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Data Science vs. Analytics?

by Dashboard Insight, Dashboard InsightWednesday, September 4, 2013

Piyanka Jain, CEO of Aryng, writes on the difference between data science and data analytics

I came across the term “data scientist” a few years ago when somebody (from the valley, of course) asked me, “So are you a data scientist?" And my immediate answer was, “No, I am not a scientist." Although I already had spent a decade in the data space, driving business impact through analytics, I did not see myself as a scientist.

My answer today is not all that different. To me, scientist conjures up an image of fully antiseptic lab environment, white lab coats and pipettes. Marry data to that term, and it still sounds very white lab coat-ish, with a definite R&D bent and with graphs running on a big screen monitor. A few other data science leaders in the Silicon Valley, like Daniel from LinkedIn, have similar interpretations of the term data scientist.

But words are just words. What is the big deal? Actually, there is a big deal in the middle of all this. I frequently keynote at analytics conferences, and one of the things I hear a lot from the data scientist/analytics professionals is that many of them are producing a lot of analytics insights using state-of-the art-algorithms, BUT nobody in the organization really cares! This I have heard from data scientists, spanning the breadth of apparently “data-driven” Fortune 1000 companies including LinkedIn, Facebook, Visa, eBay, Apple, Oracle, and SAP, to name a few.

So what is going on? On one hand, we see reports about the massive dearth of data scientist (Source: McKinsey’s Big Data report). On the other hand, the work they are doing is hardly being leveraged. Why?

The reason is what I call “the MISSING green track.”

Let me explain. Although the word “analytics” conjures up the image of graphs, data, numbers and complex algorithms, it is only part of the story. At Aryng, we use a structured approach to analytics (see Figure 1) that includes a green track and a blue track. When analytics is done right, the blue track, the process of getting insights from the data, needs to happen in parallel with the green track, the process that drives decision making and impact in the organization. The green track is all about what one needs to do to bridge the gap to the business, to understand the business priories, to work within business constraints, to bring along the key stakeholders and to make the right handshakes at the right time so when one is ready with insights from the data, the stakeholders are ready and poised to make decisions and take actions based on those insights, thus driving impact through data.



Figure 1: Aryng's BADIR Methodology

Today, data scientists are well trained, or perhaps over trained, on the blue track; but the green track often eludes them, mostly because it is not taught as a science in the universities. Nevertheless, green track is a science and is completely learnable (check out Aryng’s Data-to-Decisions Week – a week for complete hands-on education on analytics and testing – with green and blue tracks).

Unless an insight sees the light of the day by way of getting transformed into a decision, it is a complete waste of resources and time. Unless analytics drives business impact, it is not analytics. It is just statistics; it is just data science. That brings me back to the term data scientist, which sounds academic and all too blue track to me. To me, data science + decision science = analytics.

But again, words are just words. As long as both green track and blue track processes are followed, data will lend itself to decisions – call it data science or call it analytics.

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Source: BeyeNETWORK

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