Knowledge at Risk: The Hidden Dangers of Tacit Knowledge

Unlocking the Power of Tacit Knowledge

In every industry, staff develop skills, knowledge, and insights that exist in their minds, but often go unrecorded. This phenomenon is commonly referred to as "tacit knowledge."

While tacit knowledge is invaluable for solving problems and making informed decisions, it comes with a vulnerability: it's stored in people's minds rather than explicitly recorded and maintained for easy search, transfer, and re-application.

Consider your business:

Do your analysts in engineering, science, and business often solve problems that have already been addressed? Or do they fail to utilize and repurpose valuable insights gained by their colleagues? Are you vulnerable to delays and disruptions when experienced staff leave or are unavailable?

If you answered yes to any of these questions, your organization is likely experiencing knowledge loss — a vanishing act equally difficult to solve and costly.

The Real Costs of Mismanaged Tacit Knowledge

In 2020 and 2021, we conducted extensive research, surveying over 100 engineering and science-based companies, to understand how frequently staff fail to leverage tacit knowledge. The results were eye-opening.

Astonishingly, around half of them reported that 40% to 60% of the time, valuable insights and solutions were not being reused effectively. Why? Because they can't find it to begin with.

As a result, highly paid staff expended valuable time and effort replicating knowledge that was already known, needlessly delaying objectives. This is waste, and it's often due to the mismanagement of tacit knowledge.

Even more concerning was the fact that not a single company had hard data to support their estimates – they were all based on conjecture.

That means that tacit knowledge is not being managed like the valuable asset it is.

Harnessing Tacit Knowledge for Competitive Advantage

Generating knowledge is expensive. Why lose it? Almost any initiative can benefit from prior knowledge, if it can be found. Tacit knowledge holds the key.

Knowledge and information are often used interchangeably, but they are not the same. Information is data or facts, while knowledge allows for prediction.

Simply knowing yesterday’s production and yield numbers alone do not predict output for the next day – that’s the difference between knowledge and information. Knowledge predicts.

Traditional data storage systems like data lakes and data warehouses are excellent for housing information, but they fall short in managing tacit knowledge effectively. Electronic Lab Notebooks (ELNs) can be rigid and disconnected from actual work processes, making them inadequate for capturing and transferring full sets of tacit knowledge.

In order to gain competitive advantages — say, faster times to market or higher manufacturing yield — prior knowledge must be easily accessible. Searchable in a moment's notice.

Ensuring Accessible and Useful Tacit Knowledge

Managing tacit knowledge requires maintaining the interconnections between data, the analysis of that data, and the insights generated. We call this the bi-directional knowledge generating pipeline. At the end of the pipeline, these insights take the form of models, recommendations, standard operating procedures (SOPs), and more, all of which predict and rely on tacit knowledge.

The Knowledge pipeline provides traceability, allowing analysts to identify and review the analytical methods and data used in generating insights.

If a model comes into question, it’s easy to locate and reconsider not only the analysis that produced the model but also the exact data points that were fed into that analysis. When, as is often the case, the interconnections have not been maintained, analysts must begin anew, capturing new data and analyzing it to regenerate the insights.

Recording and Protecting Your Tacit Knowledge

The final crucial part of managing tacit knowledge is to capture the narratives and discussions among analysts and integrate them closely with the data and work conducted in the knowledge pipeline.

Analysts can record their thinking with respect to the modeling methodology they’ve employed. They can note observations that they have excluded from their analysis and why. When other staff, perhaps years later, have the need to look back, they have important context of the work that was done.

When a model fails to make accurate predictions, analysts should record their concerns and link them to the model. This allows others to engage with these concerns, add their own thoughts, and so forth. Eventually, the model might undergo revisions, and those who raised concerns get a chance to assess the new version.

Such collaboration happens asynchronously. Notably, participants in this discussion might include analysts who have left the company. Without a structured approach to capturing these insights, and a framework to attach them to, the valuable experiences and concerns, generated at a high cost to the company, would dissolve from explicit to tacit states and, eventually, be lost.

Senior management must take immediate action to establish programs that safeguard their investment in tacit knowledge. The annual expenses allocated to data collection, storage, analyst compensation, and associated costs constitute a significant portion of revenue. Don’t allow the results of this expenditure to become hard to locate or vanish when individuals move on. Tacit knowledge is an investment that should accumulate and be readily found and repurposed at every opportunity.