Big Data Abuse and Challenges
By Anastasiia Tokar

Big Data Abuse and Challenges


Governments that are striving to digitize their operations experience the benefits of IT solutions to collect, store, and analyze data to proceed with decision making and transform the process to become reliable, evidence-based and controlled. However, sooner or later, the officials encounter the temptation to abuse big data. As more states follow the trend of transferring towards digital governance, trying to improve their digital government practices, it is essential to revise policies and practices that follow the use of information. Authorities must do so for the sake of preventing data abuse by striking a balance between the benefits of information provision and the need for its legitimate employment.​

Big Data

There is a growing number of countries around the world that engage with the implementation of digital government tools[1]. Australia, Canada, the United Kingdom, Mexico, the United States, and many others among them have developed multiple data collection, storing, and analysis instruments that help them effectively implement decisions concerning public programs, healthcare, or voting. Vast bulks of information are gathered from private entities, social media, hospital reports, and businesses to make the analysts see the big picture yet may complicate decision-making and model-utilization by including as many extraneous[2] and confounding[3] variables as possible. 

Large volumes of data that come structured and unstructured from all the mentioned above sources and become available to governments for analyses and decision-making create what we know to be the big data. However, it is not the amount of data that’s a primary concern — at least not for the decision-makers who implement rulings. It is most remarkable how data is collected and what governments do with it

Big Data Utilization

Big data is flexible, and its beholders can apply it in different sectors. Governments can use it to have better control over resources and streamline their effects on the healthcare or agriculture systems. Authorities can also improve public transportation systems by allocating assets and funds where transportation is insufficient and flow is disrupted, or provide access to education and enhance its quality. Moreover, big data may provide insights on poverty eradication by disclosing need and wealth gaps over vast territories, help prevent cyber attacks, or improve security maintenance by enhancing emergency response, implementing anti-money laundering activitiestracking insider threats, and amplifying workforce effectiveness

While massive amounts of personal data may improve the government’s ability to effectively fulfill its responsibilities and protect the citizenry, data availability can pose a great temptation. With the expansion of data, the room for improving the policies surrounding the collection, storing, analyses, and use of data also grows, stressing the need for appropriate protection and effective utilization.

Big Data Protection and Misuse

Following the creation of General Data Protection Ruling (GDPR) in 2016, the U.S., Brasil, Thailand, India, South Korea — all joined the global movement for robust data protection regulations. Besides, the Countries that form the Organization for Economic Co-operation and Development (OECD) have succeeded in creating comprehensive digital government standards and principles, guides, and requirements that provide decision-makers with a framework to transform their institutions and embrace digital governance. Adoption of these gave rise to the creation of internationally praised Principles for Digital Development and the OECD’s very own General Digital Service Design Principles.

However, the misuse of data and its unlawful collection remain a pressing issue until today, not only making the predictive models[4] overfitted[5] with unnecessary data but also creating instances of human rights violations. For example, women in Saudi Arabia pay a disproportionately high price for “committing sexual relations outside marriage, including adultery, extramarital and homosexual sex” as a result of intrusion in their private lives and private information that is poorly protected. Numerous violations of women’s rights — with the use of data gathering tools and due to inadequately guarded data — were also recorded in the U.S. Data can be used to track and “out” individuals within the LGBTQ communities and give rise to prosecution and discrimination. At the same time, it is also used to suppress minorities by using torture to maintain control of their citizens, target journalists, and citizens that oppose regimes.

Concluding Remarks

Indeed, big data has great potential to help locate concerning trends, predict future events, give rise to evidence-based decisions, and improve resource performance and allocation. These and many other benefits can prove to be undeniably crucial in improving regulatory practices, enhancing institutional capacity, and transforming the work of the institutions for better. However, the temptation to misuse data is present. The threat of information abuse should not prevent governments from continuing to enhance their performance and improve decision-making practices. However, institutions that monitor governments and political elites for instances of power abuse cannot underestimate the potential that big data has and the power it grants its beholders. Hence, data collection needs to be regulated in a way so that only relevant information is collected, which is then used for the benefit rather than harm.


[1] Tools that involve information and communication technologies.

[2] Extraneous variables are any variables that are not intentionally studied within a research question.

[3]A confounder is a variable that influences both the cause and effect, creating spurious associations. 

[4] Predictive are the models that use data to forecast outcomes using available information.

[5] Overfitting is “the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably” (Oxford Dictionary).