Automation of dashboards for performance monitoring in email marketing

Marketing, Automação de Dashboards para Monitoramento de Desempenho em E-mail

doi:10.22167/2675-6528-202602862

Article

October 02, 2026

Automation of dashboards for performance monitoring in email marketing

Automation of Dashboards for Performance Monitoring in Email Marketing

Enzo Vaz Pinheiro; Henrique Raymundo Gioia

DOI: 10.22167/2675-6528-202602862

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The growth of digital marketing strategies has expanded the volume of data, making the analysis of campaign performance essential for data-driven decision-making. The impact of automating the data collection, consolidation, and visualization process of email marketing on operational efficiency and analytical consistency was evaluated. The research used a qualitative approach and a case study design. The developed workflow integrated data extraction via SQL queries in a marketing automation environment, consolidation and calculation of indicators through Google Apps Script, and automatic updating in a management dashboard in Looker Studio. The results showed that automation significantly reduced operational effort, decreasing data consolidation time from approximately 8 hours per week to less than 1 hour per week. Furthermore, the solution promoted greater standardization of metrics, traceability of calculations, and agility in generating analyses. It was concluded that dashboard automation is an effective tool for supporting data-driven management, with potential application in other digital communication channels.

Keywords: Data automation; Business intelligence; Management dashboard; Operational efficiency; E-mail marketing.

1. Introduction

The advancement of digital marketing strategies has significantly expanded the volume of data generated in interactions between companies and customers. In this scenario, the transformation of operational records into reliable indicators becomes determinant for decision-making in data-driven organizations. The analytical value of this data depends on consistent processes of collection, treatment, and interpretation (Davenport and Harris, 2007; Provost and Fawcett, 2013).

In the context of email marketing, measuring sends, deliveries, opens, clicks, and conversions is fundamental to tracking performance with granularity and supporting tactical and strategic decisions. Kotler and Keller (2016) highlight the relevance of metrics to evaluate the effectiveness of communication and customer relationship actions.

Despite the availability of these metrics, many organizations still face difficulties in consolidating and standardizing data into comparable bases. In operations involving multiple campaigns and diverse data sources, it is common for the updating of reports and dashboards to depend on manual routines for extraction, treatment in spreadsheets, and republication.

This manual workflow consumes considerable operational time, reduces information traceability, and increases the chance of data inconsistencies. The dependence on human interventions in various phases of the process, including report extraction, processing, standardization, indicator calculation, and dashboard updates, increases the risk of divergences between versions and hinders the reuse of the process for other channels.

The standardization of metrics and business rules is an essential condition for reliable comparative analyses, as pointed out by Kimball and Ross (2013). As an alternative to these challenges, dashboard automation centralizes indicators and rules, delivers updated results consistently, and reduces dependence on human intervention, optimizing operational efficiency and analytical consistency.

Faced with the need to optimize data management in email marketing and overcome the limitations of manual processes, this study is justified by the search for solutions that improve operational efficiency and analytical consistency. Thus, the present study aimed to evaluate the impact of automating the process of collecting, consolidating, and visualizing email marketing data on operational efficiency and analytical consistency.

2. Material and Methods

The developed research had an applied character, with a qualitative approach and descriptive-exploratory nature, focused on the development and evaluation of a practical solution for automating Business Intelligence processes in the context of e-mail marketing. The adopted design was a case study, investigating a contemporary phenomenon in its application context and documenting the logic of the process and the implemented solution (Creswell, 2014; Gil, 2008; Yin, 2015).

The unit of analysis corresponded to email marketing campaigns, where each campaign represented a unit for consolidation and comparative analysis. The database used originated from real email marketing campaign data, collected in the author’s operational context. To ensure confidentiality and preserve sensitive organizational information, anonymization techniques were applied, such as altering campaign names and removing direct identifiers.

The data extraction was structured based on a rolling seven-day temporal window, with weekly updates for report generation. The methodological evaluation considered recurring process executions between December 2025 and April 2026, allowing observation of indicator behavior across different weekly cycles. The data followed the logic of a marketing funnel, with decreasing volumes of sends, deliveries, opens, clicks, and conversions, ensuring analytical consistency.

