Leveraging Organizational Knowledge with Decision Modeling

Organizational decisions are not as effective as they could be because organizational knowledge is not directly connected to the decisions that impact business performance. Decision modeling helps deploy existing knowledge and highlights knowledge gaps that need to be filled.

Knowledge is diffused across the organization

How do we know if we are making good decisions? When we are confident that we have used all available knowledge in making that decision.

How can we tell if our knowledge is good or bad? There is no clear measure of knowledge but its value is understood and appreciated. We know that there is a lot of effort and investment in increasing organizational knowledge. Procedure Manuals, Training Programs, Collaboration Sites, Intranets, Portals, Share point and others are all geared towards making organizations better through pooling and managing the communal knowledge.

Decision makers are supposed to become knowledgeable on their own

Traditionally we have sought to impart knowledge to our decision makers through formal training programs, on the job training and mentoring. We have given our decision makers elaborate procedure manuals and handbooks. We have buried our decision makers under mountains of paper reports. We have created black-box systems with a mind of their own. And lately, we have been mystifying our decision makers with analytical models and optimization algorithms that are cool but difficult to apply to day-to-day decisions.

The reality is that we have accumulated organizational knowledge that we are fervently hoping our decision makers will somehow absorb, and then desperately wishing that this absorbed knowledge will somehow get applied to the daily decisions important for success and viability.

Don’t you think this is rather inefficient?

More knowledge leads to better decisions

Managing and applying organizational knowledge is a good idea and makes sense at a gut level. The big assumption is that the organization will be that much better because of ’smart’ people. They will do a better job and everyone will benefit. This is fine at an abstract level but if the process has to be managed and improved, we need to delve deeper into the mechanics of how this occurs.

What if we evaluate the quality of decisions being made as a stand-in for the quality of organizational knowledge?

We can’t describe the knowledge needed if we haven’t described decisions

But wait, no one has formally defined what the decisions are that need to be made well for success. There are a very large number of operational decisions being made every day in every organization, along with quite a few tactical and strategic decisions. All of these decisions just happen and influence organizational performance metrics, but there has been no formal effort at listing or describing these decisions. If decisions cannot be described, they cannot be improved systematically.

Discovering Decisions

The first order of business is then to discover and describe the decisions that matter. In most cases you can find them in the daily processes and also by inspecting daily reports and metrics. Since something is being done (activity) every day to achieve a performance level (metrics), there must be a bunch of decisions being made at every step. Some of these decisions may actually be embedded within our systems, like calculating a discount for a customer’s order. These automated decisions are every bit as important as ‘manual’ or human decisions within the organization. A combination of all of these decisions being made in a collaborative fashion dictates the organization’s destiny.

Now how are these decisions made? If we can describe the decision making with some rigor it would be possible to improve the overall decision by improving individual components.

Decision making components: Information and Knowledge

What are the components of decision making? We need a suitable set of information coming into the decision that needs to be evaluated against all available knowledge at that point in time — then going on to pick the best choice from a finite set of options.

There you have it — making a decision requires information and knowledge.

Managing Information and Knowledge without Decisions is Futile

Getting good, trusted information is a challenge of course. There are many data quality and data management initiatives underway to make sure that we have the best data available for making decisions. And it is a moving target because we have not described the decisions first. Despite all that we do have a reasonable handle on where the data is.

On the knowledge front, the situation is much bleaker. Most organizations have not realized that organizational knowledge is a business asset that can be curated and brought to bear systematically towards making organizations better. The few organizations that do appreciate the strategic nature of knowledge are also making heroic efforts for collecting, tagging and deploying organizational knowledge — similar to the brave souls organizing data for data’s sake. Information Sources and Knowledge Sources in most organizations are either not being governed or being governed too much — with no clear purpose in either case, apart from the basic instinct for reporting ‘everything’.

Linking decisions explicitly to required Knowledge: DMN Models

Decision modeling using the Decision Model Notation (DMN) is a proven, powerful technique not only for a sharp focus on decisions but also for clarifying knowledge requirements. The visual, simple notation for decision modeling is easily understood by all users and gives a degree of structure to how we would describe decisions in a natural language.

The overall structure makes it simpler to identify just the right type, quantity and quality of knowledge required to make specified decisions. All efforts and investments can then be focused on curating the knowledge sources that are already accessible, and on procuring the knowledge that is not yet available.

Decision modeling uncovers and structures organizational knowledge for success.

Read our white paper, Decision Modeling with DMN, to learn more.