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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Ronald G. Ross

Ronald G. Ross

Ron Ross, Principal and Co-Founder of Business Rules Solutions, LLC, is internationally acknowledged as the “father of business rules.” Recognizing early on the importance of independently managed business rules for business operations and architecture, he has pioneered innovative techniques and standards since the mid-1980s. He wrote the industry’s first book on business rules in 1994.

Comments (7)

  • Max Tay

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    In reponse to the article above and a discussion on LinkedIn, I am sharing some of my thoughts.

    Despite the term “knowledge worker” was first coined by Peter Drucker in 1959 , it has over time carries diverse meaning to different people. Here are two quotes from Pter Drucker on the term. “Every knowledge worker in a modern organization is an executive if, by virtue of his position or knowledge, he is responsible for a contribution that materially affects the capacity of the organization to perform and to obtain results.” The Effective Executive, 1966. “Finally, these new industries differ from the traditional modern industry in that they will employ predominantly knowledge workers rather than manual workers.” The Age of Discontinuity, 1969

    The term becomes even more vague when it was adopted by the IT industry. According to an IBM article (IBM Systems Journal, “Ethnographic Study of Collaborative Knowledge Work”, Volume 45, Number 4, 2006) describes someone who adds value in the workplace by processing existing information to create new information which can be used to define and solve problems. Some examples given in that article include managers, salespeople, nurses, doctors, lawyers, judges, and analysts.

    Now my view of knowledge worker and the nature of today’s offer workers.

    As I mentioned in other post, the advancement in knowledge representation in the field of AI and KM has enabled many knowledge to be represented in a well formed structure, such as decision models and business rules described by Ron ald. What was once domain expert knowledge could now be explicitly expressed in structured pieces of interdependent information. With such methods (and technologies) coming of age, it is time to seriously consider the differentiation of the “information worker” to that of the “knowledge worker”. In short as in the LinkedIn discussion, it is generally agreed that a konwledge worker is the one that IMPROVISE and create knowledge such as policies and business rules, and not the one that applies these policies or business rules. Hence most of today’s office workers are NOT knowledge workers per se.

    Another extreme example I like to use is carpenter making a cabinet. Before the days of mass production and assembly line, a carpenter would be viewed as a “knowledge worker”. Even the passing of the knowledge is through master and apprentice nature. Today probably only the designer is the knowledge work left where the rest are repetitive works in manufacturing. IMO that type of changes (evolution of work nature) is similarly happening to jobs in the service industry in recent decades, turning some of the knowledge type works to become information works (assembly line of information).

  • John H Morris

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    Ron, your analysis of the different between types of “office-oriented labour” (even if performed remotely) is terrific. And especially the emphasis on “decision engineering”, which is where a lot of value creation likely takes place.

    Max, your survey of the history of the idea of knowledge work is also terrific. And shows the value of such a survey of the history of an idea. I’ll pick up on your reference to cabinetry and the guild system, i.e. that apprentices would work and learn from masters etc. Of course all this discussion has an economic dimension; Ron’s classification of true knowledge workers as “gold collar workers” is an acknowledgement of that.

    So I will add a little more on the economics of skills question. The history of unionism in both Europe and North America is documented as significantly involving struggles over control of work processes and factory floors. In the beginning, factories would only run because well-trained apprentices or journeyman could be relied upon to bring their knowledge with them. Some would have it that management’s desire for control was a motivator for the de-skilling that may have taken place over the past 100 years. However, others hold that such a position suggests that control is a value in and of itself, and that de-skillng only happened in pursuit of profits. Regardless, all these debates are moot now with the anticipated massive wave of automation.

  • Can You Differentiate ‘Knowledge Workers’ by How Much Improvising or Innovating is Desired? — Ron Ross on Business Rules

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    […] 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-deci…) […]

  • Haumiller

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    Automating such things is what we as manufacturers want to do more and more and innovate in new ways and automating white-collar decision-making work well is exactly the focus of business rules and decision engineering.

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