I am giving a keynote at the Drools bootcamp in San Diego. The bootcamp has some days focused on the healthcare industry as well as some more general sessions

I am giving a workshop on Advanced Decisioning for Process Excellence at the Gartner BPM event in Las Vegas.

I wrote an article on the basic steps to Decision Management for BR Community this month and you can check it out here: How to Get Smarter with Decision Management. BR Community requires a (free) registration and is well worth it for those interested in business rules.

Got a quick update today from the new co-CEOs of SAP – Bill McDermott and Jim Hagemann Snabe.

Jim focused on their innovation strategy – making significant steps into on-demand business applications, aiming to support a hybrid approach allowing customers to mix on-demand and on-premise software. In addition they aim to increase support for running the applications on new mobile devices – this, of course, requires a separation of decision-making business logic from front-end logic. Hopefully this will see SAP investing more in its business rules capabilities (described under the SAP tag on the blog). All of this requires that processes and MDM can be orchestrated across this increasingly complex environment, even when non-SAP application components are involved. They are also rolling out more agile development methodologies (like those being described in the new SAP BPM book on which I am working with various other SAP folks).

Lots of interesting questions got asked and here are some of the responses that seem most interesting from a decisioning perspective:

• In memory analytics will change the way high end analytics are deployed. Focused on a variety of partners to bring new approaches, new techniques into high-end analytic space. Still expect to work with SPSS in this regard but also looking for new technologies that take advantage from the ground up of in-memory analytics.
• Business by Design, SAP’s easy to configure on-demand (SaaS) offering, is coming out this summer. Will be interesting to see the extent to which business rules are used to make it configurable.
• Interesting challenge for a company like SAP is that different product lines, different deployment options have a different cycle. On premise software, for instance, cannot be updated too often as customers don’t want to constantly re-install. On-demand software, however, gets updated more often and on-device software is driven by a very dynamic consumer technology market. This is a large scale change, ensuring that different parts of the company can operate on the right timescale while remaining part of the same company. Personally I think that rules-based decision evolution is a key element of this and I hope to see some sign that SAP thinks this way too.
• Asked about mergers and acquisitions – the point was made that Oracle has been much more aggressive – Bill and Jim acknowledged that they are going to be more aggressive going forward while remaining focused on innovation and an integrated, coherent business application suite rather than generating growth through acquisitions. As more and more established customers have been acquired (up to the acquisition of Chordiant by Pegasystems today) this is an interesting topic – increasingly SAP will have no option to grow through acquisitions but this may suit their corporate culture better anyway.
• Asked about the trend (Oracle, IBM) to mix hardware and software they replied that they see a heterogeneous world that is in constant flux – customers never own one vendor’s complete set – so being good at working in this environment is key. Eliminating layers using hardware is good but they see working with multiple partners not owning their own. This requires collaboration with a mix of hardware partners rather than acquiring and integrating their own hardware. Customers don’t want vendor lock-in, they buy a business outcome not a “stack”.
• SAP is not worried about the ownership of Java by Oracle – they see a vibrant, open, multi-company ecosystem around Java and don’t expect Oracle’s ownership to impact this. Interestingly they made the point that programming languages come and go and that Java is not therefore the be-all and end-all.

Interesting conversation, nice degree of openness and responsiveness – much improved over SAP TechEd where avoiding questions was the order of the day.

The news today is that Pegasystems (rules-based business process management) is acquiring Chordiant (decision-centric CRM). This is interesting news as it merges a company (Chordiant) with a very decision-centric/decision services separate from process mindset with one (Pega) that has mixed rules and process together much more.

Chordiant have been one of my companies to watch for a while, with a great decisioning platform. Their clear separation of decisioning, their support for rules and analytics in combination, their strong adaptive analytics engine for self-learning models, their recent integration of real-time conversations with decision management and their powerful business simulation tool (Visual Business Director, see below) are enough to put them at or very near the top of the decisioning vendors.

Pega, of course, have been best known for their business process management focus. They have always addressed this from a rules-centric perspective and we have had some active disagreements about the role of decision services and the value of a clear separate of processes and decisions (see this post and comment thread, for instance). Nevertheless we agree on the power of business rules to drive more agile and smarter systems and Pega has been one of the rules vendors active in supporting PMML (Predictive Model Markup Language) to allow the integration of business analytics with business rules.

The potential of this merger is real. Clearly the merged company will be larger, important as the big players (IBM, SAP especially) get more serious and rules and decisioning. Chordiant’s decision management and simulation components are, in my estimation, better than Pega’s for specific purposes but not as general purpose. An intelligent combination of the two is therefore potentially very powerful. In particular, bringing Chordiant’s adaptive analytics and simualtion capabilities to the broader rules-based platform that Pega offers could be great. In addition both are very focused on CRM or at least on customer treatment decisioning, and this should help give the merged company a clear focus.

The risk, of course, is that the fairly serious difference of perspective between decision-centric / decision management on one hand and rules-driven BPM on the other will derail the technical integration or cause the merged company to merge its operations without merging its products. Either will ensure that the talented people behind the products will not stay and that would be a pity. The merged company must figure this out and make some clear statements on product direction and positioning in this respect – though I appreciate that this can’t be done right now, as it must wait for regulatory clearance etc. There is a lot of overlap in technology. This could be good – giving the merged company enough of a common vocabulary to build a powerful solution – or bad, resulting in lots of infighting about which version to keep.

