Despite it being difficult to gauge which choices will impact our lives in significant ways, we don’t treat them all with equal levels of importance.
At the same time, it’s not always easy to tell whether we made the right decision about something. After all, we don’t have data about our personal lives to analyze.
Data is abundant in the business world, yet many leaders still make decisions based on their expertise instead of the numbers.
Let’s examine what the decision-making process looks like with and without data analytics.
Even though business intelligence software has existed for decades, it was mostly large companies leveraging data. For the rest of companies, decision-makers relied on emotion to inform their next move. In other words, decisions were made with gut instinct, anecdotes heard throughout their career, and personal bias. Little empirical evidence, if any, influenced their decision.
The decisions powering today’s data-agnostic enterprises are dependent on the experiences of the employees making them. When there’s not an objective way to evaluate results, nobody learns from their decisions. Whether the outcome is positive or negative, the process is void of any specific lessons.
Failing to incorporate data into the decision process also welcomes conflicting opinions, and potentially initiatives, from different departments. And without a knowledge discovery system, employee development can stagnate. How can they get better at their job without access to facts? The lack of resources won’t only flatline employee performance, insufficient resources could cause widespread disengagement, either resulting in nose-diving productivity or high turnover.
There’s also the question of whether the typical employee should ever be making decisions on their own without any evidence-based support. It’s one thing for C-suite team members to steer a company based on their learned business acumen, but countless more decisions surface in an organization during the average workday. Without data, it’s not just high-level leaders using guesswork to decide the business’ fate, it’s every individual member of a company.
Decision-Making in the Age of Big Data
The data generated every day through our growing number of devices certainly adds layers of complexity to analytics initiatives. Currently, there are three sections companies fall into: traditional (no analytics), analytics through specialist (what the majority of organizations leverage today), and self-service analytics (which empowers business users to drill into their own data, with oversight from a central team).
According to Gartner, between 60–85 percent of analytics implementations fail. Analytics tools aren’t to blame, however. Embedded data analytics technologies, such as ThoughtSpot, are providing companies with quick insights at scale through a simple search bar. Various branches of AI make this happen, such as machine learning, pattern recognition, and natural language processing. These features fall into the third bucket we mentioned above.
In that same vein, using tools that eliminate the need for data scientists to build manual reports makes moment-to-moment business decisions more accurate. It also provides employees with more autonomy to think critically and drill down into data every time they’re curious about something. Companies have already built coalitions of these data interpreters, known as “citizen data scientists” and are reaping the benefits over their competition accordingly.
This is where tools complement a data-driven culture, giving employees quick access to data to make decisions or evaluate past results from an initiative. Employees don’t need to be prodded to use data after they see how it aids their workflows. Once a data-fluent culture is established, knowledge discovery, insight sharing, and performance tracking become cyclical. As a result, decision making, and the grading of those decisions are based on evidence instead of suspicion.
Making decisions will always be an imperfect act. But when it comes to using analytics to make them in business environments, there’s no comparison for which is more effective. We’ll never have it exactly right with any business decision we make, but we won’t know how wrong we were unless we have the data that proves it.