In 2023, a public entity in Latin america faced an audit of criticism: the reports do not match, the indicators were duplicates and no one could explain where it came from the data. The result: observations, sanctions and a loss of institutional trust.
Two years later, that same entity operates with total traceability. Their reports are generated automatically, alerts anticipate inconsistencies and address decision-making based on evidence. What changed? Implemented a comprehensive model of Data governance, powered by IA Labs.This case is not unique. The sad reality is that "73 % of the projects of analytical fail not for lack of technology, but because of lack of governance," according to KPMG (2025). The Data governance it is the strategy of survival in the digital age.
1. Gobernanza de Datos: Una Estrategia de Confianza
The Data governance it is much more than a technical policy or a committee; it is an institutional strategy that sets out the rules, roles, and processes that ensure that the data are accurate, safe, traceable and helpful throughout the organization.
In essence, the value of the data is summarized in one sentence: “It is not how much you know, but how reliable is what you know.”
Its central objective is not only to meet standards but to ensure that the decisions of business and public management are based on reliable information and ethics.
2. El Alto Costo del Desorden: Por Qué Fallan las Organizaciones
The chaos informative that prevents the intelligence institutional have common causes and serious. According to Gartner (2025), and 82% of businesses still operate with fragmented data between departments.
This disorder is manifested through:
- Duplicate databases or out-of-date, raising the costs of storage.
- Lack of responsible, clear quality, and ownership of the information.
- Processes without traceability (data lineage), making it impossible for the audits.
- Systems that do not communicate, forcing you to rework manuals.
- Decisions taken "by intuition" rather than evidence.
This situation not only creates operational errors, but also prevents build a culture of responsibility digital is essential for the future.
3. Los 5 Pilares de un Modelo de Gobernanza Moderno
Inspired in frameworks such as DAMA-DMBOK 2.0, a system of Data governance cash combines structure, culture and technology, by focusing on these key pillars:
Calidad y Confianza
It focuses on defining standards of accuracy, completeness, and consistency. The direct benefit is the institutional trust in the decisions taken.
Lineaje y Trazabilidad
Consists of recording the journey of each piece of data, from its origin to its final consumption. This is vital for the audits and the regulatory compliance.
Seguridad y Privacidad
Applied standards (such as ISO 38505) to the responsible treatment of data. The aim is to protect the reputation and avoid legal sanctions.
Gobernanza Ética y Explicabilidad
Established principles of fair use and non-discriminatory. This pillar is crucial to integrate the IA in a transparent and equitable.
Cultura del Dato
Train teams and promote the shared responsibility. Without a culture that values the data, the governance is not sustainable.
4. La Ventaja de IA Labs: Automatizando la Gobernanza con IA
Unlike vendors that only offer "tools", IA Labs designed ecosystems governance. We combine methodologies such as LADY with our AI & Data Platform, a suite of technology that automates the traceability, security, and advanced analysis.
Our capacity to transform the governance of a load documentary to a competitive advantage institutional:
- Cataloging Automatic: Discovery and indexing of sources and data assets.
- Quality in Real-Time: Metrics dashboard that automatically detects and corrects anomalies automatically.
- Module Compliance: Traceability legal and alignment with standards such as ISO 38505 and the future AI Act.
- Predictive Models: Use of the IA to detect inconsistencies before they affect decision making.
As stated by Forbes Tech Council (2025), “The data are well governed, are the most profitable asset of the TWENTY-first century.”
5. El Nuevo Equilibrio: IA, Ética y la Decisión Confiable
With the expansion of the artificial intelligencegovernance is no longer just about protecting data, but to protect the reliability of the decisions that algorithms make.
As pointed out by the OECD (2024), “A model without governance can be just as dangerous as a decision without data.”
IA Labs it integrates in its platform beginning of AI Governanceensuring:
- Traceability of Models: You know what data and rules trained each algorithm.
- Control of Biases: Monitoring and mitigation of the discrimination algorithm.
Explicabilidad Results: Ability to justify the outputs of the IA.
6. Cómo Empezar: Pasos Clave para Implementar Gobierno de Datos
The successful implementation of a Data governance comprehensive follow a strategic roadmap:
- Initial Diagnosis: Evaluate the current maturity on quality, safety and culture of the data.
- Definition of Roles: Designate a Chief Data Officer or governance committee with clear authority.
- Set Standards: Align policies of data with DAMA-DMBOK and international frameworks.
- Implement Enabling Technology: Take the AI & Data Platform of IA Labs to automate the compliance and monitoring.
Train and Communicate: Promotes a culture of responsibility and decision-making based on evidence.
Conclusión: Gobernar los Datos es Gobernar el Futuro
The Data governance this is not a project, it is a continuous process and an indispensable means to make more intelligent decisions, ethical and sustainable. In a world where the digital confidence is the new capital, to govern the data is to govern the future of your organisation, whether public or private.
IA Labs not only design platforms, but that builds ecosystems where the fragmented information is converted into knowledge-building and knowledge translates into strategic action.
Request a diagnosis of maturity-free in Government Data.
Download our Guide DAMA-DMBOK applied to public and private entities and begin today to transform the chaos informative in understanding institutional.
Fuentes y Bibliografía
- KPMG (2025). Data Governance in the Age of AI.
- DAMA International (2024). DAMA-DMBOK 2.0.
- OECD (2024). AI and Data Ethics Report.
- Gartner (2025). The Future of Data and Analytics Governance.
- Forbes Tech Council (2025). Why Data Governance Is the New Competitive Edge.
ISO (2024). ISO/IEC 38505-1:2024 – Governance of Data.
