The importance of Big Data Analytics in public policies

Mendes, Beatriz Vieira Santello; Capucho, Pablo Henrique Paschoal

doi:10.22167/2675-6528-2026M03

Article

June 11, 2026

The importance of Big Data Analytics in public policies

Beatriz Vieira Santello Mendes; Pablo Henrique Paschoal Capucho

DOI: 10.22167/2675-6528-2026M03

Article developed by the ResumeAI tool, an artificial intelligence solution developed by the Pecege Institute focused on synthesis and writing.

Abstract

The study addressed the importance of applying Big Data Analytics in the formulation of public policies, focusing on the financial sustainability of the Severance Pay Fund (FGTS). The objective was to analyze the correlation between FGTS net revenue and withdrawal amounts from April 2020 to January 2024, seeking to understand the impact of recent legal changes that expanded withdrawal possibilities, such as the birthday withdrawal modality. For this purpose, monthly public data on net revenue and the main types of withdrawals (birthday, retirement, own housing, and dismissal without just cause) were extracted from the official FGTS portal. Bivariate statistical analysis employed Pearson’s correlation coefficient. The results revealed positive correlations between net revenue and withdrawals for birthday, retirement, and own housing, and a negative correlation with withdrawals for dismissal without just cause. The analysis suggested that external variables, such as inflation and labor market formalization, may have simultaneously influenced revenue and withdrawals, without establishing a direct causal relationship. It was concluded that Big Data Analytics proved effective in identifying relevant patterns and supporting strategic decisions in public policies, and it is recommended to expand studies on the impacts of turnover in the formal market and its effects on FGTS sustainability.

Keywords: Data Analysis; Correlation; FGTS; Birthday Withdrawal; Financial Sustainability.

1. Introduction

The incorporation of data analysis into the organizational culture of companies is not recent. For some time now, organizations have sought to structure themselves in a data-driven manner, aiming to offer personalized products and services. This trend also extends to the public sector, where the ability to process and interpret large volumes of data can transform the development and execution of public policies.

The strategic use of data, through quantitative and statistical analyses, as well as predictive and explanatory models, which guides evidence-based decision-making, is known as Analytics. This field encompasses different levels. Descriptive analysis allows for the visualization of historical data. Predictive analysis contributes to the anticipation of future scenarios. Prescriptive analysis supports experimentation and the understanding of behaviors. In autonomous analysis, human intervention is reduced, with artificial intelligence and machine learning to extract knowledge from data (Davenport and Harris, 2018).

The strategic decision to adopt a data-driven approach challenges organizations to manage and manipulate massive volumes of information, measured in exabytes. When addressing the concept of Big Data, it is common to refer to the five fundamental dimensions in data generation and availability: volume, velocity, variety, variability, and complexity (Fávero and Belfiore, 2025). Volume refers to the immense quantity of data generated. Velocity concerns the speed of its creation. Variety is related to data heterogeneity. Variability refers to fluctuations in generation. Finally, complexity is linked to the multiplicity of sources, demanding effort for processing and analysis.

It should be noted that Big Data, in isolation, is not capable of providing strategic information with the potential to generate value. For this purpose, the use of Analytics is associated with the concept. The integration of these two elements configures the macroprocess called Big Data Analytics, which can be understood as the strategic use of data with the objective of generating value and achieving competitive advantage (Ferreira and Gomes, 2019). This macroprocess is composed of subprocesses of data capture, processing, and analysis, resulting in insights that support strategic decisions.

While companies use Big Data Analytics to maximize profits and retain customers, governments have the opportunity to apply this macro-process to seek efficiency in the development and maintenance of public policies. The use of data to promote social efficiency and generate measurable impact is crucial in the public sector (Davenport and Harris, 2018). A significant example of public policy in Brazil is the Fundo de Garantia do Tempo de Serviço (FGTS).

The FGTS was created in 1966 by Law 5.107 (Brazil, 1966), replacing job stability, with the main objective of constituting a financial reserve for the worker in case of dismissal without just cause. Furthermore, the Fund was conceived to assist in improving the population’s quality of life, allocating resources for housing, basic sanitation, and urban infrastructure financing, according to Law 8.036/90 (Brazil, 1990). However, throughout its existence, legal changes have expanded the reasons for withdrawing from the Guarantee Fund.

Since 2020, for example, workers who have joined the systematic withdrawal-on-birthday scheme can withdraw part of their FGTS balance regardless of contract termination. This new withdrawal modality, along with other changes, raises a fundamental question: do these changes impact the Fund’s net revenue and its ability to fulfill its original functions, compromising its sustainability?

