Exploring Big Data for Development: An Electricity Sector Case Study from India

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Abstract

This paper presents exploratory research into “data-intensive development” that seeks to inductively identify issues and conceptual frameworks of relevance to big data in developing countries. It presents a case study of big data innovations in “Stelcorp”; a state electricity corporation in India. In an attempt to address losses in electricity distribution, Stelcorp has introduced new digital meters throughout the distribution network to capture big data, and organisation-wide information systems that store and process and disseminate big data.

Emergent issues are identified across three domains: implementation, value and outcome. Implementation of big data has worked relatively well but technical and human challenges remain. The advent of big data has enabled some – albeit constrained – value addition in all areas of organisational operation: customer billing, fault and loss detection, performance measurement, and planning. Yet US$ tens of millions of investment in big data has brought no aggregate improvement in distribution losses or revenue collection. This can be explained by the wider outcome, with big data faltering in the face of external politics; in this case the electoral politics of electrification. Alongside this reproduction of power, the paper also reflects on the way in which big data has enabled shifts in the locus of power: from public to private sector; from labour to management; and from lower to higher levels of management.

A number of conceptual frameworks emerge as having analytical power in studying big data and global development. The information value chain model helps track both implementation and value-creation of big data projects. The design-reality gap model can be used to analyse the nature and extent of barriers facing big data projects in developing countries. And models of power – resource dependency, epistemic models, and wider frameworks – are all shown as helping understand the politics of big data.

Cross-posted from University of Manchester.