Data Science

“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 priorities, 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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Notes from the author:

For more details on blue and green tracks, which are part of BADIR – the 5-step process from “data to decisions,” feel free to download this white paper on BADIR. And if we can help your organization in the journey towards being data-driven, with green track married to blue track, feel free to contact us.

If you are an business intelligence (BI) executive frustrated with low ROI from your data investment, in spite of a large data science and BI  team, then I invite you to join us for a half-day Data-Driven Executive Workshop on April 5th, 2013, in Santa Clara, CA. This workshop will guide you on what is analytics (and what is not analytics), how organizations such as yours leverage data as an asset, how to measure your organization’s analytics maturity and then how to transition your organization towards higher analytics maturity, such that all the decision makers in the organization, irrespective of where they sit, have the right tools to make smarter, data-driven decisions.

If you are a BI manager and want to deliver more than just data to your stakeholders and want to learn the green as well as blue track, then I invite you to attend our Data-to-Decisions Week  alongside product and marketing managers, where you can learn how to drive decisions using insights from analytics and testing.

Data Science
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