The Story of Al’s Spreadsheet and Absent Brains
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Tags: absent brains, Business Rules, knowledge retention, reverse engineering business rules, tacit vs. explicit knowledge
Written by Ronald G. Ross on . Posted in Business Agility, Business Rules, Complexity, Knowledge Retention
Tags: absent brains, Business Rules, knowledge retention, reverse engineering business rules, tacit vs. explicit knowledge
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Jacob Feldman
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Hi Ron,
This is an important and timely post. We all may agree that it is much better to have explicitly expressed business rules than tacit knowledge hidden in “Al’s spreadsheets”. But I suspect that this good advice didn’t bring any relief to the mentioned “national taxation authority”. It would not directly help many other companies that already found themselves in similar situations. My question is: what actually can be done to help these companies?
The technology is moving forward and I believe today we may offer some practical solutions to “Al’s spreadsheets” problems. Your “objective was to reverse-engineer business rules from a very complex spreadsheet” but it seems it was impossible to understand how this magic spreadsheet actually worked. However, the fact is that it did work! It makes me believe that modern predictive analytics tools could be in help.
You wrote that the spreadsheet was “mission-critical and used by many of their systems and embedded in various websites.” It means it was possible to run thousands of test cases, for which this spreadsheet produced specific results. It is also probably reasonable to expect that their business experts were able to evaluate the produced results and to mark them as correct or incorrect. Thus, it was possible to create quite representative training sets with positive and negative results. These sets could be used by a rule learner to automatically generate business rules which produce similar or very close results. The modern machine learning algorithms (such as RIPPER) allow you to make sure that generated rules can be understood, interpreted by experts, and managed be business people, while they also can be executed by a rule engine.
It is not just a theoretical consideration. A few years ago OpenRules did a similar work for the major US taxation authority (http://openrules.com/what_they_say.htm#IRS). We did not had an “Al’s spreadsheet” in this case but we even did not have an access to the rules that were used by this authority to red-flag certain tax returns – naturally these rules is an ultimate secret. We only knew the results (positive and negative) for a quite representative population of already audited tax returns. Applying an integrated machine learning and business rules approach, we managed to generate business rules that our customer found quite valuable. More importantly, their subject matter experts were able to improve these rules. Interested readers may find more information about the proper architecture at http://openrules.com/rulelearner.htm.
I need to clarify that we did not reproduce the same rules as original ones (we would never know them), but the results produced by generated rules were better to compare with original rules. Especially, in this case the generated rules avoided to picking up tax returns which would end up with wasted audits.
Again, to address “Al’s spreadsheet” problems it is important to be able to run “something” (without knowing how it actually works) that produces many positive and negative examples to be used as training data. Another interesting example is described in our Rules Compressor (http://openrules.com/rulecompressor.htm). You will see how a set of multiple business rules may be reduced to a much smaller ruleset while both produce similar results with minimal mistakes.
Thank you,
Jacob