The data flow designed for the prototype comprised three main layers. The source layer used a marketing automation environment compatible with the Automation Studio of platforms such as Salesforce Marketing Cloud (ExactTarget). A scheduled routine was modeled to periodically execute SQL queries on email campaign tracking tables (_Sent, _Open, and _Click), related to the send configuration table (_Job). The results were stored in Data Extensions, serving as intermediate bases.

The SQL query was designed to count, for each campaign, the number of unique recipients who received the message and the unique users who performed at least one open in the analyzed period. Only campaigns linked to conversion journeys were considered, identified by conventions in the naming of the pieces, such as terms associated with invitations, onboarding, and promotions, and by their prior relationship with transactional sales bases.

In the intermediate consolidation layer, the data were made available in a spreadsheet and processed using Google Apps Script. The developed code performed the reading of records, the grouping of information by campaign, the automatic calculation of performance indicators, and the recording of results in a summary tab, replacing manual processing procedures. The analytical layer used Looker Studio for visualizing the consolidated data in dashboard format, employing indicators, tables, charts, and filters.

Operational variables of the email channel were considered, such as sending, delivery, opening, click, and conversion events, in addition to derived indicators. Among the calculated indicators, the delivery rate, open rate, click rate, and conversion rate stood out. The use of weighted rates, calculated from the effective volumes of deliveries and interactions, was chosen to avoid analytical distortions. Conceptual standardization of metrics was a principle followed in the definition of indicators, aiming to ensure comparability and analytical consistency (Kimball and Ross, 2013; Farris et al., 2010; Martins and Costa Neto, 1998).

To ensure transparency and reproducibility, the automation script (Google Apps Script), the Structured Query Language [SQL] query, and an anonymized version of the database (Pinheiro, 2026) were made available in a public repository. The database was provided in Comma-Separated Values [CSV] format, preserving the structure and relationships between the observed variables. The documentation of procedures and code contributed to the traceability and auditability of technical decisions, enhancing the reliability of qualitative research (Creswell, 2014; Lakatos and Marconi, 2017).

3. Results and Discussion

The analysis of the research results allowed for an in-depth understanding of the impact of automation on the process of collecting, consolidating, and visualizing email marketing data. The study demonstrated the relevance of a standardized and automated workflow to optimize operational efficiency and analytical consistency, as proposed by the central objective. The findings reveal that the transition from a manual model to an automated system not only reduces the time spent on repetitive tasks but also enhances the quality and reliability of the generated information, crucial elements for strategic decision-making in digital marketing environments.

Initially, the research focused on the transition from the manual process to the automated workflow, which was the routine for monitoring the email channel. Before the prototype implementation, the Business Intelligence team performed a manual process daily. Weekly, data were extracted from operational reports, underwent treatment and standardization steps, including field adjustments, consistency validations, and indicator calculation, and were then published on a dashboard for executive reports. This process, besides consuming significant time, increased the probability of discrepancies between data versions and hindered the reuse of the methodology for other communication channels.

Transition from manual process to automated workflow

The transition to the automated flow represented a paradigm shift in the management of email marketing data. The manual model, which relied heavily on human intervention, was prone to errors and inconsistencies, as well as being time-inefficient. Automation, in turn, was designed to be reusable across other digital communication channels, such as in-app notifications, instant messages, SMS, and phone support. The only manual step required in the new flow is the initial configuration of the input database for each channel and its respective connectors, which demonstrates the scalability and flexibility of the developed solution.

This change is fundamental for organizations seeking data-driven management, where information agility and accuracy are crucial. The dependence on manual interventions in multiple phases of the process, from extraction to dashboard updates, not only increased operational time but also compromised information traceability. In scenarios with a large volume of campaigns and frequent updates, the fragility of the manual process became a significant bottleneck, directly impacting the Business Intelligence team’s ability to provide timely insights.

