Affine Transformations 101: The Analytics Scientist Spiderweb

Analytics is the buzzword these days. Businesses are increasingly realizing the need to use analytics, or for some, even the need to be seen using analytics. A recent article in the Harvard Business Review identifies Data Scientists or Analyticians to be having the Sexiest Job in the 21st century.

But what does it take to become a good analytician? How is their DNA different from the rest? Do they eat differently / follow a separate exercise regimen?

At Affine Analytics, we believe we have identified the secret ingredients to creating successful analyticians[1].


Business Knowledge – As George Clooney rightly said in “Up in the Air” –“Before you try to revolutionize my business, I’d like to know that you know my business”, one should not approach a problem without having a proper knowledge of the business. It is utmost important to appreciate the “why and how of a business problem” and one should do a proper ground work before embarking on the approach. Every business is unique in its own way and needs to be understood thoroughly before attempting to solve the problem. Do not try to blindly fit the hypotheses learnt from one problem to the other. The more you know, the easier it is!


Problem Engineering – Don’t engineer a problem, but engineer a solution to an existing business problem.

There exists a misconception that analytical solutions necessarily require applying new and advanced statistical procedures. Not always true: instead what is always needed is the ability to take a business problem in its rawest format, break it down into logical pieces and then view /solve each of the pieces in a systematic manner.

Common sense coupled with an understanding of the business context is the first & foremost requirement to get started on the road to become an impactful analytics scientist.

The next logical step in solution engineering is to find creative solution approaches. To do that, you need to keep in mind the end goal of what is required to be achieved. Don’t have the door-to-door salesman approach selling your standard product. Find what the end goal is and create a solution that achieves it. Sound knowledge of statistical techniques is needed here. Superficial knowledge can help you in coffee table discussions, but doesn’t work here. A deep understanding of the pros / cons of each method can get you to the optimal solution approach.

Innovation is also a key driver here.  It is required not just at the apex of the hierarchical pyramid, but at the lowest level. Innovation can range from creating an automated business suite for a retailer to a completely different way of creating a variable or a metric.


Curiosity & Skepticism – Necessity is the mother of invention, said Pluto, but it is more curiosity than necessity. Of late, new things are born just because people are inquisitive. Whenever a person is faced with a challenge (Something one hasn’t seen or solved before), the curious devil in him wakes up and doesn’t sleep until he’s become an expert on that topic. Curiosity makes one productive and work becomes fun.

Managers & leaders, take note: curiosity can create the passion or more commonly used (/abused) “fire in the belly”.

Analyze the approach from various different angles to increase confidence in your results. The more you look into the problem, the more you will get out of it. Be critical of your own findings, and use multiple approaches and techniques to verify unintuitive results.


Math – Mathematics forms the basis of the analytics industry and every data scientist is expected to have a good grip on the subject. Mathematical skills especially appreciating numbers in general and variable trends are more important in the field of analytics than knowing machine learning techniques. Master the basics of all the techniques and you will rule the world. The basic understanding of the principles governing numbers changes the way one looks at a variable.


Technology – If math forms the foundation of analytics, then technology enables us to construct a proper structure out of it. One can’t give meaning to the math behind, unless he knows the right tools. Great analysts and even managers cannot work without these tools. Knowledge of MS-Excel and R is must in this field. Though technology is evolving rapidly, one can deliver high quality analytics with proper knowledge of these two tools.


Writing ­- Your story, however great it may be, is worthless, until you are not able to sell it. Presentation forms the key to create an “impact” of an analysis. If people cannot make out any meaning out of your presentation, then your analysis holds no value. If the client cannot understand, forget about them implementing it. Create decks/reports that make proper sense. Simple decks / reports with systematic storyboards are what create the impact. If someone stumbles upon your deck in the future, and is able to understand it without much background –then you will have carved a good analytics scientist out of you!

[1] Affine’s leadership contains a concoction of a few decades of analytics experience in analyzing diverse problems across multiple business functions & industries

Vineet Kumar & Krishna Agarwal


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