We use cookies and other similar technologies (Cookies) to enhance your experience and to provide you with relevant content and ads. By using our website, you are agreeing to the use of Cookies. You can change your settings at any time. Cookie Policy.

Blog: James Taylor Subscribe to this blog's RSS feed!

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.

May 2009 Archives

Gary Cokins had a great post - Fill in the blanks: Which X is Most Likely to X? in which he identifies some great uses for predictive analytics.Increasing employee retention, increasing customer profitability and increased shelf opportunity are classic uses. What Gary does so well in this post, though, is point out that a prediction is not enough - you must take action. For example, knowing which employees might leave will not help unless management intervenes. All too often I hear folks talk about predictive analytics as though the prediction is the end game. And when I hear this I always say "so what?". For instance:
  • We can predict which customers are at high risk of churn - so what? What decsion(s) will you make differently as a result?
  • We can predict which products are most profitable - so what? Can you change the way your website makes offers to promote the ones that are more profitable?
  • We can predict which transactions have high fraud risk - so what? Can you mix this risk with policies and regulations so that you can intervene effectively and legally in a real-time process?
This whole area was the focus of a webinar I gave for bettermanagement.com on Putting Predictive Analytics to Work and you can watch the recording on the website. You might also like the White Paper of the same name that's up on the BeyeNetwork site.

Posted May 26, 2009 8:38 AM
Permalink | No Comments |
Merv Adrian recently posted on Information Builders Prepares to Ramp It Up and this made me think of webFocus. Like Merv I recently spoke with Michael Corcoran and learned a little more about Information Builder's attitude to decision making and information.

The webFocus page says "Because Everyone Makes Decisions" and pushing information access and analysis to front-line workers, customers, consumers is clearly a big focus at IBI. In this context I am fond of a quote from Peter Drucker "Most discussions of decision making assume that only senior executives make decisions or that only senior executives' decisions matter. This is a dangerous mistake".

Now IBI moved to a web-based architecture a long time ago (webFocus came out in 1996) to try to change the dynamics of potential users - not just a few desktop users but massive numbers of end users and customers. While this was a little early for many, today a web-centric approach is clearly mainstream. This focus on distributed access over the web combined with their "If you can buy a book online you can get your own information on demand" interface for guided ad-hoc reporting makes the product particularly interesting for non-technical users.

One of the interesting side effects Michael discussed was that of behavior change in those folks who were given access to information, especially information that allowed them to compare themselves to others. It turns out that people with more information about their performance take their performance more seriously.

It is clear that lots of people make decisions and those decisions should be supported by the right technology and for many of those decisions this means making data and analysis of that data available. But the systems those people use should also make decisions. This might mean taking a decision completely out of someone's hands and automating it or, more likely, automating all the easy ones (80-95%) and leaving the user to handle the tricky ones. It might also mean automating the process of determining context for a decision - helping a user focus on the 3 viable options not the 300 possible ones. This is where decision management comes in.

I wrote about pushing BI beyond business managers before. I think one of the most important steps a company can take in adopting the right mix of business intelligence and decision management is to be explicit about the decisions it is trying to influence. Once you know that, you can look at each to see if it should be supported or automated or some mixture of the two (using automation to restrict the array of options available, for instance, to a shorter list). Leaping in to using BI to support a decision or rules to automate one without having given enough thought to the who/what/where/when of the decision is unlikelly to result in the best outcome. BI, especially BI for consumers and front-line staff can and should be balanced with decision management.

Posted May 18, 2009 1:01 PM
Permalink | No Comments |
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 is available publicly.
He has a point, of course. Lots of data is available publicly and using it won't necessarily give you much of a competitive advantage. What I think he misses is the value of data that you have about your customers and their behavior. You know, or at least could know, which of your customers bought what and when. You can track what they look at on your website and map that to your products and offerings. You can track who calls and what they call about. And you can use this data to segment them, make predictions about them and assess them. To a large extent, your competitors cannot do this. This gives you some critical, defensible, advantages:
  • You should be able to make retention offers that are more compelling than the acquisition offers your competitors make when trying to steal away your customers
  • You should be able to target your customer acquisition efforts on those people who look the most like your existing customers - after all people like that chose you over your competitors before.
  • You should be able to enhance the publicly available data with your own data to form a picture that is richer and more actionable than someone working from the public data alone.
Of course all this only works if you have the ability to effectively and rapidly develop and use analytic models based on this data (to minimize decision latency) and, in particular, if you have a way to put these analytics to work in the production systems that interact with customers and prospects. Putting a decision management framework in place allows you to do both these things, turning your unique data into decisions that are, in fact, defensible.

Posted May 12, 2009 1:07 PM
Permalink | No Comments |