The need for quality, accessible, timely and reliable disaggregated data has emerged as one of the strong messages as UN Member States were preparing to adopt the 2030 Agenda in September 2015. A global indicator framework was developed by the Inter-Agency Expert Group (IAEG) on SDG indicators and adopted by UN Member States in 2016, among other, as an evolving basis for countries in their efforts to compile national indicators for tracking their progress on the SDGs. The progress in turn is expected to be reflected in country reports, in addition to the global SDG progress report produced each year drawing on national official data sources.

Beyond the objectives of stronger accountability and reporting, timely granular data were particularly called for with a view to providing evidence for decision-making jointly with evaluation findings. More specifically, better data had to help identify those populations who are the furthest behind – poor, vulnerable and with no access to essential services - and spot critical policy linkages between different goal areas.

Yet one of the common challenges discussed at length in a recent report “Avoiding Data Graveyards” by AidData, a research lab committed to open data on development finance and assistance, is the underuse of the produced data specifically for policy-making purposes. Drawing on an extensive desk research and interviews with more than 200 high and mid-level officials from 3 countries, their study shows that three major reasons prevent policy-makers from using data in applying corrective course actions. These include the lack of more disaggregated data, including local-level data, and the facts that data are often outdated and inaccurate, data sources are not integrated and data are collected using non-standardised methods. The study has further showed that even where data is available, it is not always accessible, and when it is accessible, there is low awareness about how to access public sources of information.

Upgrading current results-based management systems and tools can help respond to some of the above challenges and reflect the complexity embodied in the SDGs. The consistent application of the upgraded tools at project/programme, sector and national policies levels can help strengthen local and national data ecosystems and contribute to a more effective use of data for decision-making purposes. One of such tools, UN-DESA-designed RBM 2.0, for example, can help promote a new approach in this area moving from linear, siloed results chains to non-linear 3D models where planning is done taking into consideration relevant outputs under the main goal and other goal areas and where implementation considers impacts in a given and other affected sectors. Furthermore, it can help streamline data production and use at project, sector and national levels using a nested, cascading model. The iSDG model developed by the Millennium Institute can strengthen the integrative approach to monitoring and data collection by helping to reveal some of the critical linkages between different policies. Other ways to strengthen the production and use of relevant data include integrated national data systems, standardized methods and formats, publicly available and easily accessible data and leveraging of new data sources, including geospatial and citizen-generated data.