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/
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
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!
“Sessions flow together well and build upon the concepts for the series which makes the learning easy and better retention.
The instructor is knowledgeable and very attentive to the audience given the range of attendees skill and knowledge of the subject at hand. I enjoy her training sessions.”
Deborah – American Family Insurance
“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
“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
“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!”