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Blog: James Taylor« September 2008 | Main | November 2008 » October 30, 2008Optimizing Customer Lifecycle ManagementCopyright © 2008 James Taylor. Visit the original article at Optimizing Customer Lifecycle Management.David Griffith from CUNA Mutual Group talked about predictive analytics in a B2B environment. CUNA targets credit unions and cooperatives and their members with software and insurance products. CUNA Mutual needed to acquire new credit union accounts for direct insurance products - credit unions who sign up can offer a full range of insurance products in return for access to the credit union customers. The program was mature and already had 50% market share and incremental sales were a challenge. Plan was to target the 50% of credit unions analytically and focus on those credit unions likely to sign up and those whose members were likely to participate. The approach was to develop look-alike targets using analytics by analyzing product performance and sales rates/pipeline. Used Angoss’ KnowledeSeeker tool to develop decision trees - the power of the tool for exploratory work and the ease of communication of decision trees were key drivers. First step was to find credit unions who performed well - whose members signed up. 80 variables that described the credit union (asset size, branches, state, type….) and 20 variables that described the membership of the credit union (ages, income etc). Focused on credit unions that had become members of the program recently to see which ones would be good. The tree quickly identified those with higher rates of married members as doing better than average, for instance. Credit unions with average numbers of married members and high rates of auto loans coming from auto dealers were very below average. And so on - what kinds of credit unions will be more successful. Second step was to find targets based on sales activity. First thing was found that credit unions with multiple products like 401K were more likely to join this program. To make this work they had to focus it on the actual decision, which was going to be executed by the sales team. Developed a little 3×2 grid mapping likely/unlikely to buy and above average/average/below average memberships. Did the scoring and then mapped scores on to the grid and loaded the grid into the CRM system - essentially the action being recommended - so that the sales people could use it. Models worked. There was a 20% difference in actual performance between above and below average performance accounts and almost twice the response rate in more likely to buy v less likely to buy. Conclusions:
Biggest challenge was overcoming the cultural barriers to a change in the sales targeting approach.
New Approaches to Creating, Simplifying and Visualizing RulesCopyright © 2008 James Taylor. Visit the original article at New Approaches to Creating, Simplifying and Visualizing Rules.Stuart Crawford from Fair Isaac’s R&D group presented on New Approaches to Creating, Simplifying and Visualizing Rules. While decision trees can be very clear, they can also become very complex. His group has been working on algorithms for simplifying decision trees. Because decision trees often have repeating sub-trees - pieces of the tree that are identical but in different paths - a Directed Action Graph is often much simpler. The graph can re-use the logic by linking to it in multiple ways. Building one of these requires some fairly complex math to find the right nodes and ordering. While these are simpler they can still be complex so the next step is to develop what is known as an Exception-based Directed Action Graph or EDAG. This takes the most common outcome and puts it at the top and then only has nodes for the other paths. In other words the action at the top is the default. However this is not always enough. He showed an example of a 492 node decision tree reduced to 89 nodes in an EDAG. A 30,000 node (!) tree from a bank was reduced to a 480 node graph. These are clearly simpler but not simple. An Action Graph is the next simplification - take an action and find all the logic that could result in that action. This becomes a single action graph. Complex trees and graphs can have many of these action graphs extracted from them and each is focused and easy to read. Of course you could in fact author this way. Each action graph can be built separately, ordering can be changed and different variables used. Once you have the individual fragments you can merge them into a single decision tree or EDAG. The action graphs can be stitched together but you have the potential for errors when they are built separately like this. They can overlap - have two paths in different graphs that are the same but have different actions - or have gaps - where certain values are not covered across the various action graphs. Unless you can manage these overlaps and gaps, you cannot use the individual graphs for development. Finally this approach allows you to generate comparisons between trees. Instead of showing structural differences, which can be confusing, logical differences can be shown as an EDAG. Much simpler. I saw Stuart present this technology before and blogged it here.
