DataOS: The Data Activation Layer for AI and BI

DataOS, from The Modern Data Company, is the AI-native data activation layer for enterprise data. It integrates with your existing stack, building on your current infrastructure rather than replacing it, to unify and govern fragmented systems into AI-ready data products for analytics, AI applications, and automated workflows.

Recognized by CIO Review as #1 Data Product Platform for 2025, DataOS helps organizations orchestrate data end-to-end, delivering up to 80% faster activation with built-in governance and compliance.

Data Products: From Pipelines to Reusable Assets

At the core of DataOS lies its data product architecture, a fundamental shift from traditional pipeline-driven data delivery to a product-centric model. In DataOS, the dataset is packaged as part of a data product, which is managed, versioned, and governed with clear lineage, ownership, and business context. These products are discoverable and reusable across teams, ensuring that the same trusted data fuels analytics, marketing, finance, and AI applications. For example, Customer 360 data products enable unified segmentation, personalization, and campaign orchestration without redundant engineering. This approach eliminates duplication, shortens delivery time, and enforces reliability at scale.

Governance and Trust by Design

DataOS embeds governance into the fabric of data operations rather than layering it on top. Security, privacy, quality, and compliance policies are enforced automatically as part of data-product creation and consumption. Role-based access control, audit trails, and policy propagation ensure that every data interaction remains transparent and compliant. This governance-by-design approach builds organizational trust, business users gain confidence in the data they consume, while IT and compliance teams maintain control and visibility across the entire data landscape.

Context Through a Universal Semantic Model

The universal semantic model in DataOS serves as a critical enabler for teams, ensuring consistent meaning and alignment across data products. By providing a shared vocabulary and unified definitions, it connects data from disparate systems into a single contextual graph. It allows users, both technical or non-technical to query and interpret data using business terms instead of complex schema logic. Marketing teams can quickly activate unified customer intelligence, while analysts and AI models operate on consistent, context-rich data. The result is a connected enterprise where information flows meaningfully, unlocking predictive insights and faster decision-making.

Software-Engineering Principles for Data

DataOS applies the rigor of software engineering to the world of data. Declarative configuration, CI/CD for data pipelines, modular orchestration, and version control bring repeatability and automation to every stage of the data lifecycle. These capabilities reduce human error, accelerate iteration, and make data environments auditable and testable. just like application code. The platform has a cloud-native and composable architecture that ensures portability across hybrid or multi-cloud deployments while optimizing scalability and performance. This engineering discipline enables organizations to deploy governed, AI-ready data environments in weeks rather than months, often resulting in operational cost reductions of up to 50%.

Turning Data into Continuous Innovation

DataOS is recognized as the Data Solution of the Year by CIO Review, underscoring its measurable business impact: faster implementation, higher data quality, and greater business agility. By combining reusable data products, governance automation, context-rich data, and an engineering discipline, DataOS enables enterprises to operationalize intelligence across multiple functions, from marketing and supply chain to finance and customer service.