Start-up’s software gives technological edge to panchayat

Rummaging through old, dog-eared files to identify beneficiaries of various welfare schemes is set to become history in Pampakkuda panchayat in the eastern suburbs of the district, thanks to a cloud computing-driven data analytical tool.

Governance is fast turning digital in this local body with e-gram — created to give technological edge to grama panchayats in their day-to-day affairs — being implemented. “We are in the process of getting the panchayat declared as the first fully digitalised panchayat in the entire country,” said Pampakkuda panchayat president Eby N. Elias.

The application developed by Nextuz, a young start-up operating out of Piravom-based Technolodge supported by the Kerala State Information Technology Infrastructure Ltd, gives the panchayat authorities real-time access to the data of around 10,000 residents spread over 5,000 households in Pampakkuda.

Six months ago, the panchayat conducted an extensive socio-economic survey based on a painstakingly created questionnaire. The data was then fed into the system based on which the software was created. The final product was a lot more than what the panchayat asked for. It threw up instant responses to even a complicated search combining multiple queries.

E-gram has been developed as a tool that stores and analyses all the information regarding people in a panchayat. It throws up real-time analysis based on wide range of parameters, including population, literacy rate, male-female ration, poverty threshold, internet penetration, access to electricity and clean water, healthcare and a lot more.

“Considering the high mobile penetration in our country, e-gram has a built-in functionality to send text messages and it supports regional languages. This could be used by panchayats in rolling out benefits, announcing, or even acknowledging receipts or certificates. The automated SMS service can also be used to remind citizens when their taxes are due,” said Alok Babu, CEO, Nextuz.

It can do much more. For instance, it is possible to get precision data on a query asking for list of females aged between 20 and 40, who are employed, are from a backward community and are postgraduates.

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