Enterprise data, without the enterprise overhead. Built for regional banks.
You have the data complexity of a large bank. You shouldn’t need the data infrastructure of one.
Loan origination. Digital banking. Deposits and lending. None of them know it’s the same customer.
WHAT’S ACTUALLY IN YOUR CORE
Advance the energy transition for a lower carbon future with lower risk and greater impact.
Cities are among humanity’s most magnificent creations, but the world’s cities are currently under severe threat by climate change. Major floods, storms, droughts, and fires are becoming increasingly common and deadly. To respond effectively to climate change, cities must mitigate and adapt in the short term and become Net Zero GHG producers in the medium term. Both objectives will require
new policies, behaviours, and regulations across a wide range of urban management issues.
But what are the right policies?
To determine what’s best, we need to collect reliable data from a broad spectrum of agencies and firms and across the urban landscape. Then, we must perform rigorous analytics to understand what the data means. At present, however, the necessary data are incompatible, fragmented, and mostly inaccessible. Net Zero Data Science has been created to help solve exactly this problem.The data is there. The answers aren’t.
Cores like Fiserv DNA don’t store a clean “customer”: a person’s relationship to an account is buried in role tables (owner, joint owner, signer), and everything else is coded values, not business language. Answering something as simple as “which deposit customers qualify for a loan” takes hours of manual, spreadsheet-driven work, and most banks this size have no data warehouse to start from.
vs. the DataOS way
30–32 weeks
4–6 weeks
faster
Governance
Powered by the first operating system for enterprise data
SUCCESS STORY
How Lenovo Built a Foundation for GenAI with DataOS
Integrates effortlessly with your existing infrastructure
anywhere
replace
out of the box
It’s time to treat data
The Modern way
Frequently Asked
Questions
Yes, DataOS is a Data Management Platform and more. DataOS does what a data management platform is designed to do: it organizes, governs, and makes data accessible across an enterprise. But traditional data management platforms stop at management. DataOS goes further.
Where most platforms focus on storing and organizing data, DataOS activates it. Governance, context, and semantic meaning are built into every data product from the start, so data isn't just managed, it's ready to use across analytics, applications, and AI without additional preparation.
That's the distinction. DataOS is a data management platform built for the AI era, where the goal isn't just organized data, it's data that works.
Most data captured by enterprises goes unused. It sits in warehouses and lakes, disconnected from the teams and systems that need it.
A data activation layer is what sits between your raw data and the people and systems that need it. Think of it the way you think of an operating system: it doesn't replace your existing infrastructure, it gives everything underneath it a shared foundation of context, governance, and semantic meaning.
With DataOS, that layer is built in. Governance, context, and meaning are applied at the data product level, so every team gets data that is accurate, consistent, and ready to use across analytics, applications, and AI, without starting from scratch every time.
Traditional data teams build pipelines for specific use cases, one at a time. Each request becomes its own effort, often taking several months, at least, to deliver value, with governance and context bolted on after the fact, if at all. The result is rework, silos, and data that can't be trusted or reused.
The Modern Data Company takes a different approach. We offer a data operation system based on data products that embed governance, context, and activation from the start, so data is ready to use across analytics, applications, and AI the moment it reaches a team.
DataOS operationalizes how data products are defined, versioned, governed, and activated across teams and systems. By managing data products as code and enforcing policies automatically, data organizations can compress multi-quarter delivery cycles into weeks, turning raw data into insights with greater reliability and consistency.
No. DataOS layers over existing warehouses, lakes, tools, and platforms. It integrates with existing infrastructure and provides governance, lifecycle management, observability, and activation capabilities without requiring system replacement or disrupting existing investments.
DataOS turns existing data stacks into AI-ready foundations by embedding semantic context, governance, quality controls, and versioning directly into data products. This enables LLMs, AI agents, and applications to access consistent, trusted data without manual preparation or duplicated engineering effort. Every data product in DataOS is AI-native from the start.
The Modern Data Company is the enterprise software company behind DataOS, the award-winning data operating system based on data products. Modern helps organizations turn their existing data infrastructure into AI-ready, governed data products that deliver business outcomes faster and at lower cost. The company is headquartered in Palo Alto, California, with offices in Indore, Hyderabad and Bangalore, India.
Think of DataOS for data the way you think of an operating system for your computer. Your OS doesn't replace your apps. It gives them a shared foundation: memory, file management, security, a common language. DataOS is that activation layer for your data infrastructure, the foundation that makes everything in your stack work together with shared context, governance, and intelligence.
DataOS is a data management platform from The Modern Data Company. It layers over your existing data stack, working with platforms like Snowflake, Databricks, and BigQuery, without requiring migration. The core unit of DataOS is the data product: a governed, reusable, and ready-to-use data asset that teams can build, manage, and activate for analytics, AI, and agentic workflows out of the box.
Unlike traditional data management platforms, DataOS doesn't ask you to rip and replace anything. It works with what you have, adding the activation layer your stack is missing.
Data products in DataOS are outcome-driven, reusable units of data that are self-contained and versioned. Each data product bundles data, transformation logic, a semantic model, quality contracts, access policies, governance, and consumption APIs into a single platform-managed unit. They are reusable building blocks for analytics, applications, and AI.
DataOS integrates seamlessly with existing data infrastructure without requiring rip-and-replace. It layers over tools like cloud warehouses, lakehouses, and data catalogs to add context, governance, and activation capabilities. Organizations retain their current investments while gaining a unified data product layer.