision Management |
15th
January
2009
With the business world in a state of flux and everyone worried about what might happen next, and how they might respond to it, scenario testing (and its compatriot, stress testing) should be top of mind for executives. They should be thinking about different scenarios, testing out how those scenarios would effect their business and trying out various alternatives. On the risk side they should be using this kind of scenario planning to stress their assumptions – stress testing – to see how their financial reserves would cope with the various alternatives.
For too many executives, however, this kind of testing is done only at the aggregate level and done largely (if not completely) in Excel. I have nothing against Excel but this is clearly not really acceptable. Good scenario or stress testing should consider how customers, products, suppliers, locations will be impacted by the scenario at a granular level and then present rolled-up results, not simply attempt to model some averages or totals. Similarly, if executives want to develop alternative scenarios that would be effective in certain possible futures then they need to test those scenarios against actual transactions, actual customers, to see if they work.
Companies that have adopted decision management have the infrastructure to manage this. Decision management brings the crucial decisions – choices of actions – into the open and makes them explicit. Scenarios can be developed for these decisions and tested against real data. The results can be compared against what happened, or against alternative scenarios to see what would work best. Different assumptions can easily be fed into the decisions to see what impact those assumptions have and stress testing or scenario development conducted based on the results. Decision management makes all this possible. It’s still work, but it is much less work and the results can be much more precise and grounded in real decisions.
A growth in scenario management was one of my predictions for 2009 and Jim Sinur wrote a nice piece on this too – Scenario Planning is No Longer Optional.

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posted by James Taylor in Business Rules, Decision Management |
18th
December
2008
I just went back to check and found no predictions on the blog for 2008 (so I get a 100% accuracy rating with no errors) so I thought I would make some for 2009. In no particular order then:
Cloud computing will impact decision management.
There are already at least two decision management vendors offering decisions in [...]
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posted by James Taylor in Analytics, BI, BPM, Business Rules, Decision Management |
3rd
December
2008
Charlie Berger and some others presented on using data mining for fraud detection. Fraud is a huge issue – for instance there is $31B annually in insurance claims fraud (10-15%) with 25% of all claims have some fraud and more than 1 in 3 bodily-injury claims from car crashes involving fraud. Other industries have similar [...]
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posted by James Taylor in Analytics, Data Mining |
3rd
December
2008
Marty Gubar presented on Deliver Depper Insight by Combining Data Mining and OLAP. Marty presented on Oracle’s analytic spectrum and how OLAP and Data Mining fit and can be combined. OLAP and data mining are embedded in the Oracle database and share security, the partitioning, ETL etc. All can be accessed using PL/SQL so cubes [...]
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posted by James Taylor in BI, Data Mining |