Who is in the Data Trust? Understanding the people behind the data

September 13, 2026
Categories: General News

by Sydney Idzikowski

In 2025, there were 397,572 unique people represented in the Charlotte Regional Data Trust data, about 33% of Mecklenburg County’s population.

If that number reflected the population of a city, it would be the THIRD LARGEST in North Carolina. Curious about the characteristics of those individuals? Let’s take a closer look at what the data show, together. 

Robust and reliable demographic data collected by our data partners gives us  a stronger understanding of the people represented in the Data Trust data. From an array of ethnicities and races to a range of gender groups, ages, neighborhoods and more, we have the ability to learn a lot about the broader community. And that is crucial to our work in the Charlotte region. 

[Dive deep and read the 2026 State of Our Data Report HERE!]

Let’s continue to think about the Data Trust as its own city – we can call it Data Trustville. In 2025, the population of Data Trustville was:

  • Fairly young. The median age of most people was under 40 years of age. This was  true for all but two of our data partners. Fifty-three percent  (n=9) of our partners maintained information about children. 
  • Majority people of color. Approximately forty-seven percent of people identified as Black or African American,  while 15.9% identified as multiracial and nine and a half percent  identified as Hispanic or Latino. Nearly twenty-two percent  of people identified as white, representing the second largest racial group of people in Data Trustville. People who identified as Asian, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander were also represented, but in smaller numbers (4.4%, 0.2% and 0.1%, respectively). 
  • Evenly male and female. Fifty-two percent  of people in Data Trustville identified as female and 46.2% identified as male. Only a small percentage of people identified with other categories or  did not specify their gender. 
People across the life cycle are represented in the Data Trust. Most partners have a population where the median age is under 40 years old.
The population is evenly male and female.
 In 2025, the majority of people in the Data Trust identified as Black or African American. People who identify as white represent the
second largest group. (n=397,572).

Representation: Disproportionate overrepresentation or underrepresentation refers to how the data we hold compares to data on the overall population of the county or the particular area we are describing. Understanding representation is important in the use and interpretation of Data Trust data.

How do these numbers compare with our real city and county population?
People whose data is part of the Data Trust live across Mecklenburg County, NC. Here’s what we know:


Age = Representative: The median age of people in the Data Trust was similar to the median age of people in Mecklenburg County, overall. The average age of residents in Mecklenburg County was 36 years old (U.S. Census Bureau. American Community Survey 5-Year Estimates, 2018-2022), and the median age of most people in the Data Trust was 40.

Gender=representative: The gender distribution of people in the Data Trust was  representative of Mecklenburg County overall. However, we may have undercounted people who identified with a gender other than male or female due to limited data collection.

Race and Ethnicity = Not Representative: The 2024 population of people in the Data Trust differed from the overall population of Mecklenburg County (U.S. Census Bureau. American Community Survey 1-Year Estimates, 2024).

  • Compared to the Mecklenburg County population, people who identified as Black or African American are overrepresented in Data Trust data. In Mecklenburg County in 2024, Black or African American individuals were 29% of the population and 46.5% of people in the Data Trust. 
  • People who identified as multiracial are also overrepresented in the Data Trust compared with Mecklenburg County, with more than three times as many multiracial individuals. This may be because the Data Trust counts every racial category a person identified in any dataset, capturing more detail than the U.S. Census may report. 
  • White individuals are underrepresented in Data Trust data in comparison to the Mecklenburg County population, as are Asian, and Hispanic or Latino individuals. 
  • There was a slightly higher percentage of people who identified as American Indian or Alaska Native, and Native Hawaiian or Pacific Islander than in the Mecklenburg County population. 
The 2025 population of people in the Data Trust differs from the overall population of Mecklenburg County. People who identify as Black or African American or multiracial are overrepresented in the Data Trust and people who identify as White are underreprsented(Data Trust n=397,572; County n=1,206,285).

