Bad decisions (or at least not the best decision) are made more frequently by business analysts than one might expect. This is often due to a number of biases that occur when gathering information and analysing patterns and trends.
Some common biases that can occur are:
Anchoring Bias: Being over-reliant and placing too much emphasis on the first piece of information obtained.
Groupthink Bias: Assuming that the group has arrived at the right answer because there is agreement. This often occurs when analysts haven't taken the time to gather enough info. Everyone thinks the same way so it must be right.
Availability Heuristic: Overestimating the importance of information that is available for no other reason than simply that it's readily available. Often times information that isn't readily available is just as necessary to avoid a bad decision. Of course, it's rare to have all information to make a perfect decision but it's important to know when to keep digging.
Confirmation Bias: Focusing on information that confirms what is already believed to be true and casting off or ignoring the bits of information that contradicts what is believed to be true. A clear example of this is when people discuss their political views. But confirmation bias can occur in many business circumstances.
Outcome Bias: This is basing a decision based on witnessing a specific outcome but without proving what actually caused that outcome. A great example of this is winning money at a game in Las Vegas and assuming it's due to your winning betting strategy, when in reality the betting strategy might prove to be a failure over the long term and you just got lucky.
Clustering Illusion: Seeing patterns in otherwise random events. For example, if you saw a roulette wheel hit red several times in a row you might be convinced it's more likely to hit black next. When in reality it's still statistically a 50% chance.
Salience Bias: Focusing on what is the most recognizable or prominent rather than what is statically the most likely to occur. Most people will focus more on the risk of a plane crash rather than a car crash, when in reality many many more people die of car crashes every year.
Survivorship Bias: Focusing only on surviving examples causing the analyst to misjudge a situation. For example, if you were analyzing a specific characteristic within a company but don't have information about the characteristic within companies which have already failed. This could also be applied to surviving versus failed IT systems.
Recency Bias: Weighing the most recent data more heavily than older data.
Innovation Bias: Overvaluing what's innovative and overemphasizing it's usefulness while overlooking it's flaws. This is common within our current entrepreneurial/startup culture where new products are being launched all of the time.
Placebo Effect: Simply believing something to be true and therefore influencing the outcome in such a way that it becomes true.
By understanding and bearing in mind these common biases, the business analyst can avoid some of the pitfalls that lead to a less than ideal decision.
Credit: Chris Adams
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