Structuring and consolidation of data

The structuring and consolidation of data were critical steps in the prototype’s development. The database was modeled to represent multiple email marketing campaign submissions over a week, covering different types of creatives and audience segments. The consolidation routine, implemented in Google Apps Script, aggregated these records by campaign, generating a summary tab. This tab contained, for each campaign identifier, the total volumes of sends, deliveries, opens, clicks, and conversions, in addition to automatically calculated percentage metrics. This process was essential for reducing information fragmentation and ensuring greater consistency in subsequent analyses.

The organization of data at the campaign level, as detailed in the methodology, aligns with the concept of analytical granularity, which is the level of detail at which data is structured for analysis. This approach, as highlighted by Kimball and Ross (2013), is vital to ensure the consistency, comparability, and interpretability of indicators over time. By consolidating operational information into a coherent unit, the study facilitated comparative analyses between different mailings, allowing for a clearer view of the performance of each campaign individually and in relation to the whole.

Automation of the consolidation process

The automation of the consolidation process represented one of the main operational gains of the prototype. The execution of the `atualizarResumoEmail` function in Google Apps Script allowed data to be grouped by campaign in a few seconds, eliminating the need for manual consolidations in spreadsheets. In a scenario of recurrent use, this routine can be scheduled to run at predefined intervals, ensuring that the dashboard information remains updated without manual intervention. From a methodological point of view, automation ensures that the same calculation rules are applied uniformly to all analyzed periods, which is fundamental for the validity of comparisons.

This standardization of calculations and the consistent application of business rules are key elements for data reliability. Human intervention in manual calculation processes, as previously occurred, introduced an inherent risk of errors and inconsistencies. With automation, the calculation logic is defined once in the script and applied identically to all data, ensuring the integrity and comparability of metrics over time. This advancement is crucial for building robust indicators that support management, as advocated by Martins and Costa Neto (1998).

Construction and use of the management dashboard

The Weekly Report dashboard was built in Looker Studio from the summary tab generated by the code, configured to present, in its upper layer, aggregated indicators of sends, deliveries, opens, clicks, and conversions. Additionally, the panel displayed weighted average rates for each metric, offering an executive view of overall performance. In the intermediate layer, bar charts were arranged that highlighted the campaigns with the best relative performance in terms of click-through rate and conversion rate, facilitating the quick identification of successes and areas for improvement.

The dashboard layout prioritized executive reading, aiming to facilitate interpretation by decision-makers and comparison between campaigns. The inclusion of filtering features by period and campaign category allowed for more specific analyses by business users, adapting the visualization to their needs. The panel’s ability to support quick comparisons between campaigns, with a table structure that used conditional coloring to identify above or below-average performance, demonstrated its usefulness as a support tool in periodic management meetings, given the importance of presenting results in qualitative research (Creswell, 2014).

Preliminary analysis of indicators

The preliminary analysis of the indicators was carried out based on 99 campaigns consolidated in the dashboard. The total observed was 8,890,823 shipments, resulting in 8,416,272 deliveries, 2,135,526 opens, 714,353 clicks, and 242,700 conversions. In aggregate terms, the weighted average delivery rate was 94.66%, the weighted average open rate reached 25.37%, the weighted average click rate was 8.49%, and the weighted average conversion rate was 2.88%. These values provide an objective quantitative benchmark for evaluating the behavior of the analyzed email marketing funnel, allowing for a solid basis for future comparisons and optimizations.

It was observed that delivery rates remained high and relatively stable between campaigns, suggesting a good capacity to distribute messages to recipients. Open rates, on the other hand, showed greater variation, reflecting differences in audience interest, subject line relevance, and campaign segmentation. Click-through rates were lower than open rates, as expected in funnel structures, indicating that not all users who view the message take further action. Conversion rates, in turn, showed a lower level, consistent with the expected behavior in digital marketing campaigns, where only a fraction of users who click complete the desired action.

Impact assessment of automation

The implementation of the automated solution allowed for a direct comparison with the previously used manual process. In the manual model, data extraction and consolidation required daily intervention, which increased the risk of inconsistency and operational time. With automation, these steps became standardized and recurrent, resulting in greater data reliability, reduced errors, and increased agility in generating analyses. This change is crucial for the efficiency of Business Intelligence operations and for the quality of information that supports managerial decisions.

