Part I

What is bias? It can be defined as “Our unintended potential to make mistakes in reasoning, evaluating, remembering, and our decision making as a result of holding onto one’s pre-existing beliefs – regardless of any available contrary information.” When we are making judgments and decisions about the world around us, we like to think that we are objective, logical, and capable of taking in and evaluating all the information that is available to us. Unfortunately, these biases sometimes trip us up, leading to poor decisions and uninformed judgments.

• Objectivity is a mental attitude that focuses on being impartial, fair, unemotional, ruled by evidence, neutral, open-minded, and detached from outcomes.
• Subjective information can lead to traps. Subjectivity is information that is colored by human perceptions, feelings, or opinions rather than being independently verifiable.

Let’s talk about objectivity and non-bias in finance and how it drives significant organizational value. Because financial information is built to be meaningful, users are expected to process and react – they USE the information. The best delivery vehicles to produce this information are courtesy of robust financial reporting and financial analytics packages. They’ll continue to consume and apply the information as long as finance departments are timely, credible, and implicit trust exists. Finance partners (internal and external) depend on smart decisions, many based on the careful, thoughtful work that finance professionals put into creating information, and the way that the information is produced and disseminated.

How can we (or finance partners) be biased? Some textbook examples include:

• Belief in history as a solid predictor of the future (said another way, “disbelief in prediction”).
• Assumption of linearity. A common mistake we make about financial models is linearity. We assume that the relationship between any two variables is easily expressed with a straight-line graph. Small forecasts won’t manifest this error as evidently as large forecasts, where non-linear relationships can stretch and skew the outcomes.
• Assumption of normality. We wrongly assume that random variables always follow normal distributions under any circumstances – our love of the “bell curve.”
• Assumption of quantitative knowledge. We assume that we have the mechanics of the model, and the answer/output without actually wasting time on a model. This includes our avoidance of performing critical thought, input collection, input testing, etc. This can come from impatience or even intellectual laziness.
• Missing variables. The misguided belief that relevant inputs are not actually a factor nor an influencer, now they can vary.
• Disbelief in variables. Our inability to accept that an input is not variable, but rather we stoically believe that it is highly predictive and fixed.

Motivations – especially in Budgeting and Forecasting

• While bias can be an invisible force of assumptions – motivations can stem from emotions, selfishness, political forces, ego, or avoidance.
• Incentive structures (“set an easy goal, over-perform”).
• Budgets (“inflate necessary expenses to appear to be a hero when you underspend”).
• Funding (“this is more important to me personally than other competing ideas”).
• Perpetuation by keeping “forever funding” (“I cannot shrink my organization or department”).
• Wasteful spending (“I need to get dollars ‘out the door’ as quickly as possible, regardless of the organizational value that this spending brings”).
• Ego (“I am the smartest person in the company. I’ve been doing this many years”).
• Conflict avoidance (“Let’s just take the easy path”).

Next week, we’ll explore our own potential biases that we need to be aware of, and how having a great budgeting tool can enable us to review and understand changes, patterns, trends, risks when comparing budgeting and forecasts, and spot/mitigate impactful bias from budget template owners.