On a Friday, a manufacturer handed over nearly 50 sample telemetry files. By Tuesday, The Modern Data Company had a working demonstration. In the company’s published account, a prototype was live within five days, production followed in four weeks, and leadership approved a wider rollout within twelve. The interesting detail is the size of the beginning: a handful of files, rather than a commitment to rebuild everything.
- The product: DataOS turns scattered enterprise data into reusable data products.
- The buyer: organizations whose analytics and AI projects keep repeating the same preparation work.
- The test: whether the next use case can reuse what the first one built.
Modern sells enterprise software. Its flagship, DataOS, sits across existing infrastructure, organizing data with definitions, quality controls, permissions, and interfaces for applications. Its wager is straightforward: the expensive part of data work often happens after the data has already been stored.
A warehouse is only the beginning
Consider a familiar business question: which customers are growing? An illustrative example sounds simple until finance counts customers by billing account, sales counts them by parent company, and an application counts logins. All three teams can query their tables correctly and still disagree. More computing power will produce the disagreement faster.
DataOS makes a data product the unit of work. That product bundles the underlying data and transformations with business meaning, quality expectations, access rules, and ways to consume it. A team defines something useful once, then exposes it to dashboards, applications, or AI. The machinery is technical; the desired experience is less detective work.
Operational data
Policies + lineage
AI agents
Conceptual view of Modern’s product model.
The distinction matters in a market full of warehouses, catalogs, integration services, and governance tools. Modern’s proposition combines several jobs in an operating layer. Its documentation describes a Data Product Hub for discovery and activation, alongside interfaces for tools such as Power BI and Tableau. It is selling a path from stored information to something people can repeatedly use.
The pilot that shortened the argument
The manufacturer story provides the company’s clearest demonstration of that path. Its problem began with fragmented telemetry across systems and teams. Each new requirement threatened to become another bespoke engineering project. Modern’s response was to place reusable, governed products over the existing infrastructure.
The published case reports a five-day prototype, four-week production deployment, and training for more than forty engineers. It also credits executive sponsorship and rapid prototyping. Those details matter as much as the software: a working example gave stakeholders something concrete to approve, while training helped people adopt the new method.
“DataOS fundamentally changed how we think about data.”
Senior Director of Data & Analytics, manufacturer case study
These are vendor-published results, rather than a controlled comparison. Still, they suggest a useful buying principle. Ask a supplier to demonstrate one consequential workflow on your own data. Then ask how much of that work survives when the question changes. A quick first answer is encouraging. Reuse is the harder proof.
And then there is tequila
At Lobos 1707, the business question concerned where sales representatives should spend their time. Market information, distributor records, customer details, and sales activity lived in separate systems. Reconciling them consumed attention that could have gone toward the next account.
Modern’s case study describes bringing those inputs into a governed foundation and supplying daily recommendations, informed by inventory and distribution coverage. It reports 170,000 potential sales opportunities, initial value in 45 days, and 15% year-over-year volume growth. Potential opportunities are prospects, not booked revenue; the growth figure alone cannot establish what DataOS caused.
To begin seeing value in the Lobos 1707 case study.Reported by Modern; specific to this engagement.
The useful connection between tequila and telemetry is repetition. Both stories involve assembling information for a decision, then making that assembly available again. The user might be a salesperson or an engineer. The underlying ambition is the same: stop charging every new question the full preparation cost.
An operating system, with a small “o”
Co-founders Srujan Akula and Animesh Kumar built the company around simplifying enterprise data. Kumar now appears as CTO and CPO. In January 2026, Modern announced Saurabh Gupta as CEO, with Akula moving into product strategy and execution. The leadership combines platform engineering with experience in data strategy and governance.
The naming has its own revealing history. A 2022 brand essay explained that “data fabric” had become too narrow a label for the company. Its redesigned M used composable pieces to echo the architecture. Modern wants the operating-system analogy to communicate a shared foundation that different tools can use.
That positioning also complicates the competitor list. Snowflake, Databricks, and BigQuery can be part of the stack DataOS sits above. An enterprise could instead assemble comparable capabilities from separate products, or evaluate a broad platform such as Microsoft Fabric or Palantir Foundry. The comparison should follow the workload and operational burden.
Price the work that comes next
Modern’s public FAQ describes pay-as-you-go pricing with no upfront fees and an annual enterprise subscription. A useful evaluation needs the contract alongside infrastructure costs, implementation effort, and the people responsible for maintaining definitions and policies. Modern’s AI cost calculator itself calls its estimates directional.
Its 3D Sprint offers a smaller starting point: a six-hour session using customer exports to develop a governed answer to a business question. That is an invitation to test the method. An afternoon demonstration still needs a production plan, especially when access, performance, and ownership cross departmental boundaries.
The next customer has a mission
Modern is extending that proposition into government. Leidos confirmed a June 2026 partnership bringing DataOS into HeadWay Mission OS. Carahsoft supplies a procurement channel. In October, Modern announced FedRAMP Class D (High) certification as deployed through Second Front’s Game Warden environment. That deployment scope matters.
The approach looks most persuasive where existing systems must remain, several teams need the same information, and leaders will assign ownership. A simpler organization may have less repeated work to remove. Software also cannot settle an unresolved argument about what a metric should mean. The lesson readers can copy is modest: choose one decision, define the data behind it, name its owner, and test whether a second team can use it.