Integrating Predictive Analytics and BRM to Improve Health Plan Member ExperienceCopyright © 2008 James Taylor. Visit the original article at Integrating Predictive Analytics and BRM to Improve Health Plan Member Experience.Two gentlemen from Deloitte presented Integrating Predictive Analytics and BRM to Improve Health Plan Member Experience. 80% of healthcare costs are incurred by 20% of members and traditionally the 20% get all the focus. Analytics and data mining get applied to claims, authorization, costs as a result. Segmentation focuses on the unprofitable and unhealthy. Increasingly segmentation and analytics are being applied to managing the people who are not yet sick, though a lack of data and focus is an issue. Business rules management has also been applied in healthcare but almost only in process-centric areas like claims processing or fraud for instance. Some case management and care management is beginning as are connections between different parts of the member lifecycle. Health insurance companies are facing some major issues. Premium revenue is dropping and less profitable products are becoming more popular. In addition there is a shift to consumerism and individual choice from company coverage. Healthcare costs, meanwhile, have become a more and more significant element of disposable income and this is beginning to force trade-offs between healthcare and other expenditure. Fewer employers are offering healthcare and more people are opting out/becoming uninsured. These changes are creating new “infomediaries” like webMD who are trying to own the information relationship between consumers and health providers, new products from traditional insurers, new competitors as retailers and financial services target healthcare with products for individuals using their more analytic and targeted marketing skills. All this means that health plans need to attract new members and retain existing ones by creating loyal members whose primary medical relationship is to the plan. This requires both predictive analytics to develop insight and business rules to push these insights into production - decision management, in other words. Part of what is driving the more effective use of predictive analytics and rules in healthcare is the broader base of data available - claims data used to be the main source but this only applies to a small percentage of the members. Using demographic data, lifestyle data, census data and other sources of information about individuals enables much more holistic modeling and segmentation and this data has been shown to be very predictive of future health risk. Taking these new data sources, aggregating and cleaning this data and integrating it with claims data drives new segmentation for members. Rules-based decision making can use this segmentation and models for targeted outreach, incentive programs, compliance programs, personalized customer service and improved disease management. All the consumer-facing decisions throughout the member lifecycle. An example model is one that predicts the likelihood a member will dis-enroll in the next 6-12 months. 80-100 variables get used to create a model that generates a probability score. This might use some attributes from traditional data but also things like demographics, distance to primary care, active gym memberships etc. The score might be used to group people into Low Touch (not likely to dis-enroll), Average and High Touch (likely to dis-enroll). Rules can be used to ensure minimal outreach to the Low Touch group but instead focus on quality and medical management while also focusing outreach efforts on the High Touch group. Segmentation can be used to understand how to reach the consumer, what products and services they want, what support they need and their value. This segmentation can be used, with rules, to drive better decision making in sales and marketing, pricing, customer service, medical management - decisions throughout the lifecycle. Some of these decisions are ones familiar to plans while some are new. For instance, member rewards/loyalty decisions can be driven very effectively with these approaches and this is new area for most plans. Medical management is one they always thought about but new data sources can be used to improve the analytics and rules being used to drive these decisions. End results:
This session touched on many of the same issues that came up when I was working with Silverlink. Pretty classic “why use EDM” stuff.
Building Blocks of Decision ManagementCopyright © 2008 James Taylor. Visit the original article at Building Blocks of Decision Management.Michele Edelman of Discover presented on Building Blocks of Decision Management: “Tools to Rule”. Michele spends a lot of time educating people inside Discover and her team use sources like McKinsey to show executives why EDM matters. For instance, a report on top 10 macro-economic trends:
All this leads to business reasons for EDM. Discover’s definition of EDM is
EDM to Discover is about automating decisions to enhance business performance - approve/decline decisions (their most important decision), marketing decisions, credit limit assignment (second most important) and underwriting decisions and many more. Interestingly Discover focuses on the really tough decisions first not a low-risk, start small approach. This has worked for them although it is a higher-risk approach. To give a sense of the scale of their work, one decision has 1,200 decision points and hundreds of rules and model attributes are used in the credit limit decision! The business objectives for Discover’s use of EDM are to:
The decision management group is an “enterprise utility” separate from but works closely with the IT group and the drivers for their business architecture are precision (increasing intricacy of models, delivering robust and accurate data), consistency (processing controls), agility (event-driven processing, adaptive v predictive decisioning, reusability/extensibility) and performance (millions of daily decisions, scalability, business forecasting). Michele made the point that “coding it right” is not the issue with decisioning - business impact is. Making sure the business can simulate and forecast the business impact of a decision change is critical. Their data strategy involves pulling a lot of different sources of data together into a robust, reliable platform for delivering data. Initially this was focused on the modeling analysts but the availability of the same data in production as was available to analysts for modeling was critical for ensuring that models could be developed and then pushed into production. As a note, a small data project for Discover is to add a few hundred attributes and a large one is 1,000! The analysts and reporting both have the same timeliness also - overnight updates. For performance reasons they use Teradata on the data side. Authorization has a separate and more limited view of data for both performance and reliability reasons - authorization cannot be allowed to go down or take too long. Some of the data used for authorization is generated by models that run overnight. From a tools perspective they try to avoid focusing on specific products - they use two decision engine platforms (Blaze Advisor and Strata), several modeling tools (SAS, Model Builder). They are focused on parallelism and avoiding duplication. A business user environment allows them to manage the rules and models in the engines and they have a single administrative interface for multiple Unix/Mainframe instances. Deployment is handed off to the technical folks but all the rule and model management is handled by the business. From a results perspective:
Lessons Learned
Michele made the point that they have not done a formal ROI study, in part because the costs of the whole program pale into insignificance relative to the value created. She had a great phrase “execution latency” and emphasized that in the current climate how valuable the agility and change management that EDM provides. As she said “there is no data on the future”! Discover is one of those companies that are truly adopting EDM. They have gone from rules-based decisions to statistical models and data mining to account level economic models to customer level economic models but see that there is more to do, more analytics to use and more data.
