Whenever a talented employee leaves a job or assumes a new role, a company risks losing valuable knowledge that he or she has gained while mastering their job. This knowledge often involves rules of thumb or tricks of the trade that have been learned through years of experience. Such knowledge is often referred to as Tribal Knowledge. Tribal knowledge is any unwritten information that is not commonly known by others within a company. People use tribal knowledge to help them make quick, but correct, decisions. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert that if you can improve a company’s decision making you can dramatically improve its bottom line. They explain:
“The best way to understand any company’s operations is to view them as a series of decisions. People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out. We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”
In a corporate setting, “Tribal Knowledge or Know-How is the collective wisdom of the organization. It is the sum of all the knowledge and capabilities of all the people”. If Mankins and Sherer are correct that improved decision making positively impacts the bottom line (and I believe they are correct), then capturing and leveraging tribal knowledge as part of an advanced analytics process should be a priority for business executives. Another article puts it this way:
“If you’ve been at your job for more than a few months, you’ve probably picked up some tricks of the trade. Maybe you know which buttons to hit when the copy machine goes haywire or how to make a word processing task a lot faster. The same is true for production employees who create their own tools, troubleshooting methods, and operational procedures based on their experience. Despite having standard procedures imposed on them, operators and technicians usually develop their own way of doing things. In many ways, tribal knowledge is great for business as it keeps things running smoothly and efficiently. But manufacturers run into trouble when trade secrets and improvements are only passed along verbally or by other informal means, leading to inconsistent performance across teams.”
The challenge, of course, is how to capture and leverage tribal knowledge. A good cognitive computing system can help; particularly one like the Enterra® Enterprise Cognitive System™, which uses both advanced mathematical calculations and semantic reasoning to assist decision-making. Tribal knowledge used in decision making is really a collection of rules and rules can be written into code and leveraged by a computer. Although that sounds pretty straight forward in theory, in practice it is much more complicated. For example, it is common to have conflicting rules where there are multiple options or opinions. There must be a way to adjudicate truth in these situations. The state of the art way to solve this is called Argumentation functionality. It is virtually impossible to have no contradictions in a large rule base. Most times, we do not want exceptions to invalidate the general case (e.g., we do not want the exception that chickens don’t fly to invalidate the general fact that birds fly). This method of reasoning is sometime called “true by default”; however, where needed, facts or relationships can be denoted as invariably true. Enterra’s cognitive computing system uses the world’s largest common sense ontology to perform its semantic reasoning and argumentation functionality. Common sense allows inference chaining to occur where other systems would halt because they are missing a fact that is obvious to a human but must be taught to a machine. For example, when a body moves, all their part (arms and legs) move with them. That means that when tribal knowledge rules are in conflict the best rule will be used. Just as importantly, once tribal knowledge is captured it is retained, even if an employee leaves or assumes a new role.
An article published by Supply Chain @ MIT, offers a good supply example of why capturing tribal knowledge can be valuable. It states:
“There is a vast range of knowledge associated with supply chain planning that can be a valuable resource if organized and stored as a retrievable source of information. The challenge is transferring these facts and figures from employees’ heads to a knowledgebase, so it can be used to improve organizational memory and make supply chains operations more effective. Certain suppliers might be habitually late in fulfilling orders, for example, or customers in some parts of the world routinely overestimate demand by a wide margin. This kind of intelligence helps incumbent planners to do their jobs more efficiently, achieves savings and improves service levels when acted upon.”
Alfredo Zangara, a business architect at Intel, writes, “We’re all familiar with the phrase, ‘Knowledge is power.’ But an often-overlooked source of power is ‘tribal knowledge’ — the collective knowledge of the organization contained within the context and boundaries of the various ‘tribes’ (business units, functions, product teams, and project teams) that make up the organization.” He continues:
“While the instinct to form tribes goes back hundreds of thousands of years, the recognition of challenges associated with the sharing of tribal knowledge across an enterprise is starting to spread. To date, tapping into this knowledge has been difficult and costly. But the desire to make it easier to access and organize this information for broader benefit is inspiring fresh thinking. … The reality is that tribes are here to stay. The good news is a shared desire for transformation toward operational alignment is also here to stay.”
Part of that fresh thinking is using cognitive computing to capture and leverage tribal knowledge. Mick Holly (@) writes, “According to Seth Godin in his book, Tribes: A crowd is a tribe without a leader. … A crowd is a tribe without communication. … A tribe that communicates more quickly with alacrity and emotion is a tribe that thrives.” Holly goes on to note, “The average company is only tapping into one third of its tribe (those who are actively engaged and in pursuit of improvement).” A good cognitive computing system can help ensure that valuable tribal knowledge is captured, persisted, and leveraged.
 Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
 Leonard F Bertain, Ph.D. and George Sibbald, The Tribal Knowledge Paradigm (2012).
 “Recording Tribal Knowledge: Keys to Distributing Information on the Manufacturing Floor,” Documoto, 19 March 2015.
 “A Platform for Corporate Memory,” Supply Chain @ MIT, 22 May 2015.
 Alfredo Zangara, “Unlocking Tribal Knowledge to Transform Your Organization,” Training, 4 December 2013.
 Mick Holly, “The Secret to Harnessing Tribal Knowledge to Accelerate Operational Excellence,” Sigma Breakthrough Technologies, Inc.