Enabling Operational Excellence
Enabling Operational Excellence
Enabling Operational Excellence
Enabling Operational Excellence


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Is “Knowledge Worker” the Best Term for Decision Engineering?

In a day and age where the automation of operational business decisions is increasingly the goal, I maintain that knowledge worker is the wrong term for business process modeling. The term is simply too broad. Instead I use the terms white-collar worker and gold-collar worker. What’s the key differentiation?    
    • Gold-collar workers. The work of gold-collar workers involves non-routine problem solving, which requires a “a combination of convergent, divergent, and creative thinking” [Wikipedia].
    • White-collar workers. The work of white-collar workers involves fairly repetitious sets of tasks, which at least in theory should produce relatively consistent results. Also, white-collar workers generally receive much less training than gold-collar workers.
Although the boundary between the two categories is somewhat fuzzy, I believe they generally can be distinguished. Relevant questions include:
    • How routine is the work?
    • How consistent should results of the work be?
    • How much training is required?
As an example, consider loan officers in a bank handling applications for mortgages. White-collar or gold collar?
    • Routineness. I’d call their work relatively routine. Even though each loan application is different and might involve special cases or exceptions, the work is always about mortgages.
    • Consistency. You’d like to think different loan officers could produce consistent results on similar kinds of loan applications. Although certainly true in theory, it’s often not the case in practice. More about that momentarily.
    • Training. Although loan officers do receive significant training and mentoring, it’s not on the order of years as for gold-collar workers.
Based on this analysis I believe loan officers fall into the white-collar category. What about consistency of results? I’ve seen studies comparing results across peers with roughly the same training and experience. The numbers are significantly lower than you might expect. That’s not at all a good thing for either customer experience or the well-being of their organizations. So why not automate the white-collar decision-making work?! Automating white-collar decision-making work well is exactly the focus of business rules and decision engineering. From experience, I’m certain that at least 50% to 80% (maybe more) of the decision work for mortgage applications can be automated, especially if a company is willing to standardize and simplify the adjudication rules some. Huge benefits can be achieved in terms of consistent customer experience, higher productivity, and directly provable compliance. P.S. Mortgage applications are not automated well if rules simply refer applications to humans when the rules cannot handle them. www.BRSolutions.com

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Single Source of Business Truth

Re-engineering knowledge work is the central problem of the knowledge economy. In recent work at Centers for Disease Control (CDC) and current work a major bank in Canada we used RuleSpeak® to create what I call a “single source of truth for operational business IP (intellectual property)”. This is far more than a conceptual data model. Beyond structured business vocabulary its central feature is comprehensive rules. It may be like what some professionals call a “conceptual ontology” (as opposed to an operational ontology to be embedded in IT systems). But we would never use the term “ontology” in our work.  Most business people and SMEs simply wouldn’t ‘get’ that. The idea is that all audiences (or subcommunities) in an organization should work off a single trusted source of explicit know-how (business vocabulary and business rules), no matter what their specific responsibilities:
    • producing training materials for line workers.
    • making changes in operational policies.
    • providing proof of compliance for auditors.
    • creating new products.
    • communicating with IT.
Here are some key observations about our work to create a single source of business truth:
    • Our primary audience is not IT. Yet our work is of sufficient precision that straightforward translation into an implementation form can basically be taken as a ‘given’.
    • Our approach recognizes that people are the essential ingredient in business (as opposed to other kinds of knowledge problems). People can violate rules. For coordinating the work of people, direct support for behavioral rules, not just definitional or decision rules, is a must.
    • Our work could not be undertaken without a structured natural language for business rules like RuleSpeak. The non-IT audiences do need rich business semantics, but they have no desire whatsoever to become semantic programmers. They simply would not commit if the work were conducted on that basis.
    • No one today knows what the optimal syntax is for expressing all forms of business know-how in all circumstances. I suspect there isn’t one. That fact, plus the exponential increase in computer capability for processing natural language, indicates clearly that focusing on syntax is simply the wrong direction. RuleSpeak is based on, and was one of the reference languages for SBVR (Semantics of Business Vocabulary and Business Rules, on OMG standard), which supports a non-syntax approach. A language for ‘speaking’ with computers that is not a computer language – now that’s an idea whose time has definitely come!

