How analytics reduces decision-making bias

One of the greatest benefits of data analytics in business is its ability to remove bias and provide absolute clarity, bringing better decisions, less errors and more positive results. Here’s how it works. 

Conscious and unconscious bias 

Problems and issues arise in business on a daily basis. Each must be responded to, but the type of response can vary depending on a decision that is made, usually by a manager. 

As human decision-makers, however, our opinions are often influenced by biases whether we are conscious of them or not. As an example of a conscious bias, we might actively seek out information to influence our decision by turning to learnings from past experience. 

That past experience, research tells us, tends to give us a high level of confidence in the probability that our decisions are correct, but confidence can be a double-edged sword. 

The more dangerous biases are those that influence without us realising. Sometimes we fixate on a specific piece of information, meaning we miss more important data that could lead to a better decision. Sometimes we only look for information that will confirm the decision we subconsciously want to make. And often we miss the deeper problem, instead looking for a solution tino a symptom. 

Interestingly, often in the workplace, the person who sees the bigger picture and recognises a better solution to a problem is an individual who is not as deeply connected to the subject area. However, because of the need to conform and the effects of groupthink, these people often don’t speak up. And if they do, they are sometimes silenced. 

All of these bias issues, and more, add up to the fact that there must be a better way to make decisions in business. Good decisions don’t rely only on historical experience or emotion, they don’t take into account only a specific set of information, and they’re certainly not made after a search for data that supports an already-decided course of action. 

Good decisions come from a proper analysis of the facts and of all possible options. They are guided by experience and industry knowledge, but they are not controlled by it. 

The very best decisions, particularly in an increasingly complex business environment, come from a combination of data analysis and a deep understanding of the organisation’s strategy and its position in the market. In other words, it comes from a combination of learned experience and data. 

Analysts who utilise data effectively for better decision making in their organisations will inevitably find the businesses for which they work will gain a competitive advantage over competitors. 

How data analytics helps with decision making

How does data boost the decision-making process on a practical level? 

A report from PwC and The Economist Intelligence Unit, called Gut & Gigabytes, said “highly data-driven companies are more likely to report improvement in big decision making, yet most executives don’t believe their organisations are at that level.” 

The report defined ‘big decisions’ as significant decisions around the strategic direction of the business. 

Amongst the 1135 senior executives surveyed for the study, many identified one of the great strengths of data as offering the ability to test various scenarios before a decision is made. The results of such tests help experienced business managers interpret multiple options and decide on the very best way forward. 

This type of big-ticket A/B testing, however, is just the tip of the iceberg in terms of how data can be utilised to inform better decisions in business. Here are some more practical examples of data-driven decision making. 

Analyse decisions from the past:
Great leaders are self-aware leaders, and self-aware leaders are always keen to learn more about why they and their business do the things they do. Analysts can help with this by looking at data around past decision making, to reveal previously hidden biases that have driven those decisions. Once those biases are in the open, decision-makers can consciously work with or around them. 

Find businesses that fill your gaps:
The identification of merger and acquisition targets is a complex and time-consuming task. Much of the heavy lifting, however, can be managed through data analytics. So in an environment in which growth is increasingly challenging, a data analyst is an important member of a team charged with identifying potential strategic partners, collaborations, mergers or takeovers. 

Identify growth areas:
Machines are excellent at recognising patterns and trends well before they are visible to the human eye. Sales data from a business, and from the industry in which it operates, can often identify growth markets, new territories and regions at risk of contraction. It can do this by identifying patterns in other regions where contraction, for example, has occurred. It then looks for early indicators of the same pattern developing in currently ‘healthy’ regions. 

Discover your focus areas:
Just as important as growth in a business is intentional shrinkage. More specifically, it is always important to ensure the organisation has a core focus on its strategic goal. Distractions, or less profitable projects/departments/branches, must be made smaller or removed to reduce their negative effect on the larger business. 

But where are the areas that require shrinkage? How does a business ensure it is closing the right doors? Once again, data can be utilised to ensure that one piece of information (profit and loss data, for example) is not used on its own to drive a decision that could have an unseen negative effect elsewhere in the organisation (organisational culture, for example). 

Model growth strategies:
Growth involves finance. Finance involves risk. But that risk can be somewhat mitigated when various models are set up to run their course in the safety of a closed, data-driven ecosystem. The most basic and popular way that data analysts can prove their worth to organisations is to set up a modelling environment in which data can be added and manipulated to mimic the effects of various market movements. What happens if a supply chain is disrupted as a result of geopolitical changes? If cash rates were to suddenly and unexpectedly lift, how would that feed through to P&L? If demand was far greater than expected, how would that affect talent requirements, etc? 

There are countless ways that data can be used to remove bias and support great decision making in business. It’s one more reason why data analysts are in sharp demand across sectors and industries as organisations seek competitive advantage.

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