Kindly fill up the following to try out our sandbox experience. We will get back to you at the earliest.
Simplified governance and building trust in data putting consistency and standardization at the forefront across teams.
PII and Sensitive fields are classified automatically for security and compliance.
No manual updating of meta-data. Once your sources are connected, it's auto-refreshed.
Control who can view or edit information with designated approval flow.
Effortlessly access a comprehensive list of assets classified under predefined policies. Decube’s platform allows you to automatically identify sensitive data or manually categorize assets within the Data Catalog, ensuring your organization maintains consistent governance and control over critical information.
Our advanced Governance module automates the management and protection of your most valuable data assets, ensuring robust security, regulatory compliance, and data privacy. With intelligent classification and tagging, your organization can streamline governance processes and stay ahead of evolving compliance requirements
All changes and requests within these modules are subject to an intuitive approval workflow, ensuring full oversight and control before implementation. This process safeguards your data governance policies, promoting accountability and minimizing the risk of unauthorized modifications
Decube’s workspace enforces robust access controls by assigning user permissions to specific groups, ensuring only authorized personnel can access sensitive data. This granular approach to asset management safeguards your data, promoting both security and compliance across your organization
Implement precise access controls that allow you to restrict user access to specific data assets, rather than broad access to the entire source. This granular approach enhances data security, ensuring that users only interact with the information they are authorized to handle
Data governance is the practice of managing data availability, usability, integrity, and security across an organization. It ensures that data is trustworthy and consistent so that business decisions and AI initiatives are based on reliable information.
A strong data governance framework typically includes data ownership, data quality management, metadata management, data lineage, business glossary, and access control. Together, these components create a foundation of trust in enterprise data.
AI systems are only as good as the data they consume. Data governance ensures data is accurate, consistent, and contextualized—helping organizations achieve higher ROI from AI and reducing the risk of biased or incorrect outputs.
Common challenges include siloed data systems, lack of clear data ownership, inconsistent policies, and resistance from business teams. Modern platforms help simplify governance by automating metadata capture, lineage, and quality checks.
Data management focuses on the technical handling of data (storage, integration, processing), while data governance defines the rules, roles, and policies that guide how data should be used responsibly and effectively.
Data governance involves collaboration between multiple stakeholders: data stewards, data engineers, business analysts, compliance officers, and executives. Increasingly, organizations are forming Data Governance Councils to drive accountability.
Organizations can use specialized platforms that unify data cataloging, lineage tracking, observability, and business glossaries. Modern solutions (like Decube’s Data Trust Platform) provide automation and real-time monitoring to simplify governance and make it scalable.
_For%20light%20backgrounds.svg)
.svg){kind=small}
.webp)