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Eliminate the Middlemen of Business Rules: Thinking Fresh for the New Year and Beyond

I have said many times programmers, even semantic programmers, are not the wave of the future for business rules. The future lies with enabling business people and business analysts to have dialogs with machines coming as close to unambiguous statements as they can (e.g., in RuleSpeak). The machine should ask questions to reduce ambiguity to an acceptable minimum. To protect against liability, the machine should log all assumptions, major and minor, in translating to an internal (formal and unambiguous) form so that results are traceable and improvable. Consider IBM Watson. If machines can win at Jeopardy against the best players in the world, that’s a pretty impressive feat for natural language capability. Let’s compare apples to apples. There will always be translation of business ”requirements” from human form to machine form. Even some perfect (a.k.a. formal semantic) implementation language would not reduce translation errors to an absolute minimum. Coders would still have to translate, and errors compound with every translation. So I say disintermediate; eliminate the middlemen – i.e. the coders. If you look across a great many industries, that’s the trend. Why should development of business applications be any different? I also suspect that any “formal semantic” language for business rules would inevitably be English-biased. That’s simply not acceptable in a global economy. RuleSpeak[1] has gathered increasing attention around the world. There are now versions in the works for Norwegian, Polish and Japanese, in addition to the original English and the existing Dutch, German and Spanish translations. A growing number of people find that RuleSpeak strikes just about the right balance between structured and unstructured expression. I’m not saying, however, that other useful approaches couldn’t be more formal – or less formal – than RuleSpeak. Perhaps they could. And that’s been my point for all these years working on SBVR[2] as a business rule standard. We simply don’t yet know the absolute best approach for expressing all business rules in all circumstances. No one knows enough. Perhaps there isn’t one. SBVR is brilliant precisely because it captures semantics without dictating expression form. Long term, that’s exactly what the industry needs. No, there hasn’t been rapid vendor implementation of SBVR since release of 1.0. I wouldn’t expect there to be. It threatens virtually every interface and mindset on the planet. Most people in the IT field still just don’t get it. In retrospect, OMG may have been the wrong forum for SBVR for at least two reasons:

1. OMG is the bastion of best-of-breed programming standards. Obviously there is an important role for that, but as above SBVR isn’t about programming.

2. The SBVR vocabulary is extremely useful for organizing business conversations about business vocabulary and business rules. OMG doesn’t ‘do’ business-facing standards of that kind (i.e., ones that don’t “compute”). IMO, that’s a major shortcoming. There is ultimately nothing more important than improving communication at the level of people. Get it wrong there and I promise it will be wrong in business automation, no matter how elegant the implementation language.

The bottom line is that machines for business (rule) automation now must learn to ‘speak’ human languages. The other way around is simply no longer acceptable – or even necessary. www.BRSolutions.com  
[2] The OMG standard Semantics of Business Vocabulary and Business Rules. Refer to the SBVR Insider section on www.BRCommunity.com for insight into SBVR.

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An Open Letter to Ms. Mona G.

Mona G., Who in the world are you? What do you do for a living? Mona G. recently wrote a review of our book, Building Business Solutions: Business Analysis with Business Rules, on Amazon. Unlike the other 11 reviews, which all gave the book the highest rating of 5 stars, Mona gave it 1 star, the worst. Hmmm. Mona has only written two other reviews on Amazon:
  • Oskri Fiber Bar, Almonds and Cranberries, 0.88-Ounce Bars (Pack of 40) (Grocery)
  • J.Renee Women’s Tova Ornamented Pump (Apparel)
Three reviews … fiber bar, shoes, and Business Analysis book. Hmmm. The book review is very well written. Too well in fact. Let me ask you (the reader) something. What do you usually do if you don’t like a book? I just put it down and never mention it again. I don’t read it carefully enough to write a 5-paragraph bad review. Do you?! A paragraph or two would suffice. You wouldn’t think there are dirty tricks going on in a professional industry like ours, do you?? The review raised two issues of substance worth addressing so I’ll do that here.
  • “In some paragraphs, several words and phrases that would seem unimportant are in bold text, perhaps by accident, leaving the reader wondering why there is emphasis on such unimportant content.”

 Words in blue bolded text are simply words with definitions given in the glossary so as to be crystal clear how they are used in the text. The blue bolding is definitely no accident. The glossary is 55 pages long. I’ve never been criticized for creating a comprehensive glossary before. Who would have thought?!

  • The review quotes the book about ‘wordings’, which are “the verbs and verb phrases that allow you to express what you know about those things in a consistent manner. … You need wordings to write complete sentences….” The review asks, “Really? What is wrong with calling them verbs, which is what they really are?”

 Here’s why by way of an example. A verb phrase like “arrives at” does not mean the same as simply the verb “arrives”. A verb phrase, almost always formed with prepositions (e.g., “at”), carries distinctive meaning. The statement “A plane arrives.” does not mean the same as “A plane arrives at [some specific city].” So you have to talk about “wordings” rather than just verbs. That’s just how semantics works. Too complicated? Geez, don’t know what to say.