EDM Summit - Day 3 BeginsCopyright © 2008 James Taylor. Visit the original article at EDM Summit - Day 3 Begins.Day 3 starts early - 8am for the first session. The Expo closed yesterday and today will be just content. Yesterday was an interesting day with lots of discussion among the attendees of the Oracle acquisition of Haley. Here are the blog posts I found for yesterday
October 29, 2008Oracle buys HaleyCopyright © 2008 James Taylor. Visit the original article at Oracle buys Haley.Well after SAP bought YASU and IBM bought ILOG everyone figured Oracle had to move and they did today, buying Haley. Interestingly the announcement seemed focused more on software for government agencies than on rules as part of Oracle’s platform so perhaps there is more to come regarding rules in Fusion. Interesting times.
Predictive Analytics Produces Business Rules That DeliverCopyright © 2008 James Taylor. Visit the original article at Predictive Analytics Produces Business Rules That Deliver.Eric Siegel, who is chairing the new Predictive Analytics World show, presented on predictive analytics and business rules. Predictive analytics, says Eric, is a business intelligence technology that products a predictive score for each customer or prospect … and explanations thereof. These scores come from predictive models that are developed across your historical data. This historical data is, at some level, a collective memory for the company and is a core strategic asset. You must learn from this data. Predictive analytics allows your organization to learn from its collective experience and puts this knowledge to action. Let’s say, for instance, that people who buy life insurance are likely to buy a luxury sedan. This is knowledge discovery but you must decide what to do - you might use this knowledge to drive offers to this group (do cross-sell) but not others (don’t discount).
Predictive analytics is particular effective in the low value, high volume operational decisions (Micro decisions, for instance). Because the actions taken in these decisions are individual and per-customer, predictive models work perfectly to improve them. Some key concepts:
Key online applications - content selection, retention and product recommendations. Retention, for instance, is much cheaper than acquisition. Improved retention often has a high ROI (NPV of 75%, growth of 12%) and predicting who is at risk of churn and what might prevent them is very effective. Eric walked through an example of an online dating site where they targeted dating defections. Training set of 57K subscribers with about 20% likely to leave. Predictors included length of membership, how they were acquired, residental location and others. For instance, twice the churn rate seen in people who were trying to chat AND were less than 237 days AND less than 1.85 days since their last failed login. Lots of small discoveries like this one. Some derived rules are obvious but:
Rules in tables, spreadsheets and diagramsCopyright © 2008 James Taylor. Visit the original article at Rules in tables, spreadsheets and diagrams.Jan presented on Rules in tables, spreadsheets and diagrams: Towards High Definition Communication. Decision tables are ways to represent sets of rules and there are many ways to represent sets of rules including trees and graphs. Some ways of representing rules are clearer than others and some are better for validation of the rules. You need ways to represent rules that work for specification, execution and verification/validation. Conceptually a decision table is
(Jan’s definition). Decision tables can be horizontally or vertically oriented. Developing a table from a list of rules can be an effective way to find the missing rules - a complete decision table often has more rules than the specification of it as it helps identify the gaps and issues. Jan gave a couple of nice examples where apparently simple rules resulted in a table that immediately showed some gaps and problems. Consistency by construction. To deliver consistency by construction it must contain the correct columns and all the relevant ones. Jan draws a distinction between multiple and single hit tables. Multiple hit tables - where multiple cells match a situation - are either first hit analyzed left to right or all hits where you must evaluate all the possible ones before knowing the answer. Jan does not like multiple-hit tables and additive scorecards are the only good example of this I can think of. Single hit tables are preferred because they are more declarative - sequence to columns/rows does not matter. Ensuring completeness by construction means laying out the structure of the table so that it can be validated without business know-how - the structure tells the story. Each condition is layered so that every possible combination becomes a column. Once every column has an outcome, and just one, it can be simplified but every possible situation must be covered and covered once. In addition, Jan reminded us that business rules constrain and determine the process so, as several speakers have noted already this week, hard-coding rules into process designs is a bad idea. Processes are prescriptive or procedural but business rules and decisions are declarative. Jan has done some research on which formats people like. Decision tables come first, decision trees second and textual description is third. Complex (oblique) rules come last.
