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DataOS Blog

68% say their data isn't reliable enough for AI. The Modern Data Report 2026 explores what's blocking data activation and how to fix it.

Data products are the foundation for agentic AI. Discover what makes data agent-ready and how DataOS enables you to build data products that agents can act on.

Discover how The Modern Data Company and Carahsoft are expanding public sector access to DataOS—helping government agencies modernize data, accelerate AI and analytics, and unlock value without costly data migration.

The Modern Data Company announced key leadership appointments, naming Saurabh Gupta as Chief Executive Officer and Co-founder Srujan Akula as head of product strategy and execution to accelerate enterprise adoption of DataOS®.

Build data products that compound, not fragment. Learn the organizing principles for source-aligned, aggregate, and consumer-aligned products.

The Modern Data Company, creator of DataOS®, the fastest, most cost-effective way to build a foundation for enterprise- and AI-ready data, today announced the appointment of Rick Rosenburg as Head of Public Sector.

DataOS' Data Movement captures metadata, provenance, and schema history at ingestion. AI-ready data from the first mile

Learn what data products are, how they differ from datasets, and their essential characteristics. Expert guidance on when to create data products, key benefits, and how they drive business value in modern data architectures

The Modern Data Company, creator of DataOS®, the world's first data operating system built to accelerate AI at enterprise scale, released the 2024–2025 Modern Data Report, revealing that complex data architectures force teams to spend valuable time on infrastructure over strategic impact.

Build resilient supply chains with DataOS. Turn fragmented data into decision-ready data products for faster decisions and real-time response to disruption.

Unite business and tech with a data product strategy that delivers trusted data, aligns to business goals, and empowers faster, smarter decisions at scale.

Lean AI helps enterprises cut cost and complexity by focusing on trusted, purpose-driven data. Learn how DataOS enables faster, smarter AI outcomes.

The Modern Data Company, creator of DataOS establishes Middle East operations in UAE and Saudi Arabia, appoints industry veteran Kunwar Khehar to lead regional growth

Learn how DataOS helps organizations navigate the post-M&A landscape to achieve Day 1 visibility, align business logic, and activate AI-ready data products without waiting on system consolidation.

DataOS is the "Data Solution of the Year - Marketing" in the 6th annual Data Breakthrough Awards.

Discover how The Modern Data Company and CoffeeBeans accelerate AI/GenAI readiness with DataOS—cutting AI development cycles by 90% and delivering results from months to weeks.

The Modern Data Company and Acies Global Partner to Accelerate Enterprise Data Readiness with the DataOS platform. Partnership enables enterprises to build the trusted data layer needed to drive marketing effectiveness, deepen customer insights and set a strong foundation for AI

Modern Data’s approach to data products mirrors DeepSeek’s AI efficiency—prioritizing targeted outcomes over processing everything. By focusing on lean, purpose-driven solutions, enterprises can reduce complexity, cut costs, and accelerate value creation in both data management and AI.

Discover how data teams are trapped in maintenance, rework, and integration challenges, preventing them from delivering business value. Insights from 232 global data practitioners reveal the hidden costs and a path to breaking the cycle.

The Data Product Hub transforms how organizations activate their data by making trusted data products easily discoverable and immediately usable for business teams

Lobos 1707 Tequila amplifies its sales and marketing efficiency with DataOS, unlocking data-driven insights that boost performance and decision-making.

Boost LLM accuracy and reliability by pairing them with high-quality, contextual data products that enhance context, reduce errors, and improve trust in AI.

The complexity of integrating various data sources like CRM, financials, POS, and third-party data often demands specialized skills and significant investment.

There have been constant shifts in alcohol drinking trends across the global markets, and with each new year, a new set of alcohol beverage consumption statistics, trends, and predictions follow.

It typically provides a scalable and flexible infrastructure for storing, processing, and analyzing big data and should also include features that support data management, data protection, and data governance.

A data taxonomy uses a system of unambiguous metadata terms (such as a filename or tags attached to a file) that allow an enterprise to classify a file or dataset into important business categories.

It prohibited states from disenrolling individuals from Medicaid while the public health emergency (PHE) was in effect, a condition of a temporary increase in the federal Medicaid match rate.

The cost structure of the cloud is fundamentally different than that of on-premise platforms, and the difference is not always guaranteed to be in favor of the cloud platform.

One last factor that is tied to the prior two is that Southwest ends up with planes, pilots, and crew scattered everywhere to support its unique style of operation.

The tools are used as a way to enable non-experts to do more for themselves and to free up experts to focus on other things than supporting users’ basic needs.

Despite financial services changing rapidly in response to global disruption and new technology, the industry’s data remains a key protected category in privacy regulations.

This article examines some of the goals and roadblocks on the way to data democratization and shows how DataOS makes the process simpler and, above all, genuine.

This loss of revenue comes from insufficient data to inform and drive business goals. Data quality starts at the source — which is why it’s so important how data is selected, sourced, and moved.

Not all organizations are ready to offload their ETL pipelines. Unless decision-makers are also tech experts, they may not realize that ETL pipelines continue to exist within their data stack.

While this progress will be a great thing in the long run, it has also eclipsed companies’ abilities to effectively scale their current approaches for developing and deploying analytics processes.

Disruptive technology can be a boon for the enterprise, but the implementation approach could be the difference between failure and transformation.

Metadata is a solution to enable collaboration across business units and to make data easier to find and use. Metadata (documentation, queries, history, glossaries, etc.) makes data understandable.

Companies must make strategic decisions to unlock their data for real-time use to everyone—yes, everyone—in the company that needs it. Here's what to know.

Most advice centers around internal silos, i.e., departments not sharing their data with each other. For retail and CPG, the equation also includes external silos.For