To understand the future of processes, you must dig a little deeper than many people do.
Process thinking goes back well over a 100 years, to the origin of modern iron and automobile production. The raw materials and finished goods of such manufacturing and production processes are literally spatial – 3-dimensional. What can you do to significantly improve productivity in a 3-dimensional world? The answer these days is simple: You build robots. Robotization has literally changed the world during the past 30-40 years.
Rather than manufacturing and production processes, however, the world is now increasingly focused on white-collar and digital processes. What 3-dimensional presence do the raw materials and finished goods of these processes have?
Well, exactly none. The raw materials and finished goods of these processes aren’t physical and simply have no spatial presence whatsoever (except maybe for paper artifacts). Robots (at least physical ones) aren’t an option. That fact of life makes a huge difference in how you have to think about automation for such processes.
Instead, the raw materials and finished goods of such processes are all about your operational business knowledge – your intellectual capital – and your capacity to express and apply it. That capability, in turn, depends directly on your business terminology and business language. For white-collar processes you have no choice – the world is semantic. So you must deal with the subject matter semantically.
That takes us in a very different direction than most professionals currently foresee. For one thing it takes us toward natural language and away from diagrams-for-everything. That’s a huge shift in mindset. Imagine having a business conversation with your smart phone about gaps and ambiguities in business policies and in the meanings of the vocabulary you use to talk about subject matter knowledge. Don’t think that’s possible? Have you watched your kids talking to their smart phones lately?
Sooner or later businesses will realize that operational business knowledge differentiates their product/services and enables their ever-more-automated processes to function. Capturing, managing and re-using that intellectual capital puts a premium on structured business vocabulary (concept models) and on business rules expressed in structured natural language. Those business rules are the only way you have to ensure quality from white-collar and digital processes.
Read more on this topic:
Are Processes and BPM Relevant in the Digital Economy? http://www.brsolutions.com/2015/10/19/are-processes-and-bpm-relevant-in-the-digital-economy/
Measuring Quality and Defects in the Knowledge Economy: http://www.brsolutions.com/2015/10/27/measuring-quality-and-defects-in-the-knowledge-economy/
Quality and Tolerances in the Knowledge Economy: http://www.brsolutions.com/2015/10/29/quality-and-tolerances-in-the-knowledge-economy/
There are two fundamental kinds of business rules: behavioral rules and decision rules. Behavioral rules are rules people can violate; decision rules are rules that shape knowledge or information. Decision rules cannot be violated – knowledge or information just is what it is defined to be. Common to all business rules, no matter which category, is that you want them directly traceable for compliance and other purposes.
How do behavioral rules and decision rules apply differentially to service workers vs. white-collar workers vs. gold-collar workers?
Service workers are primarily subject to obeying behavioral rules, or are charged with applying them. Examples:
A counter attendant must not accept a credit card for a purchase under $10.
A flight attendant must ensure passengers have buckled their seat belts for each take-off and landing.
Service workers are subject to operational business decisions made by white-collar workers, but do not play a significant role in making such decisions themselves.White-collar workers are typically involved in business processes where operational business decisions are made. Examples:
Should this loan applicant be given a mortgage?
What flight crew should be assigned to this flight?
White-collar workers generally do not define decision rules themselves – that’s typically work for gold-collar workers. Where such rules are incomplete, unspecified or contradictory, however, white-collar workers generally rely on personal heuristics and experience to make decisions. This approach puts the main goals for white-collar work – consistency and traceability – at jeopardy.White-collar workers, like all workers, are subject to behavioral rules. Examples:
A loan officer must not handle a loan application placed by a family member
The website description for a new product must be approved by two senior managers.
Gold-collar workers (for explanation see http://www.brsolutions.com/2014/08/11/is-%e2%80%9cknowledge-worker%e2%80%9d-the-best-term-for-decision-engineering/) are responsible for non-routine, knowledge-intensive work. The primary goals for such work is that it be insightful (e.g., as in the case of medical diagnosis that fits the available data better) or creative (e.g., as in the case of a new marketing strategy). This type of work is generally beyond the scope of decision rules.
Although gold-collar workers often conduct their work in relatively independent fashion, the work is generally subject to “very close normative control from organizations they work for” [Wikipedia]. Think medical malpractice or following generally accepted principles of accounting. These normative controls, since they can be violated, are sets of behavioral rules.www.BRSolutions.com
Based on the OMG standard SBVR (Semantics of Business Vocabulary and Business Rules). For more on SBVR see the SBVR Insider section on www.BRCommunity.com.
Pink-collar worker is a term sometimes used (in the U.S. at least) to refer to a job in the service industry. Many people find the term off-putting because it traditionally referred to jobs relegated to women. I avoid the term for several other reasons. The category includes:
Such roles as buyers, loan interviewers, dieticians, administrative assistants, etc., whose work at the high-end should be considered white-collar.
Many workers providing personal services on an individual basis, rather than business services in the usual sense. Examples include midwives; hairdressers and barbers; baby sitters and nannies; personal shoppers and fashion stylists; etc.
