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DataOS Blog

Learn how minimum viable context helps enterprises build