  • Geography=Somewhat representative: People in the Data Trust represented  nearly every neighborhood in the county, with the lowest numbers in the south and southeastern parts of Mecklenburg. Generally, people lived in areas that have higher levels of population density. When we look at median household income, people who were in the Data Trust were underrepresented in neighborhoods with the highest levels of household income and overrepresented in neighborhoods with lower levels of household income. However, there wasn’t  a clear divide between  rich and poor. There were also concentrations of people in the Data Trust who lived in upper-middle class neighborhoods where incomes were similar to the median income of Mecklenburg County as a whole ($73,214 in 2021,U.S. Census Bureau. American Community Survey 5-Year Estimates, 2017-2021).

People in the Data Trust are represented in nearly every NPA, with the highest numbers in neighborhoods along Monroe Road/Wendover Road, uptown, and along W. Sugar Creek Road/I-85/N.Tryon Street (n=361,078)

Understanding overrepresentation and underrepresentation

There are a number of possible reasons for overrepresentation and underrepresentation in Data Trust data. The data may omit people who do not meet eligibility requirements for programs and services such as eligibility for programs based on immigration status or because their income is above the eligibility threshold. This could lead to underrepresentation of Hispanic or Latino people in the data despite the size and growth of the group in the broader county population. More than a third of  people who identify as Hispanic or Latino in Mecklenburg County were  under 18 years old, which might  have also influenced their underrepresentation in datasets that only included adults (U.S. Census American Community Survey 1-Year Estimates, 2024). Additionally, eligibility criteria influences the income levels of the people represented in the Data Trust, since many of the data partners have income eligibility requirements. 

While eligibility criteria and age distribution might account for the above underrepresentation, explanations for disproportionate representation in administrative data are more complex and often indirect. For example, historic policies and practices like redlining and subsidized housing support restricted access to housing and homeownership for Black individuals and other people of color, which has been linked to less wealth generation for families of color.1 These early and subsequent policies have contributed to the wealth gap between Black and White households impacting who has household or network wealth to prevent or delay use of public safety net programs.2 This can partly explain why people who identify as Black or African American, who have less access to wealth that can prevent safety net use, are overrepresented in the Data Trust data.3 

It is also important to consider who and what is missing. This year, disability status/type, veteran status and country of origin were not included because comparatively few partners collected the information. Since many of our data partners are serving people who are in some form of economic crisis, or a crisis that’s closely associated with households with lower incomes (homelessness for instance), the Data Trust is missing data on people with higher incomes, who are not likely to experience some of the same hardships. Without this data, it’s difficult to understand the intricate pathways and factors that influence economic mobility.

As we continue to grow our data partnerships, we will assess who is over- and under-represented  in the data. We use information from the State of Our Data Report to inform representation on the Charlotte Regional Data Trust Board of Directors and its committees,  and to guide new data partnerships.

For example: Where are there opportunities with data partners who may represent income levels that are more representative of the county, particularly those with higher incomes? What are the data partners that strengthen our ability to understand who is thriving, who is left behind and why?

Our data are always deidentified for use

The insights made possible through integrated administrative data are gained from deidentified data. Our data governance model requires that any information that could identify a person (name, address or birthdate, for example) be removed from datasets before they are used for any purpose, and all proposed uses must be reviewed and approved by a board committee of data partner representatives. You may be in the Data Trust, but your information is carefully protected and identifying information is never available to researchers.

Connect with the Data Trust

Interested in learning more about the Data Trust or how your organization can participate? We are always seeking new partnerships.  Find the data we have available here or email us at datatrust@charlotte.edu to learn more.

References

1 Scott, R. H. (2024). The Evolution of Redlining in the United States Housing Market. Journal of Economic Issues, 58(2), 588–597. https://doi.org/10.1080/00213624.2024.2344442

2 Dettling, Lisa J.W., Jacobs, L., Moore, K.B., & Thompson J.P. (2017) Recent trends in wealth-holding by race and ethnicity: Evidence from the Survey of Consumer Finances. FEDS Notes. Washington: Board of Governors of the Federal Reserve System. Doi: 10.17016/2380-7172.2083

3 Rodems, R., & Pfeffer, F. T. (2021). Avoiding material hardship: The buffer function of wealth. Journal of European Social Policy, 31(5), 517-532. https://doi.org/10.1177/09589287211059043