The Sustainable Development Goals (SDGs), signed by 193 countries, have committed the world to an ambitious objective: to leave no one behind. In order to do so, the goals state that the measures to monitor progress should be broken up not only by gender and age but also by specific population groups, including those who are economically disadvantaged, people with disabilities and ethnic minorities.

So, how does data help us ensure we leave no one behind?

Measuring inequality is a complex issue and a lot of work is being undertaken to understand the nature, scope and trends of the problem, as well as how different inequalities – say, poverty and gender – combine to make disadvantage worse. Of course, this understanding is highly dependent upon context, as social norms or standards can influence outcomes tremendously. So how can we measure inequality in pursuit of the commitments in the Sustainable Development Goals when contexts differ from one country to another?

The challenge of monitoring global progress is now up to the inter-agency and expert group on SDG Indicators, which is meeting in Bahrain this week to discuss measurement issues, including data disaggregation – the breaking down of large-scale datasets by factors such as gender and age. Thus far, the group’s progress in defining the agenda has been slow, so there is much at stake.

Because inequality is very much linked to specific contexts that differ across and within countries, more unstructured approaches such as qualitative research are an effective way to explore these issues, but they do not always lend themselves very well to monitoring frameworks such as the SDGs that aim to look across entire populations. Although they may not be generalizable at the country level, in essence they’re trying to capture what some of the more standardized survey approaches to data and monitoring may miss.

Another effective approach is to look at the absence and/or presence of a specific country policy to address inequalities as an indicator of a government’s commitment to an issue. It’s not a perfect approach, as it may not always be as reliable in measuring the policy’s implementation (which is harder to do) as a means to test if the state’s actions reflect its commitments, but it’s still a considerable option given that it may still take years before many of the “official” indicators are widely collected and available.

This debate has raised many questions about how best to measure progress – and some have proposed moving away from comparing national averages toward a greater focus on whether living conditions and opportunities have changed for the most disadvantaged (that is, the lowest wealth quintile). An approach like this would be something quite revolutionary for a global development framework, and some sectors have managed to make it work.

Those monitoring certain goals – say, poverty (SDG 1), nutrition (SDG 2) or health (SDG 3) – have been at this a long time. And it shows, as health inequality measures, for example, have been available for years.

But those measuring progress on other goals and thematic areas, such as education, have had far less use of different data sources to understand and measure inequality.

This is not to say household surveys and other datasets that aim to capture individual differences don’t also face serious challenges. These sources can also miss the disadvantaged populations that we want to learn more about and can cause tension between the competing desire to improve data for monitoring the SDGs and to invest in the technical capacity to achieve them.

The good news is that there are new solutions for measuring inequalities, starting with improved baseline data from national population censuses, where as part of the 2020 round there is an opportunity for constituencies to call for improvements through consultations held by national statistical offices.

The UNESCO Institute for Statistics has advanced a new adjusted gender parity index, which could have applications across many other SDG indicators.

For the first time, UNICEF Multiple Indicator Cluster Surveys, which provide internationally comparable data on the situation of women and children worldwide, will engage children beyond the health assessments. A new module for basic skills in reading is being tested for the next wave of the survey from 2020, covering most developing countries. Similarly, the surveys have collected data on the “functioning” of persons with disabilities for the first time.

All of these changes will lead to a more comprehensive understanding of the lives of women and children. While these are positive steps, it is essential that the momentum does not subside, and that addressing inequalities remains high on the political agenda.

The data we collect is instrumental in our ability to influence policy change that makes a positive difference to the lives of women and children everywhere, and to help us ultimately meet our goal of leaving no one behind.

The views expressed in this article belong to the author and do not necessarily reflect the editorial policy of News Deeply.