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Intelligent metadata catalog.

KeeDATA is a comprehensive metadata catalog solution designed for companies at various stages of data management maturity.
It focuses on fast and efficient deployment, enabling organizations to easily catalog, organize, and access their data assets to improve visibility and understanding.

76

Reduction in time to access and locate data

30

Improvement in the organization’s data quality

45

Increase in the utilization of data assets

Benefits

Key capabilities for efficient data management

Efficient data cataloging

Efficiently catalog and organize data assets, making it easy to quickly access relevant information, significantly improving visibility and understanding of data.

Data-driven decision making

KeeDATA supports informed, data-driven decision-making, which is crucial for the organizations’ competitiveness and operational efficiency.

Data governance support

Helps organizations establish consistent and compliant data management practices, improving regulatory compliance and managing data privacy and security.

Cost saving

By automating the cataloging, validation, and organization of data, KeeDATA reduces the operational costs associated with manual asset management.

Rapid deployment

Complete and functional metadata catalog solution in just six weeks, enabling a quick and efficient transition to a data-driven environment.

Flexibility & scalability

Designed to accommodate companies with different levels of data management maturity, KeeDATA can grow and adapt to the changing needs of the organization.

Success stories

Transform your data into a strategic asset.

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Insights

News, trends and perspectives about KeeDATA.

My journey into AI Governance began at the 2023 Second World Summit of Future Commissions held in Montevideo, Uruguay. This event, hosted by the Parliament of Uruguay and the Inter-Parliamentary Union, was a significant starting point. We discussed the need for anticipatory governance in AI, focusing on how this technology intersects with democracy, human rights, and societal structures. These discussions set the stage for a deeper exploration of AI’s transformative potential.

In the world of data applied to public utilities, striking the right balance between a centralized Business Intelligence (BI) strategy and a self-service approach is key to maintaining agility without sacrificing control.

In recent years, utilities have invested heavily in data analytics as a driver of efficiency and decision-making. However, this evolution has not always been supported by a unified strategy around Business Intelligence (BI) platforms.

In the energy business, the time factor is crucial. Decisions about which plants to start up, when to do it, and at what capacity require careful planning. It is not enough to simply react to real-time consumption: every mistake impacts efficiency and can put the stability of the system at risk.