Juan Lagos-Obando

Organizations face increasing regulatory and stakeholder pressure to integrate environmental, social, and governance commitments into strategic decision-making, yet sustainability reporting often remains disconnected from planning, operational control, and performance measurement. This study develops MISE as a theoretically informed, design-oriented organizational architecture that couples double materiality, strategic ESG programs, governed indicators, documentary evidence, and a Balanced Scorecard–Global Reporting Initiative (BSC–GRI) dashboard. An integrated multi-source single-case study was conducted in a Colombian industrial company using documentary, perceptual, technical, performance, and expert-appraisal evidence. The evidence base comprised 62 study records: 48 internal organizational participants, 8 external stakeholders, and 6 internal expert evaluators with a separate methodological role. The structured consultation obtained 51 responses from 56 eligible participants (91.1%). The case application formalized nine strategic sustainability programs and expanded and governed the measurement architecture from 6 to 18 KPIs. Between the 2024 reference measurement and the 2025 follow-up measurement, complete governance attributes for the 18 KPIs increased from 38/72 (52.78%) to 56/72 (77.78%), equivalent to +25.00 percentage points and +47.37% relative change. Combined Scope 1 and Scope 2 emissions decreased from approximately 1000 to 880 tCO₂e (−12.0%). Six experts appraised usefulness, feasibility, traceability, governance, and scalability potential. These observed changes describe the application period and do not constitute causal effects attributable exclusively to MISE. The contribution is the explicit coupling logic that links prioritization, execution, governed measurement, evidence, review, and reassessment within one case-based governance architecture.

The growth of big data ecosystems has shifted the classical paradigm of selective storage toward an approach that preserves large volumes of heterogeneous data for subsequent exploitation, thereby strengthening the adoption of repositories such as data warehouses and, particularly, data lakes. In this context, data ingestion from multiple sources, formats, and structures is a critical activity in implementing and operating these environments. However, it is often carried out in a highly ad hoc manner, with low levels of standardization and with variability managed informally. Beyond the operational complexity of ingestion itself, the variability in features such as source types, ingestion frequencies, transformation needs, and loading strategies constitutes an additional engineering problem that must be addressed systematically. This work tackles both issues in the context of a consulting firm involved in data migration projects to data lakes under governance constraints defined by clients in the BFSI sector. The goal is to formalize the data ingestion process through an architecture that provides technical, documentation, and training support for engineering teams, while also incorporating a variability management tool to model, analyze, and guide the configuration of ingestion solutions according to project-specific needs. In this way, the proposal seeks to reduce uncertainty, improve development quality, optimize resource utilization, and provide a more systematic treatment of variability in data ingestion projects. The proposal was evaluated through a structured survey answered by two cohorts totaling 29 respondents: an enterprise cohort of 15 practitioners (60% of the firm’s staff) and a prospective cohort of 14 engineering interns. For the enterprise cohort, the survey obtained average scores of 76 (individual) and 83.1 (role-averaged) for usability and 85.71 (individual) and 91.56 (role-averaged) for perceived quality; for the intern cohort, the corresponding individual averages were 70.18 for usability and 74.74 for perceived quality. In addition, a before/after comparison against the previous ad hoc workflow, covering objective engineering indicators, was conducted with both cohorts, providing task-based evidence that complements the perception-based results.

Background: Software Product Line (SPL) engineering relies on coordinated work over variability-intensive artifacts, yet existing SPL tools provide limited support for synchronized multi-user modeling and project-level collaboration. Objective: This paper presents a collaborative extension of VariaMos that combines synchronization based on Conflict-free Replicated Data Types (CRDTs), awareness and governance mechanisms, and integrated artificial intelligence (AI)-assisted model authoring. Method: Following Wieringa’s Design Science methodology, the treatment was designed, implemented, and evaluated through unit and functional tests, collaborative proof-of-concept scenarios, controlled load experiments, an exploratory expert survey with 20 specialists, and a bounded preliminary evaluation of the AI-assisted component. Results: The environment supports project- and model-level synchronization, presence awareness, role-sensitive collaboration, comments, revision history, rollback, and vote-assisted conflict resolution. Load tests characterized the operational limits of the Yjs-based architecture and showed that partitioning users across model-specific collaborative spaces improves stability. The survey identified conflict handling, traceability, versioning, authorship, and review workflows as priorities; because it used a purposive non-probability sample, it does not support population-level or productivity claims. In a 100-case evaluation comprising 50 base prompts and 50 metamorphic follow-up prompts, a context-enhanced chatbot configuration reduced structural hallucination from 84% to 18% and increased reproducibility from 22% to 72%. These results are preliminary and limited to one LLM accessed through OpenRouter, one controlled prompt bank, and single-pass executions. Conclusions: The study demonstrates the controlled feasibility of combining multi-artifact SPL collaboration with integrated AI-assisted authoring, while identifying trade-offs among convergence, semantic control, recoverability, and latency.

