Why Centralized Data Governance Fails
The old model of a single, monolithic data warehouse creates a vicious cycle of mistrust and inefficiency. It puts an impossible burden on a central team and separates data from the people who know it best, leading to predictable problems:
Multiple Versions of the Truth
Before any formal data strategy is in place, different teams naturally define the same KPIs in slightly different ways. This creates multiple versions of the truth from the start, leading to constant reconciliation work and a deep lack of trust in the numbers.
The Central Bottleneck
To solve for inconsistency, the common strategy is to centralize everything in a data warehouse. But this puts an impossible burden on a central team, creating a human bottleneck where every data request takes weeks and slows decision-making to a crawl.
Loss of Business Context
While the intention behind a central warehouse is good, the handoff from domain teams is lossy. Critical tribal knowledge and subject-matter expertise don't survive this "game of telephone," leaving the data separated from its real business meaning.
The Inevitable Workaround
When the central system is slow and the data is untrustworthy, users inevitably abandon it. They export data to insecure spreadsheets, creating stale, siloed, and unreliable copies of data that amplify the chaos across the organization.
Our Solution: Empower Teams, Restore Trust
Our modern data platform is designed to alleviate the core challenges faced by every organization: the central data bottleneck and the loss of business context . The platform provides an easy way to create, update, and serve trusted data anywhere, with built-in tools to enforce access policies and provide a full audit trail. This approach empowers any organization looking to improve its data velocity, rebuild trust, and turn its data into a reliable, valuable asset.
Empower the Experts
Our self-service platform empowers the people who create and use the data to drive its lifecycle. We provide intuitive tools to easily model data, capture expert knowledge, and evolve models as the business changes.
Treat Data as a Product
Our platform helps your teams turn their data into discoverable, reliable, and interoperable products. Each semantic model becomes a trustworthy "contract" for that data product, managed with a software lifecycle that ensures it can be safely updated and evolved without breaking downstream applications.
Ensure Consistency and Security
Our semantic layer provides a common framework that enables decentralized velocity. It allows for clear data lineage, enforceable access policies, and a full audit trail across all data products. This ensures interoperability and security without a central bottleneck, providing the crucial balance of autonomy and consistency.
The Pillars of Modern Governance
Our platform delivers on this philosophy through a set of core features designed to provide transparency, collaboration, and efficiency.
A Software Lifecycle for Your Data Products
Your business logic is defined as code in Malloy, a readable and reusable language. Our platform treats this code like modern software: it's bundled into versioned packages, allowing you to safely update logic and manage dependencies. This workflow provides complete transparency, a full audit trail, and ensures you don't break the reports and applications that rely on your data products.
A Central Hub for Data Collaboration
Our platform is designed for team-based collaboration, not as a siloed development tool. The permission model is designed to support the data development lifecycle, ensuring the right people have the right access at the right stage. This allows analysts, developers, and domain experts to work together on the same data assets safely and efficiently.
Complete Visibility and Cost Optimization
Our platform provides a central control plane to manage your data ecosystem. Trace a metric's lineage from source to consumption, and use built-in Usage Analytics to understand who is querying what data. This allows you to optimize your system for performance and manage costs by focusing resources on the data products that provide the most value.
