This article is Talk 07 of Honest Data Science Talks series. Each talk of around 500 words length, is personal thoughts and deliberations on the domain.
When I say hideous, I speak it from my experience that when someone put forth their vision and mission, or their objectives, or the goals, or anything of that kind, they aren’t honest. Ignoring the ‘conditions apply’ exceptionally few cases, no matter what they state, the agenda is always monetary. Everyone is looking for financial benefits and only that. The societal reforms, be a change, transform, etc. is pure cosmetic crap. And also, as a consequence, the workplaces have a soft corner to the fragments which bring in the finance. Because when they have that, everything else is replaceable.
Society tends to evaluate success, directly or indirectly, based on the earnings, lesser on talent.
The success of a person is measured in terms of their earnings. Profits decide the personality of a person despite the virtues and values. The accomplishment of dreams is measured in terms of revenues. A startup is evaluated based on the income it generates. Finance secures the future. The priority, goals, functioning, etc. everything revolves around money. So how, one becomes a celebrity.
The only difference in the present scenario is that everyone knows it, most if not everyone, and isn’t a hidden agenda anymore. But still, we act as if not. Coming up now to the real issue, why does this relate and have to be discussed with data science? Well, yes, let’s get to that.
We start solving a data science problem by asking a question. We talk about exploratory data analysis, inferences, formal modeling, predictions, associations, cleaning the data, challenges, etc. There are times where the data scientist has an evident vision of the question. Then at other, he stares at the data to get the right question. We strive to get the question right. Because if not that, further analysis does not serve the determination. To the already existing challenge of are we getting the question right, we are also ignoring the unsaid unprejudiced agenda behind it.
If there is already a scientifically proven theory that buying a car does not make one happy, but instead the happiness factor is high when one takes a vacation, why isn’t that part of car sales or vacation study? Why isn’t the upstream and downstream data considered for manufacturing unit analysis? If did, why is not its ecological impact part of the analysis? Is the data linked to pollution data? Does the government consider climate impact and changes before approving the set up of another industry? The list goes on.
How artful is the smart city design? Is it ever an art?
Domain wise there have been sample studies connecting the field to various aspects and parameters. Isn’t the role of data science to combine all this and build a unified model? We started with the example of money being the ultimate objective of everyone, and this is only a representative example. Every such problem-solving task is associated with numerous indirect objectives that do not get quoted. Sometimes it is hidden intentionally; at times, it could be the lack of awareness or else wise.
Well, Data Science has sturdier responsibilities.
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The world has evolved through flows of surplus and surfeit and in this ‘food chain’ there are many light fingers. And now the light finger is also a white-finger. Motives are complex. It is the unsaid truth. It is, however, not clear to me how intelligence can be responsible to figure out these objectives and give out decisions based on the underlying motive rather than what is asked of.
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The like the way it ends. To give what exactly is asked for and only that is a challenge.
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