Business Incubators Insights from North America

At this time, the value of data in decision making is obvious. However, while understanding the mechanics of data-driven business decisions and investigating real-world examples will point you in the correct direction, knowing what to avoid will help you secure your success. How many times in your life have you prepared for a meeting, had all of the facts and data ready, just to have the decision reversed?It probably felt like the choice was determined before the meeting even started. If this sounds familiar, you're not alone. We aren't talking about a startup full of newcomers who believe that going with their gut is more important than KPIs; we're talking about large corporations. Rob Enderle, a former IBM employee and Forrester Research Fellow, authored an excellent piece documenting the flaws of IBM and Microsoft management.While the article has numerous examples, probably the most severe is IBM's partial selling of its ROLM branch to Siemens. Enderle and his team created an internal analysis demonstrating that selling to Siemens would be a disastrous disaster. It turns out that the choice had been made before the research was published. In fact, executives had forgotten that the research had been commissioned at all. 

Their gut decision cost the corporation more than $1 billion

According to BI-Survey, 58% of organizations asked claimed they base at least half of their routine business choices on gut feeling or experience rather than statistics and knowledge. On average, they discovered that organizations would only use half of the information available for decision-making.Now that we've laid the groundwork for successful data-driven operationswe'll look at the most typical obstacles or challenges that data analysts and businesses may face. By monitoring and digesting these essential aspects, you will be able to keep your approach consistent, results-driven, and focused on your goals at all times.The most common argument given is data quality. According to author Thomas C. Redmann's essay, data quality refers to a set of qualitative or quantitative variables that are "fit for [its] intended uses in operations, decision making, and planning." A solid data quality management (from capture to maintenance, disposition to distribution processes in place within an organization) 

is also critical for the future usage of this data. Collecting and gathering 

are only beneficial if the assets are properly managed and used; otherwise, their potential stays untapped and useless.Over-reliance on previous experience.Any firm can fail if it relies too heavily on previous experience. If you are constantly looking behind you, you risk missing out on what is in front of you. Many corporate leaders are hired based on their previous experiences, but surroundings and markets change, and the same methods may not work the next time. One of the most famous examples is Dick Fuld, who rescued Lehman after the LTCM catastrophe. Ten years later, he used the same methods, but, as the Wall Street Journal reports, "the experience he was relying on was not the same as this massive housing-driven collapse." The present problem was even more complex. Environments and markets are always changing, and a competent manager must blend previous experiences with current facts.Going with your intuition and cooking the evidence.While some managers instinctively follow their intuition, a sizable proportion initially trust their gut and then encourage their researchers or an external consultancy to create findings that validate their previous decisions. According to the Enderle report, this was widespread at Microsoft. Researchers were tasked with producing findings that would provide credence to the executives' judgments. This should be avoided at all costs by incorporating accurate information into any major change or decision.

Cognitive biases are inclinations to base decisions on incomplete information 

or lessons from previous experiences that may be irrelevant to the current circumstance. Every decision we make is influenced by cognitive bias. These biases might drive corporate leaders to ignore strong evidence in favor of their own assumptions. Here are some instances of cognitive biases that are widely observed:Confirmation bias: Business leaders prefer information that supports their prior opinions, regardless of whether they are correct or incorrect.Cognitive inertia is the incapacity to adjust to changing environmental situations while adhering to old views, despite data demonstrating otherwise.Group Think: The urge to belong to a group by siding with the majority, regardless of evidence or motivation.Optimism Bias: Making decisions with the expectation that the future will be far better than the past.Managers must know that we are biased in all situations. There is no such thing as objectivity. The good news is that there are approaches to overcoming prejudiced behavior. As a result, these businesses may better discover business opportunities and predict future trends, resulting in increased revenue and growth through data-driven decision making.