Given this scenario, the application of Big Data Analytics becomes crucial to support informed decisions on public policies, ensuring the FGTS’s perpetuity as an instrument of social protection. Thus, the present study seeks to address the importance of using Big Data Analytics in decisions on public policies, analyzing the correlation between net FGTS collection and the amounts withdrawn in the birthday, retirement, own housing, and dismissal without just cause modalities, in the period from April 2020 to January 2024.

2. Material and Methods

The present study adopted a quantitative and correlational approach, aiming to analyze the relationship between the net collection of the Severance Indemnity Fund (FGTS) and the amounts withdrawn in different modalities. It sought to understand how legal changes that expanded withdrawal possibilities impacted the financial sustainability of the Fund. The research focused on evaluating the correlation between the variables, without establishing direct causal relationships.

The unit of analysis consisted of monthly public data regarding net FGTS (Fundo de Garantia por Tempo de Serviço) revenue and amounts withdrawn for different reasons. The observation period covered April 2020 to January 2024. This temporal delimitation was defined by data availability, as the withdrawal-by-birthday modality was implemented in April 2020 and net revenue data were available until January 2024 at the time of collection.

The data were captured directly from the official FGTS website (fgts.gov.br), which provides public information about the Fund. Monthly historical series of net revenue and actual withdrawals were collected. The withdrawal modalities considered included birthday withdrawal (codes 60 and 60F), retirement (codes 05, 05A, 05E, and 05F), own housing (codes 91, 92, 93, 95, and 96), and dismissal without just cause (codes 01, 01M, and 01S).

After capture, the data were organized into a monthly time series, using the Microsoft Excel tool. Each variable, including net revenue and the values of each withdrawal modality, was recorded in billions of reais. This organization allowed for the structuring of the data for the subsequent application of statistical analysis techniques.

To evaluate the correlation between the net collection of FGTS and the amounts withdrawn in specific modalities, bivariate statistical analysis was applied. The Pearson correlation coefficient was calculated using the CORREL function of Microsoft Excel. This coefficient is a statistical measure that indicates the strength and direction of a linear relationship between two quantitative variables.

The Pearson correlation coefficient ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, suggesting that the variables move in the same direction. A value close to -1 indicates a strong negative correlation, where the variables move in opposite directions. A value close to 0 (zero) suggests the absence of linear correlation (Fávero and Belfiore, 2025).

The interpretation of the correlation intensity is fundamental for the understanding of the results. Coefficients between 0.1 and 0.3 are considered weak, while values between 0.4 and 0.6 indicate a moderate correlation. Results between 0.7 and 1, on the other hand, point to a strong correlation. This classification helps in identifying the intensity of the relationship between the studied variables (Dancey and Reidy, 2019).

The application of Pearson’s coefficient aimed to identify the existence of a linear relationship between FGTS net revenue and each type of withdrawal, as well as the direction and intensity of this relationship. The procedure allowed us to verify whether the increase or decrease of one variable accompanied the behavior of the other, without inferring causality.

3. Results and Discussion

This section details the research findings, which aimed to analyze the correlation between the net collection of the Severance Indemnity Fund (FGTS) and the amounts withdrawn in different modalities, from April 2020 to January 2024. The investigation used a Big Data Analytics approach, as per the macroprocess described by Ferreira and Gomes (2019), which integrates data capture, processing, and analysis to generate strategic insights. The operationalization of the Big Data subprocess was carried out by Caixa Econômica Federal, the FGTS operator, which provides treated and aggregated public data, enabling the execution of the Analytics subprocess through statistical techniques, such as Pearson’s correlation analysis.

For the study, monthly data were collected regarding the net collection of FGTS and the main types of withdrawal: birthday, retirement, own housing, and dismissal without just cause. These data, expressed in billions of reais, were organized into historical series to allow the application of bivariate statistical analysis. The analysis period was delimited by the availability of birthday withdrawal data, which began in April 2020, extending to January 2024, when the most recent data were accessible for research.

Initially, it was observed that FGTS net collections showed negative values in the initial months of the pandemic, specifically from April to June 2020, and again from May to August 2021. In the other analyzed periods, collections remained positive. This variation suggests a sensitivity of the Fund to broader economic contexts, such as crises or recovery periods. Furthermore, a seasonal pattern was identified in collections, with notable peaks in December and January, and sharp drops in March of each year, indicating the influence of external factors, such as a heated labor market and increased consumption during the holiday seasons.

