Capability Maturity Models as a Means to Standardize Sustainable Development Goals Indicators Data Production
DOI:
https://doi.org/10.13052/jicts2245-800X.633Keywords:
Sustainable Development Goals, Capability Maturity Model, Data Revolution, Institutional CapacityAbstract
Achieving the Sustainable Development Goals (SDGs) demands effective harnessing of the ensuing data revolution – the integration of new and traditional data to produce high-quality indicators that are detailed, timely, and actionable for multiple purposes and to a variety of users. The quality of these indicators, defined in terms of completeness, uniqueness, timeliness, validity, accuracy, and consistency, is crucial for their use in national level planning, monitoring and evaluation (PM&E) processes, for facilitating global monitoring of progress on the SDGs, and for enabling comparative evaluation between countries. The use of indicators for trans-national analyses and global-level decision making necessitates coordination, integration, and interoperation between the various stakeholders within the global data ecosystem. Various instruments, including protocols, models, frameworks, specifications, and standards are used widely to facilitate the coordination, integration, and interoperation across various global systems, such as telecommunication systems. In this paper, we posit that Capability Maturity Models (CMMs) can be an instrument and a mechanism towards not only ensuring the production of high-quality indicators data, but also for standardizing the key processes around the production of SDG indicators data, and for facilitating interoperation within the data ecosystem. This paper motivates for the adoption and mainstreaming of organizational CMMs within the SDGs activities. It also presents the preliminary formulation of a multidimensional prescriptive CMM to assess and articulate pathways towards the maturity of organizations within national data ecosystems and, therefore, the effective monitoring of the progress on the SDG targets through the production of high-quality indicators data. Furthermore, the paper provides recommendations towards addressing the challenges within the increasingly data-driven domain of social indicators monitoring.
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