In operational terms, the reduction in time spent on data consolidation and validation activities was significant. Before automation, the Business Intelligence team spent approximately 8 hours per week on these tasks. With the automated workflow, this time was reduced to less than 1 hour per week, with involvement restricted to specific monitoring and process maintenance activities. This gain in efficiency is one of the most tangible results of the study, freeing up the team for higher value-added activities, such as interpretation and strategy proposal, instead of dedicating themselves to repetitive and manual tasks.

Dashboard use cases for decision support

The automated dashboard demonstrated its ability to quickly identify deliverability variations and operational errors per campaign, offering direct support for decision-making. In a scenario of a drop in the delivery rate, for example, the visualization would facilitate problem detection on the same day, guiding actions such as reviewing segmentation, checking domain reputation, or adjusting sending rules before the anomaly propagated. This agility in identifying problems is a significant differentiator for proactive email marketing campaign management.

Another relevant scenario for using the dashboard occurs when the open rate remains high, but the click-through rate does not follow the same pattern. This combination of metrics can indicate a misalignment between the message subject, content, and call to action, suggesting the need for message variation testing, review of the click destination, and adaptation of the offer to the impacted segment. The ability to cross-reference and interpret these indicators allows managers to identify friction points in the marketing funnel and implement targeted improvements, optimizing campaign performance.

Finally, the joint analysis of click-through and conversion rates allows for prioritizing campaigns with greater efficiency across the entire funnel. By comparing creatives with similar opening performance but different conversion rates, leadership can direct efforts towards journeys and segments with higher expected returns. Furthermore, the dashboard facilitates the establishment of operational goals per indicator for weekly monitoring cycles, transforming raw data into actionable insights for management. This functionality reinforces the dashboard’s role as a strategic tool for continuous campaign optimization.

Analytical reading of funnel indicators

The joint reading of the indicators shows that the dashboard’s main analytical potential lies in the chained interpretation of the performance funnel, and not just in the isolated visualization of metrics. In aggregate terms, a delivery rate above 94% indicates operational stability in campaign distribution. The largest variations, however, were concentrated in the opening and click stages, suggesting that the main performance differentials between campaigns are more associated with content, message attractiveness, and segmentation adequacy, rather than delivery failures. This perception directs the focus of the analysis to more strategic aspects of marketing.

This behavior reinforces the managerial utility of the dashboard as an instrument for analytical prioritization. Instead of focusing the evaluation solely on absolute volumes, the solution allows for the identification of which stages of the funnel experience the main relative performance losses, offering subsidies for more precise decisions regarding the subject, segmentation, call to action, and conversion journey. Thus, the automation of the panel expands the team’s analytical capacity by transforming dispersed operational data into comparable and actionable evidence, even in a study of an applied and descriptive nature, such as this one.

Comparison between the manual and automated process

The analysis of workflows revealed significant structural differences between the manual and automated data consolidation process. In the manual model, the execution of steps depended directly on human intervention in several phases, including report extraction, processing, standardization, indicator calculation, and dashboard updating. This dependence increased operational time, raised the risk of inconsistencies, and hindered information traceability, especially in scenarios with multiple campaigns and frequent updates, which compromised the agility and reliability of the analyses.

On the other hand, the automated process centralizes critical steps in programmed routines, highlighting the execution of SQL queries, script processing, and automatic dashboard updates. This model significantly reduces the need for manual intervention, ensuring greater standardization of calculation rules and greater consistency in results over time. Automation also allows for greater process scalability, enabling its replication to other communication channels without a proportional increase in operational effort. Traceability is improved, as data transformations are defined by explicit rules implemented in code, facilitating future audits and validations.

The main difference between the two models lies in the transition from a fragmented operational process dependent on manual actions to an integrated, standardized, and automation-driven workflow. This change results in substantial gains in efficiency, reliability, and agility in generating analyses. The manual process, with its direct operational time of approximately 8 hours per week and manual consolidation in spreadsheets, presented a high risk of inconsistency. In contrast, the automated process reduced operational time to less than 1 hour per week, with automatic consolidation via script, standardization of calculations by programmed rule, and a significantly lower risk of inconsistency, in addition to a high potential for replication to other channels.

