How Analytics is used in e-governance

The role-playing of analytics is fast changing from a descriptive to a predictive and prescriptive mode, with application scenarios extending across domains one never imagined before.

With the Indian Government advocating the ‘digital governance’ mode, vendors like SAP are moving in on the e-governance platform across the country, implementing solutions to automate and centralise public service offerings. Its three new ‘made in India’ apps –  Rakshak, TracOHealth and The Milk Co-operative Experience – “built using predictive capabilities to track criminal activities, improve law and order and enhance other public utility services”.  This is just a small instance of how the convergence of cloud, mobile, social and big data can move beyond automation of e-Governance systems to leverage the power of analytics in real-time dashboards and decision-making process in governance.

The e-Governance system

To fathom the rationale of the e-Governance model, one needs to understand the ideology behind it. E-governance aims to make processes and the system transparent, to facilitate identification of pilferage and seats of corruption as well as to discover areas of saving cost and time. It makes use of interactive website, social media engagement, dedicated IT systems and disruptive technologies leveraging GIS technologies and Big Data analytics.

Government agencies maintain voluminous data in disparate systems and databases. Analytics helps to leverage this vast information about its citizens, projects and activities to effectively utilise resources for efficiency in public services, transparency in governance and improve operations.
 

Application scenarios in e-Governance

Tax Administration – Analytics helps provide more precise and actionable insights on cases, to help tax collectors reclaim more money at the same resource levels. Predictive models are able to empirically analyse available data, establishing weights based on proven relationship to assign each case a score, for prioritization of follow-up; and even suggestion of collectors who are best-fit to handle the case. Optimisation algorithms suggest the most successful action. Decision models also help to map the relationship between actions, responses and results to enable the Government Department to put into place the necessary outcome, set real-life limits on number of collectors, costs or time.

Subsidy programmes – The failure of Subsidy outreach to the poor and rightful individuals can be tackled with the power of analytics. A mirror approach to retail analytics with smartcards focused on the fundamental needs of water and basic rations have been employed successfully in many countries. Additionally analytics also helps detection of fraud in service deliveries, the biggest pit in the Subsidy system in India.

Law and Order – Predictive models identify ‘hot spots’ of crime based on the historical crime data of time and location and descriptive analytics. Trends in community behaviour with respect to crime or provocative activities can also be modeled using analytics.

Public Safety– As data collected by Government agencies increase, a Big Data analytics approach that combines the SMAC (social, media, analytics, cloud) and external information helps detect patterns that pose a threat to public safety or affect the access to water and power outage. Security analysts are able to detect patterns in civic violation, or malfunctioning access points of utilities to relay to keep field officials informed.

Transportation – Predictive analytics when applied to transportation supports urban planning by predicting traffic patterns and congestion spots in real-time.

Healthcare – Predictive analytics models can also help quantify links between climate variability and disease outbreaks.This is especially relevant in our country where monsoons and floods bring on water borne diseases like jaundice and typhoid that are usually confined to geographic and other variables like low income levels.

Land use – Analytics assimilate historical and real-time data related to population demographics, environmental issues, citizen grievances, crime patterns, flooding, etc., to identify needs of establishing more amenities like hospitals, citizen grievance cells, police outposts, pumping stations and so on. For instance, trends of flooding or land subsidence can be inferred to establish policies related to housing development permissions and urban expansions, which come under the purview of municipal governance and various agencies.

Smart cities – With the Government plans of establishing ‘smart cities’thefuture of analytics application in governance has taken on new dimensions. Analytics applications for effective policy implementation towards building a ‘smart-city climate resilient model’, can build an increased resilience of infrastructure, business sectors, and citizens against extreme weather events; where citizen services and national security are improved manifold.

Bottomline – The role of analytics is all-pervasive and powerful especially when applied to e-Governance, as it helps map a future of optimal citizen services and national security.

 

Reference: Nandan T, Gopi Chand, M., Application of Analytics in E-Governance-a next level

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