Data-Induced Procrastination - What it is and How to Combat it.

According to a survey published by Accenture and Qlik in 2020, companies suffer from an average of five lost work days per person per year from data-procrastination. So, what exactly is data procrastination? Essentially, when someone becomes overwhelmed by the amount of data or how to interpret it, they delay analysis and reporting.

What causes data-induced procrastination?

1. Lack of data skills

Not everyone, including executives, has the training or experience to read, organize, and interpret data. According to the Accenture survey, 75% of employees are expected to read data, and 63% to make data-driven decisions. Oftentimes, there is a small group of specialists like data scientists and analysts who gather and disseminate data. But the majority of employees are then expected to interpret and utilize it. According to a Harvard Business Review article by clinical psychologist Alice Boyes, one of the reasons individuals procrastinate is the avoidance of negative emotions. If people lack a general understanding of how to use data, they will avoid it out of the fear of uncertainty.

2. Lack of complete data processes

We’ve all been in a situation where a subject matter expert leaves a team or position, and with them goes their knowledge of how a process is supposed to run. Without proper documentation, personnel changes can result in the interruption of processes, especially when it comes to data literacy.

3. Changes in technology

If employees aren’t trained on software that is supposed to assist with better data management and reporting, they will likely continue to use outdated technology, or worse, avoid the new software together. How often have you heard, “it’s easier for me to do it in Excel”?

4. Lack of trust

According to Accenture, only 37% of employees trust decisions more when based on data. Shouldn’t data make all decisions more trustworthy? Trust can be lost in a variety of ways, including mistakes in reporting, inconsistency, and cognitive dissonance when the data doesn’t match expectations.

If you’re experiencing a case of data procrastination at your organization, what can be done to make data gathering, reading, and interpreting more successful? The following tips are some ideas for how to identify and rectify the issues.

How to overcome data-induced procrastination

1. Recognize the specific issue/s at hand

What exactly is causing your data procrastination issues to arise in the first place? Are stakeholders not utilizing dashboards or are they failing to interpret the data correctly? Are analysts not using the available software to design automated and efficient visuals? Work with team members to identify where the data literacy or process gaps exist so you can correctly diagnose the issue. Once identified, you can now move forward with how to address it.

2. Be clear with expectations and goals when it comes to new data projects

• Set Tangible Goals - Before you even start to think about the final result of a data project like a dashboard or a report, you need to identify specifically what you are trying to address. A single data set or a dashboard is not going to solve all of your end users’ problems. Define tangible goals and work towards them. It’s good to get feedback from all stakeholders, but remember that a horse by committee is a camel, and trying to solve everyone’s problems with a single dashboard typically results in a sub-par analytical product.

• Set Expectations – Once you have decided what problem you are trying to solve, communicate that back to your stakeholders. Be sure to define what the data is supposed to show as well as what it does not show. If users expect the data to show one thing, but the results are used for something else, lack of trust can result when the report doesn’t deliver on the expected solution.

• Be Consistent – Design policies and procedures around how to use data correctly, and hold employees accountable for following those policies. If a policy states that a dashboard will be updated on a specified recurring basis, ensure this is completed, otherwise users will begin to doubt the findings if it’s behind. When at all possible, automate. A drawn-out manual refresh of a data set that is required to be updated regularly will eventually lead to a mistake. It’s not the fault of the person updating, it’s just human nature. It also helps for redundancy when that person changes roles so that a process isn’t interrupted.

3. Simplify

36% of individuals in the Accenture survey said they felt overwhelmed by data, and the same percentage said they would find an alternative method to accomplish a task (and 14% would avoid the task entirely!). Even the most data-savvy of your end users can get overwhelmed by the data presented to them and how best to analyze it. For dashboard creators, make visuals easy-to-interpret and navigate. A user should be able to easily interpret results without much direction. Before finalizing any data product, it’s good to have someone who isn’t a subject matter expert give it a test run. If they don’t understand what it’s trying to convey or how to use it, then you likely need to further simplify your design.

4. Train all your employees on how your data tools work.

Sometimes the problem with data end users isn’t data literacy, it’s just a lack of experience with certain tools. If your employees don’t understand how your outward-facing data tools work, it’s no surprise they can become overwhelmed.

Regardless of what solutions you employ to combat data procrastination, acclimation is the key. Change can be difficult, but with the right training, policies and frameworks in place, employees will feel more empowered to embrace data-driven solutions once they see the results. It takes time, but understanding leads to empowerment and less procrastination, which produces greater productivity.

5. Build trust in data with Mighty Canary

Combat data-induced procrastination and empower your employees with the information they need to make good decisions.

Mighty Canary is building tools to help analytics teams build trust with their data customers. Communicate and support data customers in real-time and provide the answers needed to build confidence and trust.

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