We like to think that our decisions are based on rational facts and not a guess or a hunch. However, that is not always the case and sometimes our biases influence our thinking. Even at the most data-driven companies, allowing for some predispositions can negatively impact results.
A classic example of cognitive bias is the phrase, sour grapes. In the Aesop fable, a fox sees some juicy grapes but cannot reach them. Because of that, he assumes the grapes must be sour and moves on. Psychologists suggest the fox blindly puts a negative word (sour in this case) on the grapes to avoid the disappointment and pain of being unable or unwilling to pursue them. So too, in our own business, it is easier to change the interpretation of the data or artificially manipulate the data to fit our own agenda and avoid disappointment.
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As long as you know that a bias may exist, it's up to you to either use it in your data narrative or eliminate it from your analytics. Here are 10 common cognitive biases that can interfere with your data insight and some suggestions for overcoming those obstacles.
Anchoring Bias—you rely too heavily on, or "anchor", on one trait or piece of information when making decisions. Usually, it's the first piece of information acquired on that subject. How often do the headlines dominate your boardroom discussions? Oracle Analytics allows you to integrate multiple sources of data, so you don't have to limit your decisions to the loudest voices.
Availability Heuristic—you overestimate the likelihood of events and are influenced by how recent the memories are or how unusual or emotionally charged they may be. For example, you consider that your supply chain is immune to global events because you use local suppliers. Those suppliers, however, may be at risk themselves to outside forces. A better approach might be to use analytics data modeling to consider all options.
Choice-Supportive Bias—you remember your choices as better than they were. I have the best dog in the world (says, everyone) … except that they do bite people occasionally. Oracle Analytics allows you to apply a model within a data flow to generate a data set and see those results in a data visualization. In that way, you can spot all the times Spot was a good boy and the times he wasn't.
Clustering Illusion—you overestimate the importance of small runs, streaks, or clusters in large samples of random data (that is, seeing phantom patterns). Why guess? Oracle Analytics has a clustering model that can evaluate groups with similar traits. For example, you might assign your costumers into clusters (such as big-spenders, regular spenders and so on) based on their purchasing habits.
Confirmation Bias—you search for, interpret, focus on and remember information in a way that confirms one's preconceptions. If you were trained to think that customer relationships close sales and losing a sale meant the price was too high, you will have a bias toward data that proves otherwise. It's better to back up a decision with data and becoming data-driven.
Information Bias—you seek information even when it cannot affect action. Is there such a thing as too much data? While some may say, no, there are times when you want to look at specific parameters. Oracle Analytics data visualization feature allows you to identify and isolate specific data attributes, thereby allowing you to see the data you want and when you want it.
Ostrich Effect—you ignore an obvious (negative) situation. Research suggests that people often avoid looking at their debts if they are far behind on their payments. The remedy is to ensure you have a reliable notification system that uses machine learning and artificial intelligence to flag up any problems within the parameters you set. Oracle Analytics Mobile in Analytics has such a notification.
Pro-Innovation Bias—you have an excessive optimism towards an invention or innovation's usefulness throughout society, while often failing to identify its limitations and weaknesses. This is best illustrated in the rash of desktop analytics products that are limited in their usefulness. They can neither scale-up or scale-out. Choosing a cloud-based analytics tool that integrates into all parts of the business and can provide a single source of the truth can help prevent this bias.
Recency Illusion—you hold onto the illusion that a word or language usage that one has noticed only recently is an innovation when it is in fact long-established. Not everyone or everything is "industry-leading." The way to measure performance is to generate detailed information. In this way data is sourced without bias from the top data providers.
Zero-Risk Bias—you prefer reducing a small risk to zero over a greater reduction in a larger risk. Everyone loves a sure thing. The problem is that true progress comes when we make bold moves. Data-driven companies know to rely on augmented analytics tools like Oracle Analytics to uncover deeper patterns and predict trends for impactful, unbiased recommendations.
While even the most seasoned data scientist is susceptible to these cognitive biases, understanding them and identifying them in your decision making can help you improve your overall business analytics.
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