Performing a study is a bread-and-butter activity for most consulting organizations. This might come in the form of assessing a company’s technology systems and architecture, or an analysis of a particular market or product category. These studies are usually relatively quick and low cost and result in a dramatic final presentation, complete with dozens of well-crafted PowerPoint slides with accompanying voiceover from sharply-dressed consultants, that often tell the audience what it already knew.
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All of this is done in the name of “due diligence,” “just doing our homework,” or making sure “we have enough data.” However, a funny thing happened amid the COVID-19 pandemic: All the companies I work with stopped doing studies and went directly to action. Billions have likely been spent on studying remote work with little to show, yet over the course of a few weeks, organizations of all shapes and sizes were able to make wholesale, largely successful transformations of their workforces.
Similarly, economic models that predicted widespread doom and gloom were abandoned in many industries seemingly overnight, where rather than declining demand, stock outages and record sales became the norm.
Leaders experienced fear in managing through an unknown and uncertain time. Still, in many cases, that fear was combined with a sense of liberation that they were free to try new things, launch new projects and take more calculated risks. Instead of studies, I’m getting more calls about extending and expanding an innovative program or concept that was conceptualized in this environment. These concepts were borne of unconstrained thinking and a focus on the future, rather than reams of data about the past.
The devil is in the data
In the technology world, we’ve long been tasked with gathering and delivering data. The purported benefit of all this data is intuitively obvious: the more data you have, the better your decisions, and consultants and management scholars have been admonishing companies to become “data-driven organizations.” However, in the first period in the modern IT era where long-term historical data were rendered largely irrelevant, many organizations were at their most innovative.
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Data are undoubtedly beneficial in many situations, but they can become a crutch for poor decision-making, or lead to a poor outcome. For example, if your home is on fire, and the fire department demanded temperature data for each room, a chart showing rate-of-rise calculations and a study indicating the market impacts of similarly sized house fires in other geographies in order to perform due diligence before responding to your call, they would not be a particularly effective organization. While all these data might be wonderful and ultimately helpful, firefighters leap into action with relatively little data but significant training, teamwork and tools.
Data also fail to account for organizational missions and long-term business implications. For example, consider most modern media organizations, arguably some of the best real-time data and analytics users. Their data indicate that online media consumers want politicized, partisan content and are most likely to click on headlines that imply scandal and outrage rather than an accurate (if unexciting) summary. While producing this content has been great for generating clicks and ad revenue in the near-term, it’s also created a crisis of confidence in the reliability and impartiality of our news organizations that will take years to repair.
Sometimes, you have to take the leap
As the world lurches toward its post-pandemic future in fits and starts, our natural human instinct may be to return to the relative comfort of ordering up another study or calling up that hotshot new data scientist to perform some complex analytical gyrations on historical data, ignoring the fact that those data may not even be relevant to a rapidly and unpredictably changing world. Similarly, our data may guide us to an answer that optimizes for today but fails to consider the long-term impact of a decision or program.
We wear many hats as tech leaders, but in order to be influential and effective general leaders, we need to help guide and shape discussions of how data are best used rather than blindly taking orders on how much to acquire. It may require a challenging conversation or two when you provide some gentle pushback before launching the next study or embark on that technically interesting data analysis software project, but your company and your leadership chops will benefit in the long run.