EDM Summit - Day 2 BeginsCopyright © 2008 James Taylor. Visit the original article at EDM Summit - Day 2 Begins.Getting ready for my keynote and wanted to post a few quick things. Firstly other bloggers: Sandy Kemsley, Paul Vincent and Mike Kaviz are all here and are/will be posting. Here are the links I found so far:
Let me know if you find some others. In other news, ILOG has regained the market leader position in business rules from Fair Isaac. In their press release they report that IDC’s 2007 market share report puts them first. ILOG clearly had a very strong 2007 but good news in the report is that the market continued to grow, though I suspect that 2008 will be tougher and, with ILOG joining IBM, more complicated. Finally there was an interesting editorial in the NY Times yesterday - The Behavorial Revolution - that was all about decision making. I don’t have time for a long post on it today but I will come back to it after the show. It’s an interesting column though. On with the show.
October 28, 2008Rules and Process Management for InsurersCopyright © 2008 James Taylor. Visit the original article at Rules and Process Management for Insurers.Chubb has been working with Blaze Advisor to automate a number of decisions. They began with specialty lines underwriting (automated renewals), claims severity calculation and work queue assignment. Current focus is on integrating predictive models and some legacy modernization. The automated renewals project reduced the time to make renewal rule changes from 3-6 months of IT work to 2-3 days of IT work. The project contains a rule maintenance application and a batch cycle that runs the renewals through the rule server based on the current set of rules in the repository. The rule maintenance application was based around what they called “levers” - each lever is an attribute that can be used as part of the renewal decision. There is also a What-If feature. This allows business users to see the results of a proposed change in the rules using a year’s worth of data. The project had no quality problems, generated some good teamwork and hit its schedule. The What-If capability, a decision simulator really, seems like a crucial component. Disappointing lack of information about what Chubb has been doing and too much generic Fair Isaac stuff but oh well.
Risk Management at SunCopyright © 2008 James Taylor. Visit the original article at Risk Management at Sun.Risk management is necessary at Sun as new products are constantly being introduced. Each time there are challenges getting information out to people. Also find the same problem repeatedly in different geographies and were challenged to share information about problems and solutions between teams. By 2001 they found over 300 user developed applications supporting risk analysis and mitigation and some of these were even being offered to clients, creating problems with global clients getting inconsistent services. in 2001 tried to replace this with a single, in-house engine but this did not have the flexibility needed and was technology-based not business focused. 2005 decided to buy a rule engine and use that to deliver rule services that could be rapidly developed and evolved as fast as the old user developed applications while improving governance and quality. The new approach was a significant change for business users who were comfortable with their user developed applications. The new approach had to be fast - 6 months was allowed - and the fact that it took only 5 months made people nervous. International obstacles were real and they had to establish the new system as a source of active knowledge not just content. Key success factors:
It was important that they could replace their home grown applications and increase the speed to market. It was also important not to tie it to specific use cases or situations but to provide something more independent and easily accessible - become a platform for new applications. The system allows users to check an installation for accuracy, patches and versions needed etc as well as get proactive notification of potential problems. The system delivers a lot of rule services like Bad/Withdrawn Patch analysis, Security Path analysis, Recommended Patches - easy to use and answers critical questions. In each case they have a database component - a list of withdrawn patches, for instance - and wrap rules around it to use telemetry and systems data to find the bad patches that are in use / relevant. The systems have an API, a web interface for users and a command line interface. Big focus on reusable components. The project focused on simplifying and easing the development of rules, enabling more complex decision-making and replacing all the existing systems. A global information model was critical to this. Besides Blaze Advisor for the rules, they also focused on XML, XSD, XSLT, XQL to standardize interactions and use MySQL for storing logs. It was important to them to have an internal revolution - new approach, new technology - while looking like a gentle evolution to users. Key to this was abstracting data using data services, representing everything in XML and understanding these data structures. The use of the rules technology also allowed the subject matter experts to directly create the rules that could then be used in the automated services, often externally to the rules engine with updates being applied to the rule repository programmatically. Sun has built a risk infrastructure that allows them to globally deliver new information within 24hrs. Now run 7Bn rules a month, 9,000 active users. Benefits:
Check out http://www.sun.com/service/preventive/
Hotwire.com Revenue ManagementCopyright © 2008 James Taylor. Visit the original article at Hotwire.com Revenue Management.Darren Koch presented on Hotwire.com’s use of ILOG business rules in revenue management. Summary:
