AI/ML Advanced Analytics
Many nonprofits operate with constrained budgets, limiting their ability to invest in advanced data systems, technologies, or specialised talent. As a result, they often rely on self-reported or external data sources, which can be inconsistent or incomplete. Integrating data from various platforms such as surveys, CRM systems, and field reports can also be technically challenging. We help social impact organisations unlock the full potential of their data, combining internal and publicly available sources to assess program success and scalability. Our support to clients include descriptive analytics to identify trends and performance patterns, prescriptive analytics to guide strategic decisions, predictive analytics to forecast future outcomes, and scale for impact using tools like Social Return on Investment (SROI), cost-effectiveness analysis, and Results-Based Management (RBM). This comprehensive approach enables organisations to make informed, data-driven decisions and maximise their impact.
Projects
Innovations to Improve and Institutionalise Data Quality and Analytics
India’s public health system generates a vast amount of data through various programs, registries, and surveillance systems. However, significant challenges persist in ensuring the quality, completeness, and timeliness of this data, which hinders effective policy-making and program implementation.
The project aimed to strengthen the data ecosystem by promoting greater awareness of data quality among both data producers and users, while also supporting the institutionalisation of quality assurance processes in surveys and administrative data systems. In partnership with the National Data Quality Forum (NDQF), PopulationCouncil Consulting collaborated with IIIT-Delhi to develop a domain-agnostic data quality assessment tool using machine learning techniques.
The tool accepts a query dataset along with its codebook and generates a composite data quality score based on factors such as provenance, metadata alignment, anomalous patterns, and statistical properties. The underlying model was trained on metadata and datasets from over 250 publicly available sources and validated using multiple rounds of the National Family Health Survey (NFHS). The tool is publicly accessible and designed to support robust, scalable data quality evaluations across diverse sectors.
MacArthur project on Predictive Analysis and Modelling
Climate change is deepening global inequities and weakening resilience, while human migration is increasingly emerging as a vital adaptation strategy. Yet, its drivers, patterns, and implications—particularly around livelihoods, urban transitions, and just energy shifts—remain poorly understood. This multidimensional initiative addresses these gaps by examining climate-induced migration through an integrated lens. It brings together an expert advisory group, a big data-driven knowledge portal, and an evidence-based framework to guide future learning. By fostering cross-sectoral dialogue, the project aims to co-create inclusive, sustainable, and impact-oriented migration strategies.
The project engages AI for big data analysis – massive datasets, including census migration tables (2001, 2011), climate data spanning 60 years using Arc GIS models, and transaction patterns from the One Nation One Ration Card (ONORC) initiative. This approach results in detecting hidden patterns, optimise data integration, and enhance prediction accuracy. Along with this, machine learning algorithms helped in producing advanced predictive modelling, specifically fixed effects models enhanced to predict in- and out-migration rates for 2021.