Mona seems to have a low estimation of Business Analyst’s reading ability. I beg to differ on that point (strongly). I find most BAs to be highly motivated and quite capable. I guess I am old school, but sometimes I wonder if so much anonymity on the web is a good thing. I mean I would be glad to refund Mona’s money to her … if she would just reveal herself. But I suppose the picture might get ugly if she revealed her true identity. Oh well. C’est la vie. P.S. If you’ve read over the book and disagree with Mona, we’d much appreciate it if you’d put in a good word for us on Amazon.

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A Week of Silly Signs & Rules from China: Day 5 – Beautiful Thoughts

For a little background on this 5-day series of posts see: http://www.brsolutions.com/2013/07/08/a-week-of-silly-signs-rules-from-china-day-1-whats-that-motivation/ 5.1 [As beautiful environment is on all of us, please omnivorously put the waste in the garbage can.] … If you think about it long enough it sorta begins to make sense.   5.2 [Clean environment and civilized behavior co-create the beautiful scenery] … That’s really a deep thought – I like it!   5.3 [The scenic spot becomes more beautiful with your civilized behavior.] … Another deep thought – Good one!   5.4 Civilized behavior of tourists is another bright scenery.] … Maybe because it’s so rare?         5.5 [Don’t tread on the grass as they also have a life.] … Indeed, and yet another deep thought!   5.6 [Only in the sun of civilization can trees maintain evergreen.] … I think this one takes things a step too far.         5.7 [As the fashion color in modern society, Green color is dignified and profound, symbolizing stable and firm career; besides, it is gentle, harmonious and beautiful, implying that family is full of happiness, wish precious jade to walk with you!] … This explains why green is my favorite color … and yes, I bought some so it did walk [out] with me.

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Business Rules ‘Floating in Space’? Not!

Business rules do not ‘float in space’. They mean only what the words they use are defined to mean. So they are tied directly to business vocabulary (concept model), which in turn is represented in a system by a data model or class diagram. These days if approaches for business systems don’t step up to semantics, they’re simply not state-of-the-art. BTW, with machines more powerful every day, they should be ‘stepping up’. That means direct support for structured natural language – e.g., RuleSpeak. ~~~~~~ See the latest on RuleSpeak 3.0 (free download): http://www.brsolutions.com/b_ipspeakprimers.php

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Open Letter re: Decision Models

written in response to Jacob Feldman: http://www.brsolutions.com/2013/05/07/response-to-decisionspeak-tablespeak-annnouncement/ Jacob, Thanks! And I agree with you about the ‘executable’ part. Our emphasis is on business-friendly, business-driven models. I believe DecisionSpeak and TableSpeak move things forward significantly in that regard. There’s no reason why decision models have to be oriented to IT development. If they are robust, they will nonetheless be executable. I would sound a note of caution. Decision models are no silver bullet. There are issues of semantics (vocabulary) and integrity (restrictions) to be addressed. And they don’t cover even the majority of all business rules – especially behavioral rules. If you throw everything you (should) know about business rules out the window when you use decision models, you will be in for a very rude awaking. I’m glad we did not rush to the market. We’ve taken our time to do our homework with respect to theory (which has been out there for a great many years) and to hone our approach in real-life consulting work. I think the results speak for themselves!

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What is Your Company Doing re: Customer Experience?

Just finished reading a column “The Magic of Good Service” in The Economist (Sept 22 – http://www.economist.com/node/21563295). It’s well worth a quick read. Some of the ideas it discusses are below. Apologies for the buzzword salad … The opinion piece suggests these are management fads unlikely to improve much of anything. I’m interested in experience anyone has had with any of these ideas. Drop me a quick comment (Thanks!). I’m especially interested in coupling (or substituting) these ideas with business rules and decision management – which seems to me like the natural thing to do.
  • Chief Customer Officer (CCO): “… some firms have started appointing chief customer officers (CCOs) … These new additions to the … C-suite are supposed to look at the business from the customer’s point of view.”
  • Holistic Customer Experience / Customer for Life: “… [focusing] on the entire ‘customer experience’, rather than on individual transactions.”
  • Journey Maps: “… [identifying] the ‘journeys’ that customers take—such as choosing a product, paying for it or asking for help—and then follow them on their travels to see if they encounter any problems.”
  • Customer Archetypes: “… [inventing] archetypes of different sorts of customers to fine-tune their services.”

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