Check out these posts on Chordiant for more details. The folks at Pega have never seemed to want me to blog about them so I don’t have anything about them on the blog. Hopefully this will change…

• First Look – Chordiant Decision Management
• First Look – Chordiant Recommendation Advisor
• First Look – Chordiant’s Visual Business Director
• Chordiant Decision Management Update

Disclosure: Chordiant was a customer of mine in 2008/2009

Syndicated from ebizQ

I am working with some folks at SAP on a new BPM book – Applying Real-World BPM in an SAP Environment. I am working on chapters about the role of decisions in processes (check out this post for some help on this topic) and on the use and management of business rules in automating decisions. For those of you working with SAP NetWeaver or BRFplus I think and hope you will find the book really useful.

If you want more information, download the RealWorldBPMPostcard.pdf

I was traveling in South Africa last week (keynoting BI 2010) and my favorite online payment system demonstrated not once but twice, why business rules are so valuable in analytic decision making. First their analytics triggered a fraud alert – presumably based on patterns of problems from South African IP addresses. As I was trying to make a payment to a vendor I have used before, a simple rule could and should have overridden this (if the payment is to someone who has been paid before without problems then it is not likely to suddenly become fraudulent). But it did not and I had to go through the unblock account process.

Sadly the unblock account process assumed you were at home. Of course, I was not, and there was no way to unblock the account. This is a problem as most fraud alerts are false positives (that’s just how it works) so the assumption that I am traveling would have been more reasonable than that I was at home. Some rule to say that, if it was me and not a fraudster, that I was likely to be away from home and so could not answer my home phone would have been helpful. As it was I had to leave the account suspended until I got home.

The account is now unblocked but, trying to be helpful, I sent them a note explaining the situation. Sadly their use of text analytics was also rules-free. Picking up the words in my email about suspended account processes it simply sent me a note about how to unblock my account. Again, a simple rule would have discovered that I had, in fact, already unblocked my account and so probably did not need help on that topic. This would have allowed them to route it to someone who could have answered my email instead of merely irritating me with an automated response.

Analytics can detect fraud and give you a good sense of what an email says. But explicit business rules can map that insight to current business conditions and make sure you action makes sense. Use them together and make better decisions.

Doug Henschen has a great interview with Deepak Advani of IBM, the new head of IBM’s newly acquired SPSS business (and I am not just saying that because he mentions Smart (Enough) Systems).  I am looking forward to seeing what IBM does with the combination of ILOG and SPSS, along with InfoSphere, WebSphere, FileNet and Cognos.

Alan Fish has another great post over on his blog – DRA: Rules Discovery. He makes what I consider to be an essential point that is easily forgotten – you are not trying to find rules just so you know what they are, you are finding rules so you can make better decisions! Check out this post on the differences between decisions, rulesets and rules for a little more on this. This is part of what I call decision discovery.

Optimization is a mathematical process for finding the best decision for a given business problem – usually highest profit, lowest cost given a set of constraints. Involve applying an algorithm to data, decision variables, constraints and an objective function. In financial services and insurance optimization is still fairly new (unlike, say, supply chain) but the complex regulatory environment and tradeoffs between risk and reward are ideally suited to it. It also helps with champion/challenger and experimental design. Combining optimization with business rules, and with predictive analytics, is a growth area and I got an update from FICO on how they see these technologies working together.

Business rules and optimization allow you to use rules for flexibility and agility and optimization to get to “best” faster. FICO provides  FICO Decision Optimizer as a packaged solution that solves specific banking optimization problems, and FICO Xpress-Mosel as an optimization modeling tool for solving a wide range of industry problems.  Both of these products can be combined with  FICO Blaze Advisor to leverage business rules management.   With these tools, FICO can offer a number of ways to integrate rules and optimization:

• Use Blaze Advisor to deploy an optimized strategy tree created using Decision Optimizer or Xpress optimization results
• Invoke a configured Xpress-Mosel optimization model from a Blaze Advisor decision service (Mosel is the Xpress modeling and programming language)
• Use rules to configure the parameters of the Xpress-Mosel model and then execute it
• Blaze Advisor could provide the input data to Xpress-Mosel Models
• A core model in Xpress-Mosel with Blaze Advisor providing pieces using rules execution t assemble the pieces
• A skeleton model in Xpress-Mosel with Blaze Advisor providing the Xpress-Mosel Code and data using rules to assemble it

For example in debt consolidation use inputs and preferences to find the best payoff loan. The customer has several exiting debts, at different rates of interest, and wants to optimize for payment or pay off period etc. The optimization model does the tradeoff while the rules manage the eligibility of the customer for specific products that might be available for the pay off choice. The optimization engine gets to pick only from the eligible products (the rules for this are already being used elsewhere in most companies so this allows the rules to be reused not repeated for the optimization problem). This can also use predictive models e.g. to predictive price sensitivity (the model is used to calculate a value that is input to the model).

Business rules, optimization and predictive analytics are also being used in FICO Retail Action Manager to optimize marketing spend. Uses the business rules management system to ensure consistent and targeted messages across channels, predictive models to predict who will buy what and an optimization model to pick the optimal offer and channel given the constraints you have.

Retail space planning was another solution that included rules and optimization. Retailer was trying to maximize profitability in the “planograms” or shelf layouts that were being developed. This used predictive analytics to predict how likely customers might be to pick more expensive products if they are positioned correctly, rules for defining the constraints like competing products or store layout consistency as well as contractual requirements from suppliers. Optimization handled the tradeoffs.

I see more and more use cases for business rules, optimization and predictive analytics in combination. The move to considering these complementary technologies as a platform for decisioning is welcome.