Posted on

Blaze down in Fair Isaac’s Q1 2012

FICO reported 9% growth in revenues year over year.

  • the bulk of revenues and all the growth was in pre-configured Decision Management applications
  • FICO score revenues were half as much, w/ B2B growing as B2C (myFICO) waned
  • tools revenues were less than half again as much and flat
    • optimization (XPress) was up
    • Blaze Advisor was down

This is in sharp contrast to the success that Ilog has enjoyed under the IBM umbrella.

Blaze Advisor doesn’t seem to make sense as a stand-alone tool any more.   Applications are great, and so are combinations of BI/optimization/rules, but if the BRMS tool will survive independently it needs to find more traction, perhaps outside of Fair Isaac.

Posted on

Pursuing a decision tree down a rat hole

Fair Isaac’s recent press release touts the “key differentiator” of Blaze Advisor 7.0 as:

the innovative Decision Graph visual metaphor, a decision tree management solution that makes even the most complex rule sets easier to manage and explain

Of course, a decision tree is really more like a root system (i.e., the tree is upside down).  So, what this capability is particularly good for explaining how some pretty structured logic gets wherever it lands up, which FICO touts:

The new capability is especially valuable to businesses that need to be able to explain their decision logic to external auditors and regulators, or to internal parties such as senior management.

Unfortunately, this capability is only good for business logic that has been heavily analyzed and transformed from more natural forms of knowledge that stakeholders and regulators understand or communicate about.

FICO’s suggestion is that after operational guidelines and regulations have been laboriously translated (literally and, hopefully, appropriately) into if-then-else logic (i.e., decision trees) that viewing the path through the “code” will be informative to stakeholders and suitable for regulators.  That seems like quite a stretch given the immediately following sentence, which indicates the complexity of using the metaphor in the first place:

Decision Graph gives business analysts a more intuitive way to view and navigate decision trees, which can reach 10,000 nodes or more.

Lots of people are attracted to such “visual programming” metaphors, but they are extremely limited in their logical expressiveness and, therefore, in their utility.  Still, if you are using induction technology (such as FICO’s)  to produce very large decision trees (independent of governing policies or regulations) such a tool can be useful, if only to understand what the machine “discovered” or to explain “how” it reached a decision, albeit post facto, which may or may not be compliant with governing doctrine.

The press release also talks about using this structure to experiment or optimize certain decisions by simulation, which is good stuff.  FICO has long led in this area, especially in the markets it focuses on (i.e., B2C financial).  This would have been a better central point, imo.

Overall, I agree with the following quote embedded within the release:

“Business rules management provides a shared platform for CIOs and business managers to help their enterprises stay competitive, and making business logic clearer to all parties is an essential part of that collaboration,” said Jim Sinur, a vice president at Gartner Research specializing in business rules management systems. “Better visualization of business logic can provide a huge uplift for companies that are looking for ways to improve business decisions.”

Showing thousands of nodes in a tree or cells in a table does not accomplish the appropriate goal (i.e., effective collaboration) of the first clause, however.  And Jim did not say that decision trees provide effective visualization.

My take: the best approach is to guarantee that the statements of business policy and regulation are unambiguously understood by machine intelligence that automatically translates them into completely reliable systems.

That is, the best visualization for general purposes may be plain English.

Posted on

IBM Ilog JRules for business modeling and rule authoring

If you are considering the use of any of the following business rules management systems (BRMS):

  • IBM Ilog JRules
  • Red Hat JBoss Rules
  • Fair Isaac Blaze Advisor
  • Oracle Policy Automation (i.e., Haley in Siebel, PeopleSoft, etc.)
  • Oracle Business Rules (i.e., a derivative of JESS in Fusion)

you can learn a lot by carefully examining this video on decisions using scoring in Ilog.  (The video is also worth considering with respect to Corticon since it authors and renders conditions, actions, and if-then rules within a table format.)

This article is a detailed walk through that stands completely independently of the video (I recommend skipping the first 50 seconds and watching for 3 minutes or so).  You will find detailed commentary and insights here, sometimes fairly critical but in places complimentary.  JRules is a mature and successful product.  (This is not to say to a CIO that it is an appropriate or low risk alternative, however. I would hold on that assessment pending an understanding of strategy.)

The video starts by creating a decision table using this dialog:

Note that the decision reached by the resulting table is labeled but not defined, nor is the information needed to consult the table specified.  As it turns out, this table will take an action rather than make a decision.  As we will see it will “set the score of result to a number”. As we will also see, it references an application.  Given an application, it follows references to related concepts, such as borrowers (which it errantly considers synonomous with applicants), concerning which it further pursues employment information.

Continue reading “IBM Ilog JRules for business modeling and rule authoring”

Posted on

Goals and backward chaining using the Rete Algorithm

I was prompted to post this by request from Mark Proctor and Peter Lin and in response to recent comments on CEP and backward chaining on Paul Vincent’s blog (with an interesting perspective here).

I hope those interested in artificial intelligence enjoy the following paper .  I wrote it while Chief Scientist of Inference Corporation.  It was published in the International Joint Conference on Artificial Intelligence over twenty years ago. 

The bottom line remains:

  1. intelligence requires logical inference and, more specifically, deduction
  2. deduction is not practical without a means of subgoaling and backward chaining
  3. subgoaling using additional rules to assert goals or other explicit approaches is impractical
  4. backward chaining using a data-driven rules engine requires automatic generation of declarative goals

We implemented this in Inference Corporation’s Automated Reasoning Tool (ART) in 1984.  And we implemented it again at Haley a long time ago in a rules langauge we called “Eclipse” years before Java.

Regretably, to the best of my knowledge, ART is no longer available from Inference spin-off Brightware or its further spin-off, Mindbox.  To the best of my knowledge, no other business rules engine or Rete Algorithm automatically subgoals,  including CLIPS, JESS, TIBCO Business Events (see above), Fair Isaac’s Blaze Advisor, and Ilog Rules/JRules.  After reading the paper, you may understand that the resulting lack of robust logical reasoning capabilities is one of the reasons that business rules has not matured to a robust knowledge management capability, as discussed elsewhere in this blog.  Continue reading “Goals and backward chaining using the Rete Algorithm”