Importance of Context in Analytics
10 Mar 2015
Context: England crashed out of the Cricket World Cup by losing to (no disrespect intended) a smaller team (Bangladesh) and their coach started talking about looking at data which, as expected, hasn't gone down well.
I found a few key quotes in this article that illustrate the need to balance measurements with sensibility:
- "England have, under Moores, known the price of everything but the value of nothing."
- "But the data helped imprison them. ...The data, doubtless, meant the batting order could never be adjusted to take advantage of or respond to a changed set of circumstances. The data said this and that and turned intelligent cricketers - for such we assume they must be – into morons."
"...the data doesn’t mean nearly as much as you think it does there comes a point when your theory has to be reconsidered."
and finally
"It’s tough to measure this sort of thing, however, so instead we assume that what’s important is what can be measured. That isn’t the case. It leads to a bad place."
These quotes succintly contextualize several facets of data analysis:
- the push-pull relationship between analysis and action
- asking the right questions
- measuring the right things
- utilizing domain expertise
- understanding stakeholders and their latent needs
- communicating and acting on insights
- knowing when, where and how to rely on data
Amidst the cacophony of complex statistics, models and a variety of tools, it is important to remember that ALL organizations are results-driven.
It is already hard enough to translate complex technical insights to a broader audience that is juggling several priorities. Despite this, a data analyst is responsible for utilizing the available technical resources and organizational strategies in delivering the results that are demanded by businesses/organizations.
To that end, the context of analytics usage is paramount.