Using Data and Company Culture for Better Decision-Making

Once upon a time, decision-making was the purview of the C-suite. In the 1950s, only about 6 percent of jobs required the employee to make decisions. By 2018, that percentage had increased to 34 percent. The proportion of workers making decisions accelerates as automation and artificial intelligence take over more tasks.

Business leaders must give their workforce the tools and authority to make decisions while promoting a culture of decision-making based on data.

What is Your Gut Telling You?

Decisions are often based on intuition. Much has been written about "trusting your gut" when making choices in every aspect of life. Senior leaders may feel they can make an appropriate call based on their experience and instincts.

However, executives often don't know what they don't know. Humans are subject to cognitive bias and emotion. We usually believe that when something works once, it will work again. Yet, given the rapid pace of change, solutions that once satisfied customer expectations and operational problems may no longer work.

There are situations when a "gut feel" is an important part of decision-making. Professor and author Laura Huang counsels,

"Reserve your intuition for those decisions that go beyond routine, where calculations of probabilities and risks are not only unrealistic, they are infeasible."

She identifies new product launches and business divestitures as situations in which there is insufficient information to make a fully data-driven decision. Huang notes that gut instinct can lead to successful choices, but it should not be used in a vacuum. Instead, hunches and opinions help assess available subjective and objective data.

Instinct can complement data analysis. As noted in a Harvard Business Review blog post,

"When decision-makers feel stuck in data, instead of overthinking, they should try listening to their gut. It just might help make the right call."

Leaders should cultivate their instincts by observing patterns. They can then apply what they learn to future situations.

The Need for Data

Instinct can be a valuable starting point. But data informs our decision-making and validates a course of action. It provides confidence in an outcome that gut sense alone cannot provide.

In Forbes, Bernard Marr identified four areas where data contributes to better decision-making:

1. Decisions related to customers, markets, and competition. Information related to consumer purchases and preferences is practically limitless. Not that long ago, information came from focus groups and test markets. Now, those techniques are supplemented by reams of consumer and customer data to be harnessed and analyzed.
   
   
2. Decisions related to finance. Finance is the ultimate in data, whether it be about actual or forecasted results. Financial system users can drill down through layers of information. They can generate charts and graphics to aid in understanding and interpreting data.
   
   
3. Decisions related to internal operations. Data informs supply chain management about inventory, lead times, and costs.
   
   
4. Decisions related to people. Google's Project Oxygen gathered data from employee performance reviews, surveys, and nominations for top-manager awards. Management used it to determine what makes a manager successful at Google. The results are used to train new managers and help retain employees.

Not All Data is Good

A shift to using data for decision-making does not guarantee credible results. In Forbes, Howard Rosen cautioned,

"Fundamentally, for data to be of any value regardless of its form, it needs to be considered "good" data that, when analyzed, could result in actionable strategies."

Data proliferation does not mean that it is all objective and unbiased. Employees need to be curious about the source of information and not take it at face value.

Further, the data collection process and interpretation may be flawed. Data can be employed just to validate a leader's decision. Confirmation bias is our tendency to seek and select information that confirms one's pre-existing views on an issue. No one likes to think they are wrong, so they look for data that will confirm the accuracy of their assumptions. Sometimes, they see patterns that support their idea rather than objectively assessing the data.

Employees should ask unbiased questions in surveys and focus groups to gather information. If groupthink sets in, team members should play devil's advocate and debate the opposite point of view. It's also important not to stop the search for data once you have the desired answer.

Author Cheryl Einhorn says that it is helpful to pause before making a final decision and ask yourself:

• Are my feelings related to this decision based on what's actually happening, or do they reflect my learned behavior patterns?
• What additional information is out there that could help me make this decision better?

Always consider whether the data supports a point of view other than the one you want it to.

A Data-Driven Culture

Data and culture intersect in two ways. First, an organization needs a culture of using data to make decisions. While gut instinct and experience may have worked well in the past, the rapid pace of change may render former approaches unusable. Organizations collect so much information now – it's logical to use it to make decisions about strategy, customers, finances, and people.

Google's use of data to address HR issues is unsurprising since data is their business. Other companies may find that using data instead of hunches requires a culture shift. In many organizations, the boss makes the decision, and the employees implement it. Seeking information before coming to a decision may seem foreign.

Likewise, there may not be much delegation of decision-making authority. Investment in systems and employee training or reskilling may be required. People may need to hone their critical thinking skills to help them understand cognitive biases that may affect their decision-making.

However, given the rapid pace of technological change, organizations must seek answers through data analysis to make credible decisions.

A Decision-Making Culture

In addition to being data-driven, organizations need a culture where employees feel comfortable making decisions and challenging others' decisions. For example, an executive may champion a new product. Focus group results and other data do not merit a launch. Employees may feel pressured to support the plan because the boss likes the product.

However, in a data-driven decision-making environment, employees should feel comfortable challenging the launch plan using data. Other employee behaviors that characterize such a culture include:

Trust. Management creates a trusting environment where employees feel respected. Team members are trusted to make the right decisions, and their managers support their choices.

Empowerment. Leaders delegate responsibility and empower their teams to make decisions as appropriate. Employees are given access to data and trained to interpret it.

Psychological safety. Employees are encouraged to respectfully challenge management's decisions and use data to support their opinions. They feel comfortable speaking up in a way that helps make progress.

Curiosity. Asking questions is supported. Employees are encouraged to "think differently" and challenge the status quo. Innovation and intelligent risk-taking are rewarded.

Listening. Employees know their manager is available to be a sounding board when they have questions or seek advice.

Author Cheryl Eichorn identifies four types of active listening in decision support:

• Emotional support - listening without judgment and responding with empathy. Managers resist the impulse to try and fix things or to talk someone out of how they are feeling.
  
  
• Informational support - offering a decision-maker the information they want rather than the information the manager prefers to provide. This can help someone better understand a situation and assess potential next steps.
  
  
• Analytical support - assists the decision-maker with examining, interpreting, and analyzing the information they have but are unsure how to proceed.
  
  
• Reflective support - involves asking questions that assist the decision-maker in better identifying their thoughts and priorities. This is useful when the decision-maker has a solution in mind but hesitates to act upon it.

Decision-making is naturally prone to judgment errors and flaws. There is no failsafe approach. However, by implementing data-driven decision-making in a culture that encourages healthy analysis and debate, there is a stronger chance of making the right choice.