by Andrew White | May 28, 2020 | Comments Off on Data-driven doesn’t mean Data only Decisions
A perfect article landed in my lap Saturday morning that highlighted the fallacy so many people make in the belief of being data-driven. Too often I will be on a client inquiry or meeting a conference attendees (during the last economy, not this one!) and their assumptions would be laid out in front of me: Get the best tool, the cleanest data, the best analytic, the right visualization, and the right decision will be taken. This is not what being data-driven means.
In Saturday’s US print edition of the FT there was an article titled, “We need more than big data to track Covid-19” by Gillian Tett, FT writer and once previous speaker at our 2017 UK Data and Analytics Conference. The article tells a story whereby six years ago context tracing was developed to track people close to an Ebola outbreak. The theory, great at the time, was to use mobile phones as a means to track where people went. This would create a big map of context data: who was near who in case one of them was found to be ill. Sounds great, right?
It turns out that culturally in some parts of the world this is not useful. In Sierra Leone it is customary for funeral attendees to touch the body of the deceased person. This movement is of course too granular for mobile contact tracing to spot. But more importantly here and in other parts of the world it is also customary to pass phones around. In many places phones are not linked to a person 1-1 like I might think of them.
The result was a research paper that heralded the apparent failure of big data. This is of course so wrong on many levels. First, this is not really big data, even six years ago. It’s just a lot of well known structured data. Big data was meant to imply data of a size, scale and complexity you didn’t know what it was or meant. Today it is business as usual.
Secondly this was not a failure of the data anyway. It was the failure of the model that had assumptions about how phones were used, made by people. Thus the real failure was the solution designed to answer the problem statement. People failed here, not data.
Being data-driven does not mean to rely on data alone to make a decision. Equally you cannot blame data alone if it, the decision or outcome, goes wrong. Being data-driven means to augment people with new insights and the opportunity to ask new questions of the data around them, when challenges or opportunity confront them. It does not mean replacing people or removing them from the loop. Human in-the-loop decisions making is still required when designing the solutions.
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