24 September 2014


Having shared the concepts in Decision Model & Notation (DMN) from OMG and how it supports business rules task in BPMN Process Model, let look at the potentials between another business rules related specification, Semantics Of Business Vocabulary and Rules (SBVR), also from OMG.

23 September 2014

Introduction to Decision Model & Notation (DMN)

Early this year, OMG released the beta version of its Decision Model & Notation (DMN), a complimentary notation to work alongside BPMN for modelling of operational decision and business rules. Similar to BPMN, it aims to support designing of decision models, providing guided operational decision and potentially automation of operational decision. This article provides an introduction to the basic concepts of DMN, its constructs, illustration of decision model and decision logic expression particularly the DMN Decision Table.

11 September 2014

Knowledge Worker - Data Driven or Intuition?

“Of course I’m data driven. I take the data and I use that as an input to my final decision-making process.” A very common statement to many managers and so called knowledge workers. Is combining data and intuition a good idea in decision making? What would be the impact of taking operational decision and/or business rules management to the next stage with "thinking machine"?

The McKinsey Quarterly article, "Artificial intelligence meets the C-suite" explores the impact that “thinking” machines may have on top-management roles with experts including the authors of "The Second Machine Age", Erik Brynjolfsson and Andrew McAfee. Enjoy the read.

With predictive analytic and the massive data, a true data-driven decision making would improve the decision making outcome. And business rules management and operational decision management are the low hanging fruits taking advantage of the advancement in AI fields.

On the other hand, having a badly developed business rules and decision models could escalate the problem in multi folds. The automated scheduling case of Starbucks mentioned in Wharton Management article - Can a Robot Be Your Boss? - is a good example or poor decision model. The (data-driven) algorithm probably only considered the demand side and not taking into account of the supply side rules. In fact it is also a case that shows the importance of operational decision management and athe potential consequence for lack of it.

Well, our knowledge workers could finally working on the creative and innovative side of works and also focusing on the human side providing motivation to staff members, rather than trying to combine intuition with data driven in decision making. "It still matters a great deal to have a good boss, and it is still one of the worst things in the workplace to have a bad one.” 

09 September 2014

Knowledge Worker versus Information Worker

"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," Ronald Ross, 2014.

There has been a long discussion and debates about who is a knowledge worker and what type of job constitute a knowledge worker. 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 Peter 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), knowledge worker was described as 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 office workers - what Ross described as the white-collar workers.

The advancement in knowledge representation in the field of artificial intelligence and knowledge management has enabled many knowledge to be represented in a well formed structure, such as decision models and business rules. 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” (white-collar) to that of the “knowledge worker” (gold-collar). In a LinkedIn discussion, it is generally agreed that a knowledge 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 Morris's remark about the unionism movement is a summary of the economics dimension on the 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-skilling only happened in pursuit of profits. Regardless, all these debates are moot now with the anticipated massive wave of automation," John Morris, 2014.

In short the transfer of implicit knowledge to explicit knowledge is definitely following the similar trend in office works.