Should data analytics be used as a tool to uncover new insights from company data, or should it be used to answer or solve a specific business inquiry or problem? This question is central to any analytics project design. But like most important questions, the answer depends on several factors and is not altogether clear.
As terms like “big data”, “data science” and “artificial intelligence” continue to bleed into many aspects of our lives, a lot of companies are excited and more than willing to jump on to the data analytics highway, often without clear direction or purpose. The notion is that even without clear direction, a seasoned data scientist can use sophisticated analytical tools to uncover powerful business insights from web-scale data and internal caches, and in turn, drive company success. While such an outcome is possible, it is not likely.
Many successful analytics endeavors begin with a specific business question or problem in hand. Quantitative tools are then used to efficiently and accurately answer the question or solve the problem. A more precise understanding of the business problem immediately informs the analytics team about what data are needed to arrive at a solution. This small bit of clarity not only helps direct data collection, data maintenance, and data generating initiatives, it also ensures that your company has the data required to quantitatively address some of the most important and pertinent business needs for the foreseeable future.
But for companies looking to use data as a way to ask new questions or to discover unexamined business problems, exploratory data analysis may be valuable. While perhaps risky, analytics projects and initiatives designed to generate more questions than they answer can lead to unexpected knowledge, and valuable business insight. For example, if a company is unaware of costly staffing inefficiencies, exploratory data analysis is one of the few ways to unexpectedly illuminate the issue, and at the same time provide a solution. If problems are never identified, then they can never be resolved.
So, should analytics be used to solve a specific problem, or should it be used to uncover new insights? Of course, the answer is that both avenues can be valuable, and they serve different purposes and pose different risks and rewards. We can think of the dichotomy in the context of a highway, let’s call it our data highway. Imagine getting in your car and driving along the highway without a clear destination or purpose. Along the way, you might see some new and interesting things. A new restaurant, a new park maybe. You might also see nothing at all of interest, and your time might have been wasted. What’s worse is that you’ve already paid for the gas. If you had started your journey on the data highway with clear destination, you might eventually get there. If there are road blocks, at least you’ve identified them and can, as a consequence, chart alternative routes.