Einstellung effect — the hammer and the nail
When it comes to problem solving — typically in an organisation's context — the most-encountered phenomenon is something called the Einstellung effect.
Einstellung is the development of a mechanised state of mind: a person's predisposition to solve a given problem in a specific way even though better or more appropriate methods exist.
It echoes Warren Buffett's 1984 line that "to a man with a hammer, everything looks like a nail." The context was academic finance, and his argument was direct: "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."
Data analytics — and the tools and techniques that sit on top of it — must always be thought of as instruments that assist, simplify, and hasten decision-making. That happens by bringing sense from data through summarisation, inference, or visualisation. To do any of these well, you have to critically examine the choices available; the selection should be intuitively obvious. A useful test: imagine what a person would interpret from your summary, inference, or chart if there were no one to explain it. If the answer isn't obvious, the analysis isn't done in true spirit.
Perhaps the major difference between statisticians and data analysts is exactly this: statistical analysis done by statisticians is typically understood only by statisticians.
When it comes to problem solving, it is far more important to figure out the correct and most optimal tool — not the most fashionable one. A simple correlation exercise can be done in Excel; every data-science book or course will still suggest R is the only suitable platform.
Five dimensions of a systems-thinking approach to data
Dan Ariely, the Duke professor, put it well: "Big data is like teenage sex: everyone talks about it, nobody really knows how to do it."
- Entrepreneurial mindset. One of the most understated and least-talked-about aspects of analytics is business aptitude. Analytics will most likely not add value if it is not originating and planned with an entrepreneurial mindset. A data scientist or data engineer needs to understand the business model, its competitive positioning, the reason the company exists, what gives it the competitive edge, and how that edge can be sharpened.
- Status quo. Get a complete picture of the technology ecosystem of the organisation.
- Gap analysis. Estimate how far the technology ecosystem is from your version of an ideal state — and work out the strategy to function in the meantime.
- Cultural appetite. Understand the organisation's cultural appetite for data, especially the balance between the two extremes of quantitative and qualitative reasoning.
- Data strategy. Does the organisation have one? If not, what does data mean to the organisation, and what are the data goals — if any?