Most of us are experts at something. An expert is someone who can reliably assess a situation and apply an appropriate advanced skill or technique. Knowing what skill to apply and when is just as important as the technical capability that is applied. Examples include medical specialists deciding whether to operate and, if so, how.
Knowing what skill to apply requires data. For doctors, this is usually in the form of symptoms, for accountants, it’s the financial results and for engineers it’s the telemetry that is generated by almost all of the infrastructure that now surrounds us.
Understanding how we use data is really important. Knowledge Management experts talk about tacit versus explicit knowledge. The former is often hard to document or clearly communicate. Yet, tacit does not imply that it is not based on data, but it is often using a complex combination of the facts at hand combined with the experience of the practitioner.
Even the best knowledge systems can’t match the interpretation of the data that the tacit knowledge of experts can achieve. Although Big Data analytics solutions are making good progress they can’t make the sort of expert cognitive leaps that we rely on for some of our most critical decisions (see Your insight might save your job). It’s going to be a while before our General Practitioner is replaced by a computer.
But how good are the decisions that experts make? If the interpretation of the results is unambiguous then it is likely that an alert and capable expert will make the right decision. Their choices can be validated by a second-in-charge such as a co-pilot in an airplane cockpit with a consensus almost certain. But these are the sorts of decisions that are most at risk of automation. What about those decisions that are dealing with imperfect data, ambiguous symptoms or the convergence of apparently unrelated issues?
Teaching makes us better experts
When we teach we challenge ourselves. Many years ago, I had my first opportunity to teach students in my own discipline of data management. At that point in my career I was already considered an expert and I was very used to delivering expert advice to clients.
What changed when I had to teach was the need to provide evidence and references. In doing so, I was forced to critically examine my decision making process. While my overall approach didn’t change, I found myself being more formal in the way I referenced my client work and I tried to not only satisfy my clients but also consider what my students would ask.
There is a lot of talk around open talent models. With the likely result that organisations can access the global expert who can answer their specific question. This is happening across the board including disciplines such as management consulting, engineering, accounting, law and even medicine.
For many tasks this makes perfect sense. An expert who can review the data and provide a specific answer, recommendation or diagnosis is incredibly valuable. With social networks, finding such an expert is sometimes only a few clicks away even for the most obscure but specific facts to be reviewed.
I would argue, however, that if the expert is simply providing an answer to a specific question, then ultimately the expert’s role will be automated in the future. Not only do these sorts of experts face redundancy through automation but even when they are using their skills to provide insight they are operating in a vacuum. Their ideas go largely unchallenged and are not developed further.
The value of mentoring
Compare that situation to a practitioner who is working with a younger group who they are mentoring or teaching. The questions they will be asked force them to evaluate their whole approach and, on occasion change their view.
This is the reason why teaching and research go hand-in-hand. It isn’t only the labour capacity that students and junior staff provide, it is also the perspective that they either bring to the table or that they trigger in their supervisors.
In my own field of Management Consulting, this is the most important function of graduates and junior staff. They offer a refreshing perspective. They assume that there are no dumb questions and are eager to learn. In their eagerness, they don’t hesitate to question established orthodox perspectives.
This is the reason I am an optimist about the future for so many of our professions. Despite the threat of automation and the enthusiasm for offshoring to a few experts, the really good decisions are usually made by experts who are surrounded by teams who are eager to learn from them. There will be a role for this staffing model for many years to come.