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CPA Now
Dec 08, 2021

The 3 C’s of Data Analysis and Diversity, Equity, and Inclusion

Lisa ApolinskiBy Lisa Apolinski, CMC


The business focus on diversity, equity, and inclusion (DEI) impacts more than just employee satisfaction. Research done by Quantum Workplace found that 61% of employees believe DEI strategies are essential and do affect the workplace environment. What’s more, diversity leads to multiple perspectives that can increase workplace innovation by as much as 20%, and that can have a big impact on the bottom line.

What may surprise you is that DEI can be applied to data analysis. Companies review data to interpret the actions of groups of people and then use that interpretation to develop additional strategies. Because there is a human element to how and why data is collected, certain assumptions can quickly derail efforts through unconscious bias. Context, correlation, and credence (the 3 C’s) can be tripping points where an eye toward DEI can help overcome flaws and misinterpretation in data analysis.

Context

Data analyst reviewing data profiles.Data does not exist in a vacuum. It must have some context around it to create a story. The number 2 does not have any context and therefore cannot be interpreted. If that number is $2, that one additional data point helps, but more information is needed: is it $2 for a cup of coffee or for an acre of land? The context that surrounds the data can make the data seem positive, negative, or neutral.

Avoid Context Assumptions – In the above example, if $2 was an example of a price for land, would you assume it must be in an undesirable location? What if that price was for an acre of land in 1823? Additional data points help fill in the data picture. Avoid assumptions by adding additional data points so context is not filled in based on individual bias.

Correlation

Correlation is one way in which data is applied. We develop strategies based on the data and how the data points affect one another. For example, your organization is looking to increase diversity in a particular department: the job is advertised with a recruiter and includes an educational requirement. The number of qualified people of color is less than expected. One assumed correlation would be that, because of the educational requirement, the pool of diverse candidates is less.

Avoid Correlation Assumptions – When the recruiter in this example is interviewed, you find that the advertisement targets did not include communities of color. The correlation of education to applicants was assumed, but it was not accurate. Take the time to dig deeper into why the data may show something unexpected. Be suspicious of possible correlation unless it can be statistically verified.

Credence

Credence is having a belief that something is credible or having confidence in something as accurate. Data, if tortured long enough, can give up any story required. Data simply is; it has no bias. What causes bias and shades of credibility is the viewpoint of the analyzer. Put another way, people see what they want to see in the data. Because of individual experiences, data points can be elevated or diminished based on experiences and bias.

Avoid Credence Assumptions – In the previous example, the educational requirement was given higher credence and the community target strategy was given less credence. It is a common and subconscious practice to focus on one subset of data. Before focusing on a subset, consider the data as equal and look at trends to help identify areas of interest. Changes in trends reduce credence assumption and highlights important data.

With so much information coming in, leveraging past experiences and focusing on seemingly important data points should be expected. Those experiences, however, do not need to be labeled right or wrong: they simply help eliminate data overload.

When companies recognize the assumptions are automatically applied and they become aware of biases that have the potential to alter the analysis of data, those companies are removing blinders that could have blinded them to opportunities.

These 3 C’s are some of the first biases applied to data during the analysis phase. They are the first because they are inherent and, as such, become embedded in the data application DNA. Having an awareness of how those 3 C’s also affect DEI can have a ripple effect in an organization.


Lisa Apolinski, CMC, is an international speaker, digital strategist, author, and founder of 3 Dog Write. She works with companies to develop and share their message using digital assets. For information on her agency’s digital services, visit www.3DogWrite.com.


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Statements of fact and opinion are the authors’ responsibility alone and do not imply an opinion on the part of PICPA officers or members. The information contained in herein does not constitute accounting, legal, or professional advice. For professional advice, please engage or consult a qualified professional.