Hotwire.com was founded in 1999 to help travel partners (who invested) sell excess inventory without driving down prices over all. Now part of Expedia (with TripAdvisor and Expedia.com), 9th largest travel site and focused on short, last-minute trips. Revenue management is a challenge for Hotwire.com as suppliers don’t want to have cannibalization and want to maximize their revenue while offering a discount price. From a web perspective they have to sort results to expose best deals while managing the fact that deals are “opaque” in that consumers don’t know which hotel is in fact represented - must generate trust but also use price markups that are optimal. Originally their revenue management process took intuition as to what might work, did some reporting, developed a challenger for price markup approach in Excel and stored the pricing model in an Oracle table. This table grew exponentially and became a real problem as it was a fixed structure that took months to make changes. Also could not improve sorting of orders. Using business rules allowed rapid change to customer behavior as well enabling real-time optimization. Now smaller and smaller segments are developed to incrementally improve the results. They use adaptive control - champion/challenger testing - to test new approaches on small samples before putting the more fine grained strategy into production for everyone. Quickly found pros and cons. Pros:
Cons:
Revenue management team now uses SAS and does complex predictive modeling. When these models offer improvement it is easy to figure out which rules are required as a result and ILOG allows them to implement the rules quickly and accurately. Improvements seen:
The project took 2 business/3 IT people for 6 months with a couple of weeks technical consulting. 2 business analysts now do everything to do with the rues and 1 IT person to handle site/rule engine integration and synchronization.
Live from the EDM Summit - From Here to AgilityCopyright © 2008 James Taylor. Visit the original article at Live from the EDM Summit - From Here to Agility.I am at the EDM Summit this week and will be blogging live from some of the sessions and posting random thoughts and comments in addition. Despite the difficult market conditions, attendance looks good with a nice full room for the keynote and attendees from 17 countries. This year’s event also has a dozen new sponsors - nearly half are first timers - which I take as a great sign for the market as a whole. Ron Ross gave the opening keynote and began with a discussion of the current financial crisis. Clearly this is going to result in more regulation and some soul searching in companies about their processes and systems. Most companies are not doing things as smart as they could and the current climate makes that painfully clear. To illustrate the challenge, Ron told a story of a large health care organization that spends 400 person-days over a 4 month period to make moderate business rules changes as part of spending 24 person years per year on maintaining a key legacy system. They feel that they work for the legacy system, not the business and worry that they can no longer think through innovation as they are so used to being constrained by the limitations of the systems. He contrasted this with a medium-sized financial services company that used business rules and a business rules management system (BRMS) to cut their elapsed time to change their fraud detection approaches from 30-60 days to 3-6 days. First project, no prior experience, no real methodology and a 10x improvement. The first application more than recouped the cost of the BRMS and related costs. They also realized, however, that really taking advantage of the technology and approach would take organizational change and new business approaches. There is no silver bullet for business agility. Ron moved on to discuss the balance between business process management (BPM) and decisioning/business rules. Clearly process improvement and process management is important to companies but the opportunities to use decisioning to improve processes, to innovate new products and services and thus change the business. A balance is needed between process and decisioning. Companies are looking to find the “optimal edge” for their products and operations - trying to marry analytics, optimization and business rules to find and then operate at the optimal edge. This requires an understanding of your strategy, goals and measures to track it and clear linkage to the operational decisions being made every day. Performance management and alignment are critical to staying on the optimal edge. Business agility is often thought of simply as being able to respond quickly, make changes quickly. More accurately it is being able to respond to change in the time it takes to make the business decision and no more - the time to ensure it is the right change from a business perspective but with no additional overhead. As Ron put it:
Our goals as we engineer business agility should be that:
Ron spent a little time talking about operational decisions (check out this article for more) and about the need to reduce the time to get insight (derived from their data) into action. You must close the loop - decide, act, capture information, analyze it, deploy the insight and decide anew. Putting this insight to work requires an understanding of and control of business rules in production. Business processes and business process management will not do this - decisioning within these processes will do. Business Agility will take:
To get there from here you cannot afford to put your organization on life support. Must be able to improve the current systems by finding and externalizing problem decisions and taking gradual control over them using business rules. Deploying these rules as a decision service allows you to put the right rules to work throughout your application portfolio. You will know you are there when you can deliver:
October 24, 2008Going to the EDM Summit/Business Rules ForumCopyright © 2008 James Taylor. Visit the original article at Going to the EDM Summit/Business Rules Forum.Next week Neil and I will be at the EDM Summit/Business Rules Forum the whole week so say hi if you are there and look for posts from the show.
