Employees across industries often find themselves at the center of decision making that is critical to businesses. From client oriented front end marketers and sales professionals to senior leaders within organizations, all take judgment based decisions on matters that, when taken together, have a strong impact on the business. The portion of the personal judgment that factors in decision-making, in most such cases is fairly high. And with this high proportion of business decisions being taken mostly on 'gut' feeling, the chances of committing an error and misinterpreting facts, due to both internal and external influences, also rise. Mostly insignificant when taken in isolation, their total contribution cost businesses in billions, according to some  estimates. In an authored article by Noble Prize–winning behavioral economist Daniel Kahneman and TGG’s CEO Andrew M. Rosenfield along with Linnea Gandhi and Tom Blaser, this human ability to make erroneous decisions has been referred to as noise.   

What is noise?

Human behavior is quite erratic in nature. Our decisions are often colored with the mindset that we operate in. It might influence our way of interpreting facts and figures to simply ignoring vital pieces of information because one doesn't 'feel' like it.  It is this erratic behavior which is referred as noise when it comes to making decisions.  It refers the probability of for making an error which is an inherent part of the decisions that we take on a regular basis. Researches have shown a high variability in the decision made by employees during different instances but with similar situations. 

This can often be disastrous for businesses.  Decision- making processes among humans are often impacted by both external and internal influences.  Factors such as the person's mood and temperament often end up impacting the quality of the decisions that they take, making humans erratic in their decision- making process. It introduces a certain amount of unreliability in human judgments. This scope of variability often comes with a high hidden cost to companies which ends up impacting their bottom line performance.  

Noise is referred as the inherent human capability to make wrong decisions.

Being an unconscious factor, it often gets by unnoticed when it comes to employees taking critical business decisions.   Recognizing and tackling it, therefore, becomes a vital task for HR professionals as they work towards making employees more efficient and productive for businesses.   

Noise vs Bias

Biases are often held as culprits when it comes to an error in human decision making. They can be in the form of socials biases like stereotyping of minorities and cognitive biases like overconfidence and only accepting information that confirms to their set beliefs. Although such biases often lead to an error in decision making they are often identifiable and that makes it different from what the authors refer to as noise. An example that the authors give of this difference is by taking an example of weighing scale. “To appreciate the distinction, think of your bathroom scale” write the authors, “We would say that the scale is biased if its readings are generally either too high or too low. If your weight appears to depend on where you happen to place your feet, the scale is noisy. A scale that consistently underestimates true weight by exactly four pounds is seriously biased but free of noise. A scale that gives two different readings when you step on it twice is noisy.”

Algorithms to the rescue

Many companies have already started taking in inputs from nuanced data analytical programs and algorithms that help employees across levels perform more efficiently and reduce the chances of errors in decision making. From the banking sector to sectors like Pharma and IT employ programs that help both employees and senior leaders within the companies take better decisions that impact bottom line performance.  Organizations today are using data analytics and algorithms to make decisions across departments. From recruitment to finance, the aim of using algorithms is to make decisions more rigorous and closer to the reality of the situation. By using advanced statistical model, leaders and decision makers can successfully execute decisions that, if taken solely by the individual, would lead to both noise and biases to creep in. Since organizational noise is inherently more difficult to detect and recognize, factoring in algorithms help reduce the chances of individuals being swayed when it comes to executing decisions.    

Striking a harmonious balance

What HR professionals need to do to leverage such technologies today, is to work with senior leaders to increase their usage of algorithms driven insights. Since most employees end up trusting their own judgment when it comes to call, it is important to introduce practices that restrict their aversion towards trusting data-driven insights. HR professionals play a crucial role here by enabling employees within the organization to build a trust in algorithms power in creating useful information and that their own decision-making can be rigged with errors. 

In today's highly competitive business scenario, decision-making rarely comes with the comfort to it being taken in a calm and composed manner. Most business decisions today are taken in situations which require real-time insights where the liberty to fully comprehend the pros and cons of the situation might not necessarily be available. In situations like that, algorithms that support decision making across functional areas becomes a vital tool in enabling employees to take noise-free decisions. Factors like taking in all the relevant inputs for a decision and giving each input the right weights become imperative to take the right business decisions. Using algorithms helps companies, big or small, ensure that their line heads and senior leaders alike, take better business decisions.  But it is the role of HR professionals to make it a vital component of the organization's culture and reduce the impact of ‘noisy’ decisions on businesses bottom line performance.