In summary, the automation of the email marketing data collection, consolidation, and visualization process demonstrated a positive and multifaceted impact. The reduction in operational effort from 8 hours per week to less than 1 hour, the standardization of metrics, the traceability of calculations, and the agility in generating analyses confirm that the developed solution significantly contributes to operational efficiency and analytical consistency. The management dashboard, by synthesizing email marketing funnel behavior and identifying performance patterns, qualifies the managerial use of data, providing an effective instrument for supporting data-driven management and responding to the study’s central objective.

4. Conclusion

This study sought to evaluate the impact of automating the process of collecting, consolidating, and visualizing email marketing data on operational efficiency and analytical consistency. It was found that the implementation of an automated workflow resulted in a significant reduction in operational effort, decreasing the time dedicated to data consolidation from approximately 8 hours per week to less than 1 hour per week. Furthermore, the developed solution promoted greater standardization of metrics, improved calculation traceability, and provided greater agility in generating analyses. The management dashboard, by synthesizing email marketing funnel behavior, allowed for the identification of performance patterns and points of attention in the delivery, open, click, and conversion stages, with the greatest variations concentrated in open and click rates, suggesting that performance differentials are associated with content and segmentation. Thus, dashboard automation constitutes an effective instrument for supporting data-driven management, transforming dispersed operational data into comparable and actionable evidence.

As limitations of the study include dependence on a specific organizational context and the need for database anonymization, which reduced some of the contextual detail of the campaigns. However, the proposed architecture has potential for adaptation to other digital communication channels and new analytical applications. For future studies, it is suggested to expand the set of metrics and incorporate comparative statistical analyses to investigate associations between campaign performance and additional variables, such as processing time or email profile characteristics. Additionally, evaluating predictive models to estimate campaign performance and automating a second phase of the analytical process, with artificial intelligence tools for anomaly identification and recommendation generation, could further enhance the dashboard’s contribution, while maintaining human validation of interpretations.

Bibliographic References

Creswell, J.W. 2014. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 4ed. SAGE Publications, Thousand Oaks, CA, EUA.

Davenport, T.H.; Harris, J.G. 2007. Competing on Analytics: The New Science of Winning. 1ed. Harvard Business Press, Boston, MA, EUA.

Farris, P.W.; Bendle, N.T.; Pfeifer, P.E.; Reibstein, D.J. 2010. Marketing metrics: The definitive guide to measuring marketing performance. 2ed. Pearson Education, Upper Saddle River, NJ, EUA. Disponível em: <https://pergamum-biblioteca.pucpr.br/acervo/339190>. Acesso em: 12. jan. 2026.

Gil, A.C. 2008. Métodos e técnicas de pesquisa social. 6ed. Atlas, São Paulo, SP, Brasil.

Kimball, R.; Ross, M. 2013. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 3ed. Wiley, Hoboken, NJ, EUA.

Kotler, P.; Keller, K.L. 2016. Administração de marketing. 16ed. Pearson Education, São Paulo, SP, Brasil. Disponível em: <https://pergamum-biblioteca.pucpr.br/acervo/378796>. Acesso em: 10 jan. 2026.

Lakatos, E.M.; Marconi, M.A. 2017. Fundamentos de metodologia científica. 8ed. Atlas, São Paulo, SP, Brasil.

Martins R.A.; Costa Neto P.L.O. 1998. Indicadores de desempenho para a gestão pela qualidade total: uma proposta de sistematização. Gestão & Produção 5(3): 298-311.

Pinheiro, E.V. 2026. tcc_email_dashboard_repo. GitHub, 2026. Disponível em: <https://github.com/enzovp01/tcc_email_dashboard_repo>. Acesso em: 09 fev. 2026.

Provost, F.; Fawcett, T. 2013. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, Sebastopol, CA, EUA.

Yin, R.K. 2015. Estudo de caso: planejamento e métodos. 5ed. Bookman, Porto Alegre, RS, Brasil.

Article originating from the Final Course Project of the Specialization in Data Science and Analytics of the MBA USP/Esalq

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School Management

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The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

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 […]