In contrast to net revenue collection, the birthday withdrawal modality has shown continuous growth in withdrawn amounts since its implementation in April 2020. This pattern of progressive increase can be interpreted as a reflection of the growing adherence of workers to this new withdrawal system, which allows for the annual withdrawal of part of the FGTS balance, regardless of contract termination. The observation of this constant growth highlights the popularity and consolidation of this option among the Fund’s beneficiaries.

When investigating the correlation between net revenue collection and the birthday withdrawal, the calculated Pearson correlation coefficient was 0.45, indicating a moderate positive correlation. This result suggests that as the values of the birthday withdrawal increase, the net revenue collection of the FGTS also tends to grow, and vice versa. However, it is fundamental to interpret that this positive correlation does not establish a direct causal relationship, but rather a simultaneous growth of the variables, influenced by common factors.

The careful interpretation of these results is crucial, as the correlation between variables does not necessarily imply a cause-and-effect relationship. In the context of FGTS and the birthday withdrawal, it is plausible that external macroeconomic variables, such as inflation, exert a significant influence. Inflation, by raising wages, tends to increase the value of deposits made by companies into FGTS. Concurrently, as the birthday withdrawal is calculated based on a percentage of the balance in the linked accounts, the increase in these balances also raises the amount available for withdrawal, generating a simultaneous growth of both variables without a direct causal link (Davenport and Harris, 2018).

The analysis of the correlation between net revenue and retirement withdrawals revealed a Pearson coefficient of 0.66. This value indicates a moderate positive correlation, which is stronger than that observed with the birthday withdrawal. The directly proportional relationship suggests that an increase in retirement withdrawals tends to occur alongside an increase in net revenue. Similar to birthday withdrawals, this correlation should not be interpreted as causal, but as an indicator of underlying factors affecting both dimensions.

For retirement withdrawals, the increase in net revenue can be influenced by salary appreciation in the formal labor market, while the growth in withdrawals can be attributed to the aging workforce and the consequent increase in the number of workers reaching retirement age. These external factors act jointly, raising both revenue and withdrawals, without one being the direct cause of the other. The application of Big Data Analytics allows for the identification of these complex patterns, which would be difficult to discern without an in-depth data analysis.

Regarding the correlation between net revenue collection and withdrawal for own housing use, the Pearson coefficient obtained was 0.37. This result indicates a weak positive correlation, suggesting a directly proportional relationship, but with less intensity compared to the withdrawal-anniversary and retirement options. The dispersion of points observed in the graphical analysis reinforces the weak nature of this correlation, suggesting that the proximity to a perfect correlation is limited.

The interpretation for the weak positive correlation between net revenue and withdrawals for own housing also aligns with the influence of external variables. The growth of real estate credit, for example, can increase workers’ willingness to acquire properties, resulting in more withdrawals. Simultaneously, wage appreciation and the formalization of the labor market can contribute to increased revenue. Thus, the observed correlation reflects the joint action of multiple macroeconomic and social factors, rather than a direct causal relationship between the volume of withdrawals and revenue.

In summary, the positive correlations observed for birthday withdrawals, retirement, and own housing, although moderate or weak, should not be interpreted as a strengthening of the FGTS’s sustainability. On the contrary, these results indicate that external variables, such as inflation and labor market dynamics, simultaneously influence both revenue collection and withdrawal volume. This perspective is crucial to avoid misinterpretations about the Fund’s financial health, highlighting the complexity of the interactions between the data.

The most relevant analysis for the Fund’s sustainability was the correlation between net collections and withdrawals due to dismissal without just cause, which represents the largest volume of withdrawals in the studied period. The Pearson correlation coefficient for this modality was -0.64, indicating a moderate negative correlation. This result is inversely proportional, meaning that as withdrawals due to dismissal without just cause increase, the FGTS’s net collections tend to decrease.

This negative correlation deserves special attention, as dismissals for termination without just cause are directly related to the FGTS’s original function, which is to provide worker protection during involuntary separations. The trend of reduction in net revenue, as dismissals increase, may compromise the FGTS’s ability to fulfill its other collective social functions, such as financing infrastructure projects, basic sanitation, and affordable housing. This hypothesis becomes particularly concerning in scenarios of increased informality in the labor market or the replacement of employment relationships with corporate contracts (PJ), which do not generate FGTS contributions.

