It seems for a long while doctors in the 19th century did not wash hands between examining different patients or doing an autopsy resulting in perpetual cross infections and high mortality. One such example is by Doctor Ignaz Semmelweis who found out the link after observing a lot of cases but not before a lot of people losing their lives. The mindset is still the same, people need to be presented with a lot of data before they could take an action without applying the knowledge and reasoning.

I have met my neighbour who is a doctor and he sometimes asks my granny for advice to take care of his child. I asked him “You being a doctor, why are you asking my granny for advice, she can’t even read”. I was in school when I had this conversation, I did not understand at that point. His reply was “Not everything can be found out scientifically, people over time will develop the habit of observing cause and effect, so grandmother’s advice works most of the time”.

There are different ways one can take a decision, data is one of them. There are others are like observation (anecdotes and personal experiences), logical (computing using formulas), hypotheses (works in one case, it should work in another similar case too). The problem with a shiny tool is everyone wants to buy that and then try to find how to use the tool instead of having a place for each one of them. The mindset I observe is that people can afford to be wrong as long as the data supports them. What if the data collected and analyzed is not the right one. People who think that they can solve any problem using data, can you predict the next FIFA world cup winner with a great confidence? Can you program a bot to get 100x returns from stock markets in a year if we can access the necessary hardware and tools?

While data crunching just like multiplying numbers is best left with machines, computers will speed up the rate at which humans can make mistakes. What use is of a knowledge that we extract from data when we don’t have an opinion or a theory on what to expect? Human intelligence should have its space as it is equipped with the most complex computer which is hard to understand how it works.

Just by running behind a method is not going to help, same with running behind the toolset and techniques behind data. One should know what they want to achieve instead of just trying to setup an infrastructure then wish for something to emerge out of it. There is also a rampant misclassification of simple analytics, graphing and time series as data solutions. Let us use all the available tools in the arsenal, not just rely on the shiniest one.