At times, stress can be motivating. It encourages you to meet deadlines, work hard at the gym or break your creative boundaries. Low-level stress has also been shown to increase brainpower and give your immune system a boost.

On the other hand, too much stress can have terrible consequences for your physical,
psychological and emotional health. Symptoms include:

• Feeling overwhelmed
• Irritability
• Tiredness or difficulty sleeping
• Racing thoughts
• An abnormal lack of confidence
• Reduced creativity
• Difficulty concentrating
• Headaches and stomachaches

While these symptoms are easy to recognise, the knock-on effects of chronic stress are
much harder to see. According to Yu (2016): “When stressed, individuals tend to make more habitual responses than goal-oriented choices, be less likely to adjust their initial judgement, and rely more on gut feelings.” This means that, rather than using objective information to make well-reasoned decisions, a stressed individual is much more likely to misunderstand, misinterpret or misuse data. Similarly, the tendency towards habitual responding and unwillingness to change one’s initial judgement mean that your analysts might repeat the same mistakes with every performance report. Over time, this can have devastating effects on business growth.

In previous blog posts, we’ve discussed Daniel Kahneman’s work on the psychology of
judgement and decision making. Kahneman proposes that we have two systems of thought:

• System 1 (or “fast thinking”), which is quick and automatic, and involves no conscious input.

• System 2 (or “slow thinking”), which is slow and deliberate, using a lot of mental energy to consider information and choose an appropriate response.

When it comes to data analytics, we want System 2 in charge. With so much information
coming at us from so many data points, we must calmly evaluate what it is and why it
happened to turn it into effective action.

The problem is that “when under stress, fast and effortless heuristics may dominate over slow and demanding deliberation in making decisions under uncertainty” (Yu, 2016). Stress encourages us to let System 1 make all of our decisions, which obstructs our ability to rationally consider and spot patterns in data. This means that business leaders are left to follow irrational, unsupported guidance on how to adjust their approach. We waste time and money on actions with minimal return of investment, which is also likely to further increase stress and undermine progressive decision making.

The solution seems incredibly simple: reduce the chronic stress of your data analysts, and they will be better able to provide comprehensive performance reports. Unfortunately, “the process of decision making in and of itself can be stressful, such as when a decision involves high risk and its outcome is uncertain” (Wemm and Wulfert, 2017). Given that analytics is used to adjust business strategies based on interpretations of data, high risk and uncertain outcomes are unavoidable. Even if all other stressors could be removed, using analytics to make decisions for business growth is itself a stressor that can reduce the effectiveness of conclusions reached.

How, then, can business leaders ensure that the stress of data analytics doesn’t impair the actionable insights that analytics is supposed to produce?

In a study of the effects of stress on managerial decision-making, Gok and Atsan concluded that “involving more people in the decision making and consulting the expert judgement can assist managers to better evaluate the negative and positive consequences of their actions” (2016). In other words, by employing a diverse team of data analysts, business leaders can maximise their ability to evaluate data and use it to encourage business growth.

One solution is to recruit more members to your analytics team. However, hiring additional team members creates the financial strain of additional salaries, expanding the workspace, and ensuring that your team has all the resources they need. Introducing unfamiliar elements also means that decisions are being made by people who might not fully understand your goals and strategies, thereby adding to the uncertainty.

Another solution is to outsource the task to an established team of expert data scientists. The lack of a vested interest in your business drastically reduces the stress of data analysis, allowing a thoughtful, unbiased evaluation of your data. In addition, reducing your staff’s workload and reallocating resources spent on reporting could relieve some of the stress placed on your team. They can then devote more energy to other tasks.

The main problem business leaders have with choosing an off-the-shelf solution is cost. If you already employ people to analyse your data, why pay to have the job done twice?
However, this attitude is short-sighted. Not only are these solutions surprisingly cheap, but the insights of a dedicated team and the reduction of stress on your staff can mean
significant time and money saved in the long term.

By hiring a group of expert data scientists, you’re not just paying for analytics; you’re
investing in objectivity, experience, talent and out-of-the-box thinking that could push your business further than ever before.

Here at Good Growth, we offer such a solution with 4Front. It’s a monthly subscription
service that gives you access to an amazing team of accomplished data scientists who can bring you the real story behind your data. Some say you can’t put a price on peace of mind, but we did – and it’s not very high.

To learn more about 4Front, or to request your free demonstration, visit
https://4front.goodgrowth.co.uk/ and decide to save time, money and energy today.