Government of Rajasthan selects Teradata big data solutions to improve citizen services & engagement

Rajasthan to create a common data and analytics platform for all government departments across the State to collate and utilize data more effectively and efficiently, improving the delivery of citizen services with the help of Teradata solutions.

The Government of Rajasthan, India is working to provide a big data analytics environment to analyse state level data in order to enhance citizen services and engagement as well as increase the efficiency of departmental cross functional operations. For this project, they have chosen to collaborate with Teradata.

The Rajasthan State government aims to move from department level silos to an integrated data and analytics environment using Teradata’s solutions. This will enable it to expand on its vision to bring greater transparency and efficiency, while also delivering citizen services to individuals across the state in a timely manner.

“By leveraging leading Big Data analytics solutions, the state government will be able to utilise technology to improve service delivery and drive greater citizen satisfaction, which is critical for its vision of enabling citizens’ growth and empowerment,” said Rajesh Shewani, Head of Technology and Solution Architecture, Teradata India.

This is a first-of-its-kind implementation for the Indian government, and is a great example for other state departments on how they can use integrated data and analytics to improve citizen services whilst also making substantial savings and reducing fraudulent or fake subsidy payments by identification of entitlement anomalies for below-poverty-line (BPL) beneficiaries.
The project will also enable a 360-degree citizens’ view by unifying multiple databases.

“Under the new ‘Big data environment’ project, we have developed solutions to hold integrated data being generated real time across the state to help better address analytical requirements that facilitate state-level decision-making,” added Arun Chauhan, Additional Director, Department of Information Technology and Communication (DoIT&C).

It will also be able to offer actionable intelligence to different departments in the Rajasthan Government to improve citizen services. Some of the key citizen services the project will address include:

• Determination of Eligibility of Beneficiaries: Using advanced analytical tools, to plan and track welfare schemes to ensure that benefits reach only eligible citizens; Identify deceased, invalid, and duplicate persons to eliminate duplicate benefit payments.

• Unified Citizen Data: Unify all citizen data to improve understanding of information available across the multiple departments thereby providing cross-domain analysis of state data.

• Easy Tracking of Beneficiaries Across Schemes: Helping to identify and eliminate duplicates as well as “ghost” beneficiaries by making use of ‘Bhamashah’ as the common identity for mapping residents with various schemes.

• Enhanced Citizen Engagement: Enabling citizens to interact with government departments in a more expressive manner in their preferred language and in their preferred mode.

• Fraud Detection: Establish a citizen’s financial status with tax data: Identify non-filers, short-filers, fraudulent activities, asset and income mismatches, 360-degree Dealer views, behavior analysis with peer groups, and identify anomalies.

• Sentiment Analytics: Ability to perform analysis of unstructured data in the grievance portal to determine the root cause of grievances from Raj Sampark, e-Mitra portals and helpdesks.

Based on a three-year agreement, Teradata will supply, install, commission and integrate solutions including Teradata “Aster” — the advanced analytics discovery platform — and “Hortonworks Data Platform” (Hadoop) at the state data centre site for real-time data analytics and insights.

Teradata will also set up a research platform, leveraging data science capabilities for pre-production analysis of “Hadoop” data where different analytical models can be created quickly and reliably from sample datasets which can then be applied on the entire data set.

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