Data

Responding to Cooperative Catalyst, Metrics and "Success"

I think data is important (it's the only evidence we have!) but I think that people take a very narrow view of data, which is unfortunate.

- they think, for example, that data is just numbers, when in fact data can be found in the full range of perceptions, including observations of emotions, visceral reactions, likes and dislikes, and more

- they think the only way to work with data is to count things, while in fact data provide a rich range of possible interpretations - connections, patterns, flows, etc

- they think data is cumulative, suitable only for iterations, when (as Kuhn pointed out) the right sort of data shows a greater and greater need for quantuum leaps of scientific revolutions - data about anomalies, data that needs explaining, problems, unanswered questions, etc

- they think data should show you a single 'objective' perspective, when in fact different sets of data yield different perspectives, where these perspectives taken individually and together amount to more than the mass of data aggregated

The problem is not with the use of data to make decisions - the problem is with the simplistic one-dimensional use of data to make decisions. Instead of attacking the data - which leaves you with no ground to stand upon - it makes more sense to attack the simple-mindedness.

Change the grounds! It's not that their approach is 'data-driven' or 'evidence-based' and yours is not, it's that they have very carefully selected a subset of the evidence that will 'count', while you are using a much broader, richer, and ultimately more accurate base of evidence.

(p.s. on the term 'data' - sometimes I use it as a mass noun, and say things like 'data is important', and sometimes I use it as a plural, and say things like 'data provide'; there isn't a single 'correct' way to use the term; its conjugation travels as your usage travels).