One of the factors that has steadily eroded the legitimacy of public institutions is the recurrence of corruption and irregularities in state management, especially in procurement processes, budget allocation, and public appointments. These phenomena have intensified public mistrust and highlight the urgent need for tools to strengthen transparency in the digital sphere. In this context, e-government platforms have become key mechanisms for promoting openness and accountability in public administration. However, ensuring transparency in electronic procedures remains challenging due to the lack of standardized auditing mechanisms and system interoperability. The objective of this study is to design, implement, and technically evaluate a software architecture for an electronic platform that operationalizes a maturity-based diagnostic model to assess transparency in electronic procedures within public organizations. The proposed platform enables real-time monitoring, centralized data management, automated reporting, and evidence-based transparency assessment. The architecture follows a structured design process aligned with ISO 25010 quality attributes, incorporating microservices, database replication, and load balancing to enhance system performance and security. Performance tests identified bottlenecks in authentication, query processing, and concurrent user load, leading to optimizations such as query indexing, caching, and message queue implementation. The results indicate that a well-structured software architecture enhances government transparency by ensuring auditable, traceable, and secure public data access. The platform provides a self-diagnostic tool for institutions to assess their transparency maturity based on open government principles.

Managing and analyzing data in data lakes for big data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The increasing need to process sensitive data emphasizes the relevance of secure-by-design approaches that integrate encryption techniques and governance frameworks to protect personal and confidential information. This study proposes a protocol that combines the capabilities of Databricks and format-preserving encryption to improve data security and accessibility in data lakes without compromising usability or structure. The protocol was developed using a design science methodology, incorporating findings from a systematic literature review and validated through expert feedback and proof-of-concept experiments in banking environments. The proposed solution integrates multiple layers, data ingestion, persistence, access, and consumption, leveraging the processing capabilities of Databricks and format-preserving encryption to enable secure data management and governance. Validation results indicate the protocol is effectiveness in protecting sensitive data, with promising applicability in regulated industries. This work contributes to addressing key challenges in big data security and lays the groundwork for future developments in data governance and encryption techniques.

Context: Managing and analyzing data in Data Lakes for Big Data environments requires robust protocols to ensure security, scalability, and compliance with privacy regulations. The rapid growth of sensitive data processing highlights the importance of secure-by-design approaches that integrate encryption techniques and governance frameworks to safeguard personal and sensitive information. Aim: This study aims to design and validate a protocol that combines Databricks and Format-Preserving Encryption (FPE) to enhance data security and accessibility in Data Lakes, ensuring data privacy without compromising usability or structure. Method: The study uses Design Science a the main methodology, where a systematic literature review was conducted to identify existing encryption techniques and challenges in data governance. The proposed protocol was developed using a secure-by-design framework, validated through expert feedback, and tested using proof-of-concept experiments in banking environments. Results: The protocol features multiple layers—data ingestion, persistence, access, and consumption—that integrate Databricks’ advanced processing capabilities with FPE. It enables secure data ingestion, controlled access, and efficient governance while maintaining data usability. The validation demonstrated its effectiveness in safeguarding sensitive data, with potential for broader application in regulated industries. Conclusions: This work addresses critical gaps in Big Data security and proposes a scalable, secure framework for Data Lake management. It sets the foundation for future advancements in data governance and encryption methodologies across diverse domains.