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Big Data and Predictive Analytics in Public Sector


This paper discusses different ways in which big data and predictive analytics can be leveraged by public administrators and elected officials to improve the efficiency and effectiveness of the government. In particular, it is explained how the government can utilize automated decisions to serve its residents better and ensure the transparency of its operations. Also, challenges associated with personal data privacy and security, as well as gender and race bias, are addressed.

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The dynamic nature of big data, coupled with its large-scale availability, is changing society significantly. Under pressure from big data trends, analytics modernization has been proceeding in diverse areas where new types of data and data storage techniques are being incorporated. In the government sector, however, history is riddled with IT innovations that cause more trouble than they are worth (Kim et al. 78).

Nevertheless, public administrators and elected officials should make use of predictive analytics to improve organizational efficiency and better serve their residents. The purpose of the given paper is to discuss several beneficial ways in which big data can be used by governments, as well as focus on such challenges as personal data privacy and security.

Benefits of Big Data and Predictive Analytics

The utilization of predictive analytics can make public service delivery smart, responsive, and people-oriented. For example, text analytics and machine learning algorithms can automate multilevel checks on insurance, bank information, and certificates of origin. This will enable enhanced detection of fraud and money laundering. In healthcare, big data can help authorities improve performance and results in hospital management operations, as well as get real-time feedback (Kim et al. 80). A good example is HealthMap, a platform that automatically tracks data and creates a visualization of disease trends (World Bank Group 4).

As to public utilities, remote sensing data from satellites can be used to monitor the provision of water and electricity. It is worth mentioning that police forces may find it beneficial to rely on predictive analytics to make better policing decisions.

It can be stated that by using big data, public administrators may reduce the cost of services. This may be explained by the reduction in operating expenses which is attributable to the conduction of correct analysis or finding the hidden pattern. However, one could note that software for analytics modernization does not come cheap. Nevertheless, the entire system is cost-effective, and the benefits it yields may cover initial costs in the long run.

Governments may use predictive analytics to become more fiscally accountable to taxpayers so that people know how their money is spent. This will also allow for uncovering cases of improper payment, saving taxpayers’ money, and potentially increasing the quality of services (Bass 13). However, it is important to ensure that big data is used in conjunction with transparency to achieve the best results. In such a case, residents can even learn how taxes they pay impact their neighborhood.

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Challenges of Big Data

However, the growing ubiquity of big data algorithms raises several concerns related to personal data privacy and security, as well as race and gender bias. For example, a data breach at credit rating agency Equifax involved over 150 million customers (Olhede and Wolfe 9).

Possible solutions to ensure personal data privacy and security include the implementation of data protection regulations and the development of standards to guide data governance and security. To avoid bias in automated decisions, risk assessment tools, in particular, decision aids and random forest algorithms, may be used (Olhede and Wolfe 13). However, in some cases, it may be impossible to avoid bias as some rates often differ by race (Chouldechova 153). Nevertheless, a careful analysis of the scenario is always required.

Works Cited

Bass, Garry. “Big Data and Government Accountability: An Agenda for the Future.” A Journal of Law and Policy for the Information Society, vol. 11, no. 1, 2015, pp. 13–48.

Chouldechova, Alexandra. “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments.” Big Data, vol. 5, no. 2, 2017, pp. 153–163.

Kim, Gang-Hoon, et al. “Big-Data Applications in the Government Sector.” Communications of the ACM, vol. 57, no. 3, 2014, pp. 78–85.

Olhede, Sofia, and Patrick Wolfe. “The Growing Ubiquity of Algorithms in Society: Implications, Impacts and Innovations.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 376, no. 2128, 2018, pp. 1–16.

World Bank Group. “Big Data in Action for Government: Big Data Innovation in Public Services, Policy and Engagement.World Bank, 2017. Web.

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