Oddly, sometimes when we talk about data, we end up ignoring the real world we’re so eager to measure. In the “data for development” crowd—and quite a crowd it is these days—we talk about the merits of indicators without thinking about whether the data exists to, you know, measure them—things like maternal mortality rates within small districts. We get excited about monitoring annual changes in birth and death rates, despite the fact that the surveys that yield the numerators are only done every five years and the census data that we depend on for denominators show up once a decade. We have an unlimited appetite for more and more granular information—down to the facility, down to the household, down to the smallest child in the most remote village—with little awareness of the potential costs of collecting, cleaning, and analyzing it.

Enter the practical voice of the original data scientists: demographers. From the time of John Graunt in the 1600s, demographers have developed efficient ways to collect information about population size and change. Equally importantly, they have figured out ways to assess the quality of that information, estimate the measurement errors, and compensate for incomplete and imprecise data. That’s not a sideline for demographers. It’s their job.

Demographers’ enthusiasm for applying their expertise to contemporary data challenges in development comes through loud and clear in a recent statement from a group of distinguished population scientists convened by the International Union for the Scientific Study of Population. In “Defining and successfully accomplishing the Data Revolution: The perspective of demographers,” they write:

The special skill of demographers lies in our ability to understand the systemic linkages between human population stocks and flows across space and time. This deep understanding puts demographers in a strong position to evaluate what is feasible and realistic with data collected on human populations, the limitations of those data, and the validity of the results. The assessment and evaluation of data quality, the capacity to link and process data from a multiplicity of disparate sources, and the ability to see the data as part of a larger systemic framework, are central aspects of this skill.

In addition to offering a technical word of warning to those planning a data revolution, the demographers provide scientifically-grounded suggestions for addressing indicator design, data quality, and interoperability. If you’re constructing indicators that will actually be useful, or investing in new data collection, or trying to figure out how to triangulate information from multiple sources, you want demographers on your team. Luckily, with this statement they’re saying that they are now ready, willing, and able to enlist as data revolutionaries.