A Capability Maturity Model for the Sustainable Development Goals Indicators

This research advocates for the achievement of the 2030 Agenda for Sustainable Development through interventions towards improving the capabilities of the entities within the national data ecosystem responsible for monitoring its progress and aims to strengthen the institutional capabilities of the entities responsible for informing the national progress in sustainable development through the introduction of Capability Maturity Models.

The world development agenda is currently prescribed by a set of 17 goals that aim to protect the planet and ensure peace and prosperity by the year 2030. These goals, known as the Sustainable Development Goals (SDGs) present challenging objectives that must balance the three pillars of sustainable development: social inclusion, economic development, and environmental sustainability. Achieving the SDGs also demands to cope with the data revolution for sustainable development: the integration of new and traditional data to produce high-quality information that is detailed, timely, and relevant for multiple purposes and to a variety of users.

SDG indicators are a means for countries to monitor and report on their progress towards SDGs goals and targets. The data for the SDG indicators need to be primarily based on data produced by national statistical systems – usually embodied by the National Statistical Offices (NSOs) – and needs to maximize international comparability and time trends consistency with the data produced at international level. The monitoring of the indicators for the SDGs requires an unprecedented amount of data to be generated and analyzed, which poses a significant challenge for national statistical systems in developing and developed countries. This data should be protected and improved by strengthening NSOs, and ensuring they are functionally autonomous and independent of political influence.

Measuring sustainable development enables data-driven decision making, which is critical for the development of implementation strategies and the proper allocation of resources. While numerous efforts have been made to improve the quality of social statistics, two main aspects remain overlooked. First, most of the efforts focus on the quality of the data produced, disregarding how such data was produced; second, there are no solutions explicitly designed to deal with the data required for the monitoring of the SDGs.

This research advocates for the achievement of the SDGs through interventions towards improving the capabilities of the entities within the national data ecosystem responsible for monitoring its progress. By postulating that the more mature the organizations within the ecosystems are, the higher the quality of data that they produce, we motivate for the adoption and mainstreaming of organizational Capability Maturity Models within their SGDs activities.

To understand how the capability of the entities responsible for monitoring and reporting the progress on the SDGs can be improved, this research aims to answer the following questions:

  1. How can the quality of the data generated to inform the status and progress towards the achievement of the SDGs be assured?
  2. How can the quality of the data produced by the NSOs be determined and improved?
  3. How can the UN member states be supported in fulfilling the beliefs and principles of the SDGs?

To strengthen the NSOs to safeguard the quality of the data they produced for the SDG indicators, the following are the specific objectives:

  1. Survey and compare current practices for the production of statistics to feed the SDGs’ indicator framework, including the existing MDG monitoring architectures and other reporting mechanisms.
  2. Design a multidimensional prescriptive Capability Maturity Model suitable to assess and enhance the maturity of the organizations responsible for producing data for the SDGs at the national level.
  3. Validate the proposed model utilizing different validation techniques.
  4. Promote and support the model implementation and utilization.

This research follows a Pragmatic philosophy and takes on the Design Science (DS) approach over a Longitudinal time horizon. A three-cycle view of DS research is adopted, where DS is understood as three closely related cycles of activities: relevance – where the contextual environment is bridged with the DS activities, design – where the artifacts are built and evaluated, and rigor – where the DS activities are connected with the knowledge base of scientific foundations, experience, and expertise that informs the project.


Sustainable Development Goals, Indicators Monitoring, Capability Maturity Model, Institutional Capacity

Ignacio Marcovechio


This project is part of the Small Data Lab.
Share this

Send this to friend