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When it comes to problem solving, typically in organization’s context, the most encountered phenomenon is something called Einstellung effect.

Einstellung is the development of a mechanized state of mind. Einstellung refers to a person’s predisposition to solve a given problem in a specific manner even though better or more appropriate methods of solving the problem exist.

This effect is also echoes in the famous quote by Waren Buffett in 1984, he says that, “to a man with a hammer, everything looks like a nail“. The context behind this quote was the relevance and approach of academic finance towards solving problems, he argues that, “It isn’t necessarily because such studies have any utility; it’s simply that the data are there and academicians have worked hard to learn the mathematical skills needed to manipulate them. Once these skills are acquired, it seems sinful not to use them, even if the usage has no utility or negative utility…”.

Data analytics and related tools and techniques must always be thought of as tools which are there to assist, simplify & hasten decision making. This is achieved only by bringing out the sense from data by way of summarization, inference or visualization. To do any of these one must critically examine various choices available and the selection must be intuitively obvious. One very effective way to judge is to imagine what a person would interpret and understand from the summary/inference or visualization, if there is no one to explain. If the answer is not obvious then perhaps, the analysis is not done in true spirit.

Perhaps the major difference between statisticians and data analysts is this very fact that typically statistical analysis done by statisticians is understood only by statisticians.

Simply put, when it comes to problem solving, it is far more important to figure out the correct and most optimal tool or method. A simple correlation exercise can be done even on excel, while every data science book or course will suggest R is the most suitable platform.


I came across an interesting quote by Duke University Professor Dan Ariely, he says, “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it …

  1. Entrepreneurial Mindset: One of the most understated and less talked about aspect of analytics is the business aptitude. Analytics most likely will not add value if it is not originating and planned with an entrepreneurial mindset. A data scientist or a data engineer needs to understand the business model, its competitive positioning, the reason why the company exists, what gives the competitive edge and how this edge can be sharpened.
  2. Status Quo: It is important to get a complete picture of the technology ecosystem of organization
  3. Gap Analysis: Estimate how far the technology ecosystem is from your version of an ideal state and work out strategies to function
  4. Cultural Appetite: Understand the organizations cultural appetite towards data orientations, in terms of the balance between the two extremes of quantitative and qualitative
  5. Data Strategy: Understand whether the organization has a data strategy in place and if not what data mean to the organization and what are the data goals, if any.