These days, data and data scientists (and data engineers?) seem to rule the world. Companies are data-driven, problems are solved using data-driven methods and national intelligence agencies (arguably: also online retailers) extensively collect all the data they can get hold of.
The data-driven approach is formalised in the Jurney-Warden Data-Value Stack:
The data-value stack is to be read from the bottom up to the top. The idea of the stack suggests: the value of the data arises from raw data through various steps up the pyramid. The link to Maslow’s hierarchy of needs that the authors make implies that the upper levels of the pyramid build and rely upon the lower levels, i.e. you cannot effect actions without first collecting data at the records level, then cleaning and aggregating, exploring and inferring. In my opinion, this is a feasible approach and obviously the framework works well for some cases.
However: looking at the stack, the approach reminds me of a blind chicken which randomly picks and picks until it eventually finds a valuable corn to eat. More intelligent animals have some expertise to enhance the „random-pick“ – i.e., purely bottom-up – approach: Based on its experience, intelligence and/or guts, the intelligent chicken efficiently picks the most valuable food right from the start.
I admit, I know nothing about behavioural biology to support the claims in the previous paragraph. And yes, millions of blind chickens may help. But what I really want to say is: expertise matters, also in the data-driven world – we cannot yet proclaim the end of theory.
But how does expertise come into play in the above mentioned data-value stack? Its design principle is that higher levels depend on lower levels. I would propose a similarly shaped expertise-value stack, which aligns alongside the data-value stack. That stack would look as follows (on the left):
The expertise-value stack complements the steps in the data-value stack with the following levels of expertise:
- Wisdom: Use your wisdom for strategic decisions.
- Application of Interdisciplinary Knowledge: Use and combine your knowledge from different subject matter domains.
- Application of Domain Knowledge: Apply your subject matter knowledge to the problem.
- Information Collection: Conduct targeted collection and filtering of relevant information, like reports, opinions or results of relevant research.
- Problem Comprehension: Before doing anything, make sure you understand the problem at hand from one or several perspectives: e.g. from the perspective of the user, provider or politician.
Obviously, the idea of domain experts collaborating with, and supporting, data scientists is not new. Indeed it has been noted that subject experts may make the difference. And this is why an interdisciplinary approach (edit 2016-02-23: i.e. leveraging both expertise-value and data-value) has advantages over a pure data driven approach. Unfortunately, the benefit of including subject experts does not come for free: It takes much time to talk to each other and you need to find good counterparts to succeed. But in the long run, this interaction will pay off.
If you are interested talking to Swiss data and information experts with an interdisciplinary approach, come and talk to the team at EBP. Contact me for details. (And thanks to Ralph for editing this post)