Clearly many businesses do have extensive staff that is neither white-collar nor gold-collar working to deliver services. Examples include retail workers, sales staff, flight attendants, hotel housekeepers, counter attendants, receptionists, etc. I just call them service workers since they don’t have any traditional uniform color – white, blue or otherwise.Are service workers subject to business rules? Absolutely. Generally these rules are behavioral rules rather than decision rules, however, since their jobs do not focus on operational business decisions. More about that in my next post.www.BRSolutions.com
Some people argue that a knowledge worker is someone who gets paid to improvise or innovate, a factor distinct from the amount of training the worker receives. By this criterion even blue-collar workers can be considered knowledge workers if they constantly improvise or innovate. I don’t find the notion helpful. In my mind, a blue-collar worker who is constantly improvising or innovating, for example, has become an engineer – which is gold-collar, not blue-collar. (For explanation of gold-collar work, see http://www.brsolutions.com/2014/08/11/is-%e2%80%9cknowledge-worker%e2%80%9d-the-best-term-for-decision-engineering/)With respect to white-collar work, what I see in many organizations is white-collar entropy, all resulting from continuous and counterproductive ‘improvising’. A vacuum of coordination filled with too much information simply does not translate into a more productive organization. The more likely result is inconsistency, the enemy of good customer experience.The improvise-and-innovate argument also holds that knowledge workers don’t just apply rules – they invent rules. Hang on a minute. To take a real-life example, do we really want police officers (officers on the beat) inventing rules?! I think not. Their job is to apply rules (laws), not invent them. Otherwise we’d be living in a police state. In a well-run organization, just as in society, above all you want consistency at the operational level. If I call my bank ten different times, I should get the same answer ten different times. If I apply for a mortgage from the same bank at ten different branches, I should get the same result ten different times.
In my experience, that’s hardly the norm. Why? If staff works in an environment where many of the rules are tacit, contradictory, ambiguous, poorly implemented, inaccessible, and/or unintelligible, of course the staff will improvise.
Contrary to what some believe, well-defined rules do not lessen creativity (space to improvise and innovate about how to get desired results). That’s not the way it works. Absence of rules is literally anarchy – and only the bad guys look clever in that context.www.BRSolutions.com
In my most recent post, I distinguished between white-collar and gold-collar workers. See http://www.brsolutions.com/2014/08/11/is-%e2%80%9cknowledge-worker%e2%80%9d-the-best-term-for-decision-engineering/Can you differentiate between white-collar work and gold-collar work by whether it can be automated? In a day and age when IBM Watson can win at Jeopardy, I think it’s probably foolish to try.But I don’t think that’s the right question. Instead, I would ask whether the problem spaces are sufficiently distinct that they require different approaches. The answer to that question is definitely yes. That’s one reason I think it’s important not to say simply “knowledge worker” in process models. Companies pay gold-collar workers for their professional insight, creativity, and ability to digest huge amounts of knowledge on a continuous basis. Novel, unexpected results that fit the data better are at a premium. That’s not what companies pay white-collar workers for – or at least it shouldn’t be. Instead, they should pay white-collar workers to produce consistent results on decisions reached through directly traceable logic – that is, business rules. Unexpected results represent a failure – of an individual worker, a training regimen, or the rules themselves.More often than not, I think the problem actually lies with the rules. In many companies, we ask humans to make operational business decisions in a fog of byzantine rules – rules often far more complex than reasonable (or profitable). In addition, the ‘real’ rules are frequently more tacit or inaccessible than anyone cares to admit. In my view we simply have never been serious about defining, organizing and managing the rules in white-collar decision-making in a reasonable, scalable manner. And we certainly haven’t yet harnessed the power of computers to help with the business-side problem of rule management.www.BRSolutions.com
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
I certainly understand the need for data models, and that fact they should be coordinated/integrated with process models. Who would question that these days?! But to re-engineer business decisions or knowhow-intensive business processes (or both), you need a structured business vocabulary – i.e., a concept model. The purpose of a concept model is to provide the terminology and wordings to write hundreds (or thousands) of rules coherently and consistently. Building such blueprint is not an insignificant undertaking. Yes, a concept model can be used as the basis for designing a model of the data needed to support processes, but that’s not its primary objective. Rather its purpose is to understand what the business rules and decision logic are talking about business-wise at ‘excruciating level of detail’ (to borrow a phrase from John Zachman).
A concept model involves hundreds of terms, some whose meanings are obvious, some whose meanings you think are obvious but aren’t, and some whose meanings are simply mysterious. We constantly have to caution against setting expectations too high about how much integration based on business semantics can be achieved on relatively short notice. Even though it seems like ‘defining business terms’ should be relatively easy, concept modeling is by far the hardest work we do. The problem in virtually every organization in the world today is that these business semantics have never been developed well enough (think semantic, rather than functional, silos) to take automation of logic to the next threshold – i.e., to white-collar automation. Yet that’s exactly where a great many organizations currently want (and urgently need) to go.www.BRSolutions.com
“Instructors were very knowledgeable and could clearly explain concepts and convey importance of strategy and architecture.
It was a more comprehensive, holistic approach to the subject than other training. Emphasis on understanding the business prior to technology considerations was reassuring to business stakeholders.”
Bernard – Government of Canada
“I found the course interesting and will be helpful.
I like the pragmatic reality you discuss, while a rule tool would be great, recognizing many people will use Word/Excel to capture them helps. We can’t jump from crazy to perfect in one leap!
Use of the polls is also great. Helps see how everyone else is doing (we are not alone), and helps us think about our current state.”
Trevor – Investors Group
“We actively use the BRS business-side techniques and train our business analysts in the approach. The techniques bring clarity between our BAs & customers, plus more robust requirements for our development teams. We’ve seen tremendous value.”
Jeanine Bradley – Railinc
“You did a wonderful job!! The material was organized and valuable.”
Janell – Texas State University
“A great class that explains the importance of business rules in today’s work place.”
Christopher – McKesson
“Your work has been one of the foundations of my success in our shared passion for data integration. It has had a huge impact on innumerable people!”