Not just web personalization, extreme personalizationCopyright © 2008 James Taylor. Visit the original article at Not just web personalization, extreme personalization.Tim Walters of Forrester had an interesting post this week - Is Web Personalization Now A Matter Of “Thurvival”? in which he emphasized that, even in a downturn, getting better at web personalization has a payoff. Now I think personalization is a good thing and the evidence that it results in more engagement, better results and increased loyalty is pretty widespread. I don’t think the question should be about web personalization though. Why should the experience I have on the web be personalized but not the one at the call center or by email or at the ATM? Consumers react to your decisions as though they are personal and deliberate regardless of channel. The negative impact of generic and accidental decisions in terms of customer annoyance, disenfranchisement and lost loyalty is real. Only a focus on your customer-facing decisions and a plan to make these personal and deliberate will correct this. I call this extreme personalization. After all, if you are not personalizing the live agent channel then your live agents could be hurting, not helping and your ATMs will annoy not sell.
October 23, 2008Evidence-based (decision-centric) CRM ProcessesCopyright © 2008 James Taylor. Visit the original article at Evidence-based (decision-centric) CRM Processes.Graham Hill wrote a piece on Evidence-based CRM that focused on evidence-based CRM programs and it made me think about evidence-based CRM processes. To me, evidence-based CRM means customer relationships, and thus customer treatments, that are based on evidence (data) and not judgment, hope, guesswork etc. It means
Making CRM evidence-based is, I think, another way of saying that CRM should be decision-centric. If you externalize and manage the decisions that impact your customers - that contribute to the customer experience and so build the customer relationship - then you can drive those decisions with evidence. If you don’t then you can’t. Evidence-based decisions would:
In other words, Enterprise Decision Management, EDM, is the best way to deliver this kind of evidence-based CRM. While on this topic you might want to check out a couple of articles in DM Review this month. First is this one on Next-best offer and the use of predictive analytic - well worth a read for a nice overview. The second is Six Best Practices for Delivering a Successful Customer Experience. In this Ray talks about a customer-focused strategy and gives a great example:
Well of course I completely agree - an offer service is after all a decision service by another name. Ray also makes the point that organizational transformation and continuous measurement are critical for improving the customer experience just as they are for improving the decision making that drives them.
October 21, 2008How many different kinds of decision management are there?Copyright © 2008 James Taylor. Visit the original article at How many different kinds of decision management are there?.Well at least one more as of today - Jim Sinur, over on his Gartner blog - has finally started to use the phrase he has been threatening to use for a while “Intelligent Decision Management”. While Jim has not published a formal definition - I expect he will soon now he is back at Gartner - a few clues exist in the post.
Like the phrase Neil and I use in Smart (Enough) Systems - Enterprise Decision Management - Intelligent Decision Management is going to use business rules, analytics, optimization and simulation to take control of the critical decisions that run your business. Jim will, I am sure, add his own twist to this but I suspect he is on very much the same page as we are. Whether you call this Enterprise Decision Management (EDM), Intelligent Decision Management (IDM), Deciison Management or Business Decision Management the overall direction is clear. It is time to start managing decisions.
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