The next set of development goals should be underpinned by plans to measure progress and results — and to boost data infrastructures.
The countdown to the post-Millennium Development Goals (MDGs) agenda is a time for debate about how the development community and countries around the world should collaborate on improving wellbeing, sustainability and social justice from 2015.
Look beyond the proposals and the wrangling over priorities, imagine a new development framework in place and fast-forward two decades: how many questions will we be able to answer about what has and hasn't worked?
The challenge is bound to be greater for the next set of goals, which, as currently conceived, will draw on a broader set of development objectives compared with the MDGs' human development focus.The value of monitoring and evaluation (M&E) is clear when we reflect on progress made in the past decade or so: with the benefit of hindsight, answering questions about the MDGs' impact has been far from straightforward.
Although scientists and political leaders are right to target what needs to be achieved first, it is not too soon to consider how progress might be measured. The scientific method can be a guide, and proposals by stakeholders eager to shape the new goals a good place to start.
In recent weeks, discussions about the future course of global development have been gathering steam, led by the UN High-Level Panel of Eminent Persons on the Post-2015 Development Agenda and running alongside a separate process to develop the Sustainable Development Goals (SDGs) that was launched at the Rio+20 summit last June.
Issues around measuring progress and results have not been ignored: this list includes several proposals on how targets and indicators might be developed, with many of them focusing on inequality and wellbeing. In addition, a 2012 report by the UN System Task Team on the Post-2015 UN Development Agenda outlines ways to develop goals, targets and indicators for addressing inequalities. 
The report also highlights the need to invest in disaggregated data collection and analysis, and to develop methods — using new technologies and social media — that complement household surveys by monitoring at the community level.
But before building on existing methods, and while considering targets and indicators, it is vital to address obstacles at the root of countries' capacity to monitor progress and results — the infrastructure to collect, analyse and use data for decision-making.
Critics of the MDGs have argued that a lack of data, difficulties in obtaining valid measurements and the use of national averages that obscure inequalities within countries and populations have made it difficult to determine how much progress has been made — particularly in poorer parts of the world. These are drawbacks also acknowledged by MDGs advisor Jeffrey Sachs. 
For example, nearly half of Sub-Saharan African nations have not carried out any household surveys for ten years, according to Caroline Anstey, managing director of the World Bank. 
And last month, health experts speaking at the launch of the Global Burden of Disease Study 2010 in London said the lack of information infrastructure in the developing world means little is known about even a basic measure such as causes of child death.
The case of health-related MDGs shows that evaluating results can be difficult even where there is a strong tradition of empirical analysis. Public health policies and interventions tend to focus on physical and biological causes of disease and, although environmental and social components of wellbeing are increasingly taken into account, linking health with wider development concerns is still something of a struggle.
This highlights two realities. First, if measuring progress was a shortcoming of the MDGs, monitoring progress in the potentially wider-focused SDGs is likely to be much more challenging.
Second, if the new global goals are to address underlying components of poverty, and include environmental and socioeconomic dimensions of wellbeing, measurement of their progress will require data on hard-to-quantify factors, including governance, to be captured.
Though a broader approach to development is a step in the right direction, it should be implemented alongside a practical M&E strategy that includes investing in data infrastructure where needed.
A report released this week by the UK parliament's International Development Committee calls for improvements in how progress is measured while maintaining the "simplicity and measurability" of the MDGs.  Peter Holmgren, director-general of the Center for International Forestry Research, has also argued for keeping goals simple — and affordable. 
Calls for simplicity may not find support from those who advocate a broader approach to development. But regardless of how much complexity ends up making its way into the process, M&E should be a central feature of goal setting.
A focus on evidence need not mean technocratic solutions. But it can encourage accountability, offer guidance and enable evidence-based decisions, bridging the gap between the big-picture questions that need to be asked and the realities of how, and whether, they can be answered.
The scientific method can be a guide here — with clarity about what is expected of development goals followed by how to measure progress and results, the information required to do this and the analysis needed to draw conclusions. This process should also make provisions for tracking development outcomes that are not easily captured by data, or where data are not readily available.
The task might be easier if proposals for issues to be tackled in the next generation of development goals come with a discussion of not just their significance but also the methods, tools and resources needed to address them.