Although correlation analysis does not establish causality, the negative correlation with dismissals for just cause raises a relevant hypothesis about the impact on the Fund’s sustainability. The increase in these dismissals may be contributing to the reduction of available resources, which, in an extreme scenario of informalization, could compromise not only the financial sustainability of FGTS but its very existence as an instrument of social protection. The ability of Big Data Analytics to identify this inverse relationship is fundamental to alerting public policy makers about potential risks.

The results obtained demonstrate that the use of Big Data Analytics offers a powerful tool for the formulation and evaluation of public policies. Although the positive correlations observed for the birthday withdrawal, retirement, and own housing do not allow us to affirm that these withdrawal modalities compromise the sustainability of the FGTS, they indicate the influence of external variables that simultaneously affect revenue and withdrawals. On the other hand, the negative correlation with withdrawals due to dismissal without just cause suggests a potential risk to the Fund’s ability to continue fulfilling its original functions.

In summary, the research revealed complex patterns in the interactions between net FGTS collection and the different withdrawal modalities. The positive correlations for birthday withdrawal, retirement, and own housing indicate the influence of macroeconomic factors that drive both sides of the equation, while the negative correlation with withdrawal due to dismissal without just cause points to a dynamic that can directly impact the Fund’s sustainability. Data analysis, therefore, is essential to support strategic decisions and ensure the FGTS’s longevity as a pillar of social protection and national development.

4. Conclusion

This study sought to analyze the correlation between the net collection of the Severance Pay Fund (FGTS) and the amounts withdrawn in different modalities, from April 2020 to January 2024, highlighting the importance of Big Data Analytics in the formulation of public policies. It was found that the net collection of FGTS showed moderate positive correlations with withdrawals for birthdays (0.45), retirement (0.66), and own housing (0.37). However, it was observed that these relationships do not indicate direct causality, but rather the influence of macroeconomic and labor market variables, such as inflation and formalization, which simultaneously drive collection and withdrawals. In contrast, a moderate negative correlation (-0.64) was identified between net collection and withdrawals due to dismissal without just cause, a modality that represents the largest volume of withdrawals and is directly linked to the Fund’s original social protection function. This finding suggests a potential risk to the FGTS’s ability to fulfill its collective social functions, especially in scenarios of increasing informality in the labor market. The application of Big Data Analytics thus proved to be an effective tool for identifying complex patterns and supporting strategic decisions in the scope of public policies.

It is essential to emphasize that correlation analysis, by its nature, does not establish causal relationships, which constitutes an inherent limitation of the employed methodological design. Careful interpretation of the results is crucial to avoid erroneous conclusions about the sustainability of FGTS, recognizing that external factors act jointly on the studied variables. Given the negative correlation observed with withdrawals due to dismissal without just cause, it is recommended to expand future studies to investigate the existence of a causal relationship between this type of withdrawal and the reduction in net revenue. It is also suggested to deepen the analysis of the impacts of turnover in the formal market and its effects on the sustainability of FGTS, seeking to identify measures that ensure the perpetuity and effectiveness of the Fund as an instrument of social protection and national development.

Bibliographic References

Brasil. 1966. Lei n. 5.107, de 13 de setembro de 1966. Cria o Fundo de Garantia do Tempo de Serviço, e dá outras providências. Disponível em https://www.planalto.gov.br/ccivil_03/LEIS/L5107.htm. Acesso em 23 mar. 2023.

Brasil. 1990. Lei n. 8.036, de 11 de maio de 1990. Dispõe sobre o Fundo de Garantia do Tempo de Serviço, e dá outras providências. Disponível em https://www.planalto.gov.br/ccivil_03/leis/l8036consol.htm. Acesso em 12 mar. 2023.

Dancey, C. P.; Reidy, J. 2019. Estatística sem Matemática para Psicologia. 7 ª ed. Penso Editora, Porto Alegre, RS, Brasil.

Davenport, T. H.; Harris, J. G. 2018. Competição Analítica: Vencendo Através da Nova Ciência. 1ª ed. Alta Books, Rio de Janeiro, RJ, Brasil.

Ferreira, R.; Gomes, J. S. 2019. Projeto de big data analytics: uma discussão à luz da capacidade absortiva. Disponível em https://periodicos.uff.br/sbijournal/article/view/28905. Acesso em 23 mar. 2023.

Fávero. L. P.; Belfiore, P. 2025. Data Analysis Manual: Statistics and Machine Learning with Excel®, SPSS®, Stata®, R® and Python®. 2nd ed. LTC Livros Técnicos e Científicos, Rio de Janeiro, RJ, Brazil.

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Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]