AI-Based Big Data Algorithms and Machine Learning Techniques for Managing Data in E-Governance

Introduction

E-governance has become more and more important as customers have become familiar with the idea of their data obligations. It consists of a number of measures, standards, guidelines, and procedures that enable businesses to use the data properly and responsibly. Their usage of data must be productive and efficient in pursuing their goals. A comprehensive business intelligence and information analytics solution called a big data analytics framework uses both traditional and big data. Data is gathered by the government's analytical engine from a variety of sources, including the world wide web, social networks, devices, machinery logs, and other datasets maintained by various government departments. This causes the amount of transaction data generated by e-government to expand dramatically, increasing the need for big data analytics. They are transformed and presented in an analyzer-friendly way by this analytical engine. In this sense, e-governance includes duties and procedures for ensuring that data utilised by an organisation is secure and of high quality. Transactional data is increasing dramatically as a result of global and Indian e-Governance initiatives, making it challenging to examine such a massive number of data housed across numerous sites using conventional data mining methods. The government will be able to make better judgments thanks to it, and the public will receive timely and accurate information. In terms of efficient service delivery, result rather than output determines whether an e-Government project succeeds. The vast majority of the states and governments in India are concentrating on harnessing emerging technology to provide citizen services, improve the effectiveness of how the government functions, and find new methods to raise money. “Big Data Analytics” is a significant revolutionary innovation that affects both corporations and the governments. An integrative business intelligence and information analytics solution called a big data analytics software uses both traditional and big data. Data is gathered by the government's aeronautical engineer from a variety of sources, including the web, social networks, detectors, machinery records, and other datasets maintained by various government agencies. This causes the rate of transaction data generated by e-government to expand dramatically, increasing the need for big data analytics. They are transformed and presented in an analyzer-friendly way by this difference engine. Market Research reveals that 75% of government agencies are using big data to enhance the standard of living for individuals. Big Data Analytics offers insights into the effectiveness of administration practises and initiatives. In order to gauge public sentiment and comprehend citizens' thoughts and attitudes about government programmes, prescriptive and anticipatory analysis suggests the optimal course of action. Structured and unstructured data are the two types of big data. Structured data provides information that is typically kept in manual processes or enterprise applications, such as real estate taxes, construction permits, criminal backgrounds, and car records. Social networking, emails, movies, photos, and other types of unstructured data are examples.

Through citizen-government integrations, AI has the potential to improve e-governance assistance. For instance, the government of Andhra Pradesh in India collaborated with Microsoft to develop Kaizala, an AI-assisted app. Kaizala served as a citizen-government interaction, directing citizen questions to the proper government organization. The app may also be utilized by the government to provide computer-controlled notifications to its inhabitants. As a result of these AI-assisted interfaces, authorities can better regulate their citizens.