With the sun beating down on him, Ram Avtar Chaudhary fills his metal plate with cement and mortar, preparing to plaster the brick wall. Working under harsh environmental conditions with little to go by, Chaudhary and the other construction workers on the site are exploited by contractors. Even with several rules and regulations in place to ensure their well-being, the schemes rarely see the light of day.
Recently, we had the opportunity to work closely with field surveyors and researchers of Hazards Centre – a Delhi based NGO which assists community and labour organisations in identifying, understanding, and combating the “hazards” faced by various communities. The Centre does this through research, training, publication, communication, and assistance for campaigns.
Hazards Centre shared with us their research methodology and the challenges of manually working with data: collating a large number of data points – sometimes no less than a hundred – cleaning up the data and then filtering through the it for further analysis.
As a result, Sahbhagita was born – a mobile platform for data collection and analysis. Ideafarms and Hazards Centre worked together to understand and redesign their survey form for a mobile-friendly format. In the process, we standardised many data points and reduced the amount of text that needed to be typed by field workers. Manual data transfer was completely eliminated and at the back-end, the data was pre-processed with two levels of filtration. The filtered data was then exported into spreadsheets that the researchers could immediately use for further analysis and interpretation.
What we learnt
- creating a low tech solution to accommodate factors like low network connectivity and less space on devices
- working with the Devanagiri script
- getting direct knowledge of the intricacies and complexity of field research in the social sector
- Authenticated data for traceability
- Standardised data capture for easy analysis
- Offline data storage for environments with poor network
- Near-realtime data aggregation and analysis