That’s the provocative question posed by Tim Harford in his Financial Times article by the same title. His conclusion is that access to enormous volumes of data does not by itself guarantee insight – in fact, if analysis lacks rigor or is flawed, the result will be distortion on a grander scale.
As Mr. Harford aptly states, “New, large, cheap data sets and powerful analytical tools will pay dividends – nobody doubts that.” But “that achievement is built on the clever processing of enormous data sets.”
It is the analysis of the data by analysts, statisticians, and data scientists who are using the right tools and asking the right questions that leads to big insights.
While no single processing or analytics tool can address the issues that have plagued statisticians for centuries – like correlation vs. causality,multiple-comparison problems, sample error and sample bias -- advances in real-time computing, data visualization, and predictive analytics are helping scientists, entrepreneurs, and governments glean invaluable information from data.
Yet it is not enough to stop at robust, disciplined analysis. In a similar vein to Mr. Harford’s critique of poorly executed analysis, organizations must also apply rigor connecting Big Data projects to the business imperative. Accurate insights that cannot be translated into value because they are either not relevant or cannot be applied in the day-to-day operations are costly distractions.
Yet per my previous point, technology by itself is not the silver bullet – and there is no benefit to collecting lots of data just because you can. People who focus on Big Data technology “challenges” miss the point. Big Data needs robust analysis that is relevant to the business; technology is a critical enabler only after you have figured out the first part of the equation.
Successful companies begin by understanding the business imperative and tying rigorous analytics to support it before they get to technology. That’s why Big Data projects need to begin in the business boardroom with support from trained data scientists who are also industry experts that can make the link between the business goals, potential data sources, and information technologies.
I believe that businesses will get continuously better at extracting value from big data. But a disciplined approach is critical. No one wants to be left behind as other companies work through the challenges, refine their approaches, and gain increasingly rich insights.