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  • Writer's pictureAbbey Salvas

When Missing Data Tells a Story: The Importance of Trust in DEI Data Collection

Originally posted on the Mattingly Solutions Blog.

Organizations seeking to make progress in diversity, equity, and inclusion (DEI) efforts usually take the first step of either collecting DEI data (e.g., workforce demographics, how frequently people are treated inclusively, how much they feel like they belong, etc.) or reviewing data that they already have. In this process, questions often arise about what to do with missing data.

One example of where this missing data may arise is around disclosing demographic data. Individuals are often asked to provide their gender and their race or ethnicity for a variety of people ops. purposes.

But what does it mean when that data is missing? It could signal that your employees lack trust in your organization. This lack of trust could lead to fears around disclosing related to privacy concerns or even fear of retaliation or discrimination.

Another example would be a number of survey respondents choosing not to answer a certain inclusion- or belonging-related survey question, such as, “I feel comfortable speaking up in my team meetings.” This missing data pattern could mean a lot of different things.

First, people may not regularly attend team meetings. Others might not want to speak up, but it is more about their personality than it is about the conditions of the team meetings, so they choose to leave it blank. But one pattern that could be concerning is if individuals are not comfortable speaking up in team meetings, they’re not comfortable speaking up in employee surveys.

This example provides a lens into the complicated nature of collecting data in an effort to improve DEI. What happens when the very conditions that you’re trying to improve make it impossible to know exactly what needs to be improved?

What does missing data mean?

As the example above illustrates, missing data, by its very nature, is impossible to really figure out. But it has a lot of important ramifications. Below are a couple of factors that could lead to missing data related to DEI:

  • Concerns around disclosure

  • Particularly for demographic data, individuals may have concerns around revealing their marginalized identities, especially if there are very few individuals with those identities at the organization.

  • Concerns around privacy and retaliation

  • Individuals may feel that responses to surveys, focus groups, or even demographic disclosures, may be linked back to them and result in negative ramifications, such as lack of advancement or even firing.

  • Concerns about action

  • Sometimes when organizations are collecting DEI data, there is no plan in place for what they will do with that data. If this is a concern of employees, they may feel that providing their responses will go nowhere and, therefore, is not worth the time and energy on their part.

How can you reduce missing data?

The most important way to reduce missing data is to build trust with your employees. In order to address any of the potential concerns listed above, building trust with your employees can alleviate concerns and lead to less missing data. Below are three strategies for building trust with your employees in data collection efforts:

  1. Provide a clear policy for how individuals’ data will be protected, including nondiscrimination and confidentiality policies.

  2. Be transparent about how the data being collected will be used for action, outlining the importance of hearing every employee’s voice in order to make real change.

  3. Use best practices for demographic data collection, including making it clear that disclosure is voluntary and that confidentiality is a priority.

Taking these steps can help build trust with your employees so they feel comfortable providing the data needed to make real progress on diversity, equity, and inclusion in your organization.

Looking to advance DEI through data-driven insights in your organization? Contact us at Plan to Action today to drive meaningful change, together.

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