Many of our analyses compare an eligible population to that population served by an organization. For example, if we are interested in eligible minority households, we can find population information through American FactFinder on households in poverty by race/ethnicity. Then, we might query our internal data to find out how many minority households were served. We might compare these numbers across time, or within a single geography (such as a neighborhood). But the data aren’t always perfect.
For example, you might not be able to find exactly the external data you are looking for. Perhaps the census reports different income ranges (clouding eligibly questions) or has different race/ethnicity categories than your internal data. Conflicts like these are common.
So, what to do? First, remember that it would be unwise to use a single data analysis or data comparison to solely inform an important decision, even if the data were perfect! Rather, it is better to see these analyses as part of a complex picture. The better the data, the larger the part those data might play in that complex picture. But there will certainly be situations where one will discover useful information from imperfect data, and find nothing useful from near-perfect data.
It isn’t necessary to avoid using data when they aren’t exactly what you are looking for. It is necessary, however, to scale your conclusions (and enthusiasm) proportionally to your confidence in the input data – and that could apply both to internal and external input data. Keep in mind the context of these data analyses being but one component of a broader effort aimed at better understanding different dimensions of your organization.