Data Science
Data Science is the mix of computer science, user experience, and statistics.
The aim of data science should be:
- to make things better
- by influencing people and things to make better decisions,
- by making people and things more aware of better alternatives,
- based on better algorithms and more relevant data.
Language kept intentionally vague to set up the ‘well that could be anything’ argument when it suits me later.
If you do it right, nobody is really aware of the complexity of what just happened to them. The point is not to experience data. The point is to experience…an experience. And be better off for it!
And, the most interesting part is that it’s not really driven by humans with hidden agendas. Though, that could play a part. It’s driven by machines which generate rules that most designers don’t understand fully.
Haven’t heard of Data Science? You’re not alone. It’s only just become a ‘thing’ lately.
The usual fight for the soul of Data Science (the language, identity, ego) has begun in earnest. You can read the editorial summary here. This will go on for the better part of a decade, and frankly, nobody outside of the emerging data science community is really going to care. But it’ll be important to a few. And they’ll make it a big deal, solely because language contains bias about beliefs, and don’t question my damned beliefs, dammit.
I don’t have much of a dog in that fight. I’d much rather get to the good stuff.
Why am I excited and optimistic about the prospects for Data Science?
Never before has so much data about so much meaning so little to so many. The world is filled with waste and genuinely bad things. What if you could make sense of more of it? What would you do then? How much better off would we be?
This is a bit beyond the novelty of Freakanomics.
You may recall a line of reasoning that James Burke once put forward in his series Connections. He argued that we tend to believe that technological advancement causes the world to become better, when, in all reality, every technological advancement has made the environment worse off while making people relatively better off. There’s been a tradeoff. It seems that technological advancement is at odds with sustainability.
But does it have to be?
By becoming more aware of cause and effect as individuals, groups, communities, companies, organizations and societies – can we become better?
It is, after all, not just about tracking the world. It’s about making sense of all that data too. Thinkers like Jeff Jonas have been putting forward ideas about sensemaking for some time, and I take no credit for it. It’s not so much that the data excites me. It’s the opportunity that that data opens up.
I think there’s good reason to believe things can be better.
Picture related. Without meaning, how can you make sense of anything?
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I’m Christopher Berry.
I bridge the gap between marketing science and data science.