The “Science” in Data Analysis

Of late, a lot is being said of data scientists – a bunch of folks who have a unique ability to rip data apart and extract sensible information out of it. Rightly so – almost every time, they come up with insights that transform businesses. They look at social media data and tell you what you must do to make your product launch successful, they look at customer data and tell you personalized actions to take to retain customers or have them spend more, they look at your supply chain data and tell you where you can cut costs without cutting corners. It is really quite awesome. But is it all really science?

data scientist banner

Let me draw out a couple of reasons I ask this question.

Ask 10 people who understand rocket science, the distance between Haley’s comet and earth on Dec 25, 2014. If they do it right, the result will be 10 identical numbers – identical to the 10th decimal. The way they got to that “answer” is no different either. That is science.

Ask 10 people who understand data mining to detect fraudulent transactions amongst many good ones. If they know their stuff, you would get 10 different answers and each approach would be quite different. Each of them can, however, be fully justified. This brings us to the second difference between the 2 cases – that there is no “answer” to a business problem but a “solution”

Business is no science. Every business exists because it does something different – by corollary, no rule can be imposed upon a business to be successful. So how does the term “Data Science” justify exactly what it is?

data science

An artist is a person who can imagine a beautiful painting by just looking at a white canvas – Da Vinci’s symmetry, Dali’s surrealism, Picasso’s cubism – were all imagined before they took the form of a painting. And they all improvised as their creation took form.

Here is where I risk introducing another term into the world of data analytics – data artist. If data were a science, you would be looking for a formula – which truly does not exist – to find a business “answer”, which again does not exist. A data artist, on the other hand, will look for a new method or technique from other walks of life, will imagine how it applies to a business problem and improvises as he goes along to eventually find a solution – which he then calls his Starry Night. The solution is always elegant, simple and powerfully justifiable – you really don’t need to be an expert to appreciate van Gogh. The concepts of Big Data or parallel processing or random forests may be tools to the data scientist, but are just passing trends to the Data Artist – the real deal is in impactful creativity.

At BRIDGEi2i, we believe that a strong knowledge of what you are doing along with a zest for creativity will lead to profitable transformation of businesses. Of course, you have to know the canvas – in this case the business domain, and you have to know your paints – in this case the tools – to be the van Gogh of Data.

At BRIDGEi2i, we "dig" data

BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com

Connect with us: facebook BRIDGEi2i on twitter BRIDGEi2i on LinkedIn BRIDGEi2i on Google+ BRIDGEi2i on YouTube

 

The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.

Related Posts

Comments (4)

Interesting read…

Karthikeyan Damodaran

Beautiful concept! I love it, Arun. Superb writing as well.

Mining of data is actually similar to mining of diamonds where you will have to search and identify data sets that are relevant from the trash.

Well said Karandeep! Thanks for your comment

Leave a comment