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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.

December 2009 Archives

James Kobielus had a nice list of Advanced Analytics Predictions For 2010 over on the Forrester blog. As usual James is thought provoking with some interesting predictions. Let's start with the one's with which I agree most strongly.
  • Advanced analytics sinks deep roots in the data warehouse
    Absolutely. In database/in warehouse analytics will become more and more important and the analytical processing of streaming data likewise. However the way data is stored in warehouses will have to change too, not just the way analytics are done. Too many warehouses and marts today store summary data, rollups or data where the crucial time dimension is obscured. No matter how powerful the analytic engines get, this will have to change and warehouses will have to store the low-level transactional data that analytics need.
  • User-friendly predictive modeling comes to the information workplace
    Yup. While I think there will continue to be a role for experts in building models and that executing predictive models in operational systems is at least equally important, knowledge workers are going to expect tools that let them build predictive analytics for themselves.
  • Social network analysis bring powerful predictive analysis to the online economy
    Yes but not only t the online economy. Social network analysis is a powerful tool in telcos (see this piece on using call detail records to develop networks) and fraud detection already. Social network analysis does not require Social Networks!
  • Low-cost data warehousing delivers fast analytics to the midmarket
    Maybe. Bringing analytics to the midmarket will be more about packaging the analytics up and making them easy to consume than about appliances.
  • Self-service operational BI puts information workers in driver's seat
    I don't think this one is that compelling and I don't see most users demanding these tools. Putting information workers in the driver's seat requires making the BI tools vanish into the day to day systems and processes, not just making them self-service. Most business people want to do what they always want to do which is run their business more effectively. If tools can help with that then they will use them, otherwise not. The number who want to build mashups or self-serve on BI is a small and fairly geeky subset in my experience. Personally I don't expect the major BI vendors to make anything like enough progress in making their tools "vanish" into the systems business users use every day to deliver on this one.
And I know he had one more (Data warehousing virtualizing into the cloud)  but I don't have an opinion about that one.

Posted December 21, 2009 5:26 PM
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Jeff Jonas had a great post on his blog recently, Your movements speak for themselves: Space-Time Travel Data is Analytic Super-Food! in which he made the point that:

Mobile devices in America are generating something like 600 billion geo-spatially tagged transactions per day.

With such huge volumes involved, this information is only going to be useful if it can be analyzed and used. It's real time, high volume and constantly changing. Displaying it on a dashboard or putting it in a report is not going to add much value. Use it to drive real-time decisioning, though, and you could start to add some real value. Of course this leads to concerns of surveillance and I, like Jeff, believe:

Such a surveillance intensive future is inevitable, irreversible and as I have said before here ... irresistible.

But if your customers are generating, or could be generating this kind of information, what could you do with it? Well you could use it to recommend places to go, you could use it to target offers and advertising, you could use it to schedule and route deliveries or repair crews when they are needed, or... well, lots of things.

The point is that your customers ARE generating this kind of information and the question is what are you going to do about it?


Posted December 10, 2009 12:00 PM
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My friends at Zementis have just launched support for executing predictive analytic models in Excel - check out Predictive Analytics at your fingertips: Scoring data in Microsoft Office Excel. While not, exactly, a high-volume transaction environment, Excel is an interesting place for executing predictive models and I like the way the folks at Zementis have done it. The integration of their standard deployment engine means that IT departments have some options - cloud or on premise - for running the scoring engine while still pushing predictive analytics into Excel. I am increasingly convinced that delivering the same rules and analytics to decision support, through Excel say, as are used in automated decisioning systems is important. Most decisioning systems don't handle 100% of transactions so people will be handling the exceptions and it will be useful to them to have access to the same decisioning infrastructure.

Posted December 9, 2009 12:14 PM
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