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Using Big Data and Predictive Analytics to Modernize Government Operations


At the beginning of the 21st century, the development and global spread of Internet communication technologies in modern states led to the emergence of an independent virtual political communication space with its principles of functioning. The introduction of new types of political communication influences the processes of interaction between the state and society, requiring the state authorities to develop fundamentally new approaches to information and communication activities. According to this, at the moment, the issue of efficiency of modern analytical practices, such as big data and predictive analytics, is relevant.

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Application of Big Data in The State Apparatus

In general, big data means data that is difficult to process for users because of their large size and for work with which a unique toolkit is required. From the point of view of the State and society in general, the dissemination of big data means new opportunities, many of which are still unclear and have yet to be thoroughly explored. According to Ramirez et al., “appropriately employing big data algorithms on data of sufficient quality can provide numerous opportunities for improvements in society” (5). The use of big data provides excellent opportunities for official statistics agencies.

First, it can provide prompt access to a large amount of information on the economic and lifestyle of the population, their habits and consumer behavior, public sentiments, information requests. Second, the official statistical offices can take on the functions of developing standards of work with big data. Nowadays, the functions of collection and analysis of big data, as a rule, are concentrated in the same organization, which collects information within the framework of its principal activity. In these circumstances, official statisticians could act as advisers in the development of indicator methodology, drawing on their experience in compiling demographic, economic, and social data.

The introduction of big data technology enables tax authorities to benefit more from existing data, combat tax evasion and fraud, and make life easier for taxpayers. The use of big data technologies increases the transparency of tax processes. It helps the tax authorities to understand the behavior of the population better and gives a vision where fraud and the shadow economy can develop. Thus, resources can be directed where they are precisely needed.

Modern Analytical Methods and The Prospect of Using Them to Modernize Government Operations

The role of expert and analytical support of management processes has increased significantly in the mechanism of public administration. It is the result of information-analytical activity that should be the basis for a meaningful strategic goal-setting and formation of long-term plans for the development of social facilities. There is a need to act in advance, to prevent threats to the sustainable development of the state. Predictive analytics is a useful modern tool for solving such problems.

Predictive analytics includes a variety of data analysis techniques, game theories, and statistics that analyze historical data or events to predict the future. Such forecasting can allow public administrators and elected officials to assess possible developments better and, as a consequence, reduce the risk of making ineffective or fundamentally erroneous decisions. Using predictive analytics, officials reduce the level of uncertainty and make decisions that are statistically more effective than those made without the use of predictive analysis.

Personal Data Privacy and Security in The Context of The State

A relevant problem is also the issue of anonymity and security of personal data. Its anonymization is one of the measures aimed at minimizing the risks of harm to citizens in case of leakage of their personal data from information systems. According to Zhang, if big data is not well protected, it will directly threaten the privacy of users and the security of their data (1). Personal data privacy performs an important social function, ensuring individual autonomy and inaccessibility to those who disagree with the opinions expressed by the person or are potentially hostile to the features that they have. In the context of the information society, it makes sense to pay attention to the possibility of the presence of race and gender bias in the algorithms performing data collection and organization. As Lee puts it, “to ensure that more of these biases do not become commonplace, it is important that policymakers and technologists agree upon principles and values for what a ‘bias-free’ zone within innovation should adhere to” (8). Algorithms do not sufficiently take into account the diversity of people who could use them, and therefore need to be improved.

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Big data provides new opportunities for analysis and processing of statistical information that were previously inaccessible to users. It is crucial for official statisticians not to ignore new tools, but rather to explore emerging opportunities for integrating new data sources and analysis tools into their work. Regarding anonymity, it should be noted that the challenges posed by big data technologies require an innovative approach to the interpretation and application of the basic principles of personal data legislation. In addition, the functioning of the tax authorities without the use of advanced information technologies is now virtually impossible. The use of new technologies will result in more optimized work directly with taxpayers, and the introduction of various software and information systems and technologies will increase the efficiency of the entire fiscal system.

Works Cited

Lee, Nicol Turner. “Detecting Racial Bias in Algorithms and Machine Learning.Journal of Information, Communication and Ethics in Society, vol. 16, no. 3, 2018, pp. 252-260, Web.

Ramirez, Edith, et al. “Big Data: A Tool for Inclusion or Exclusion?FTC, 2016, Web.

Zhang, Dongpo. “Big Data Security and Privacy Protection.” Advances in Computer Science Research, vol. 77, 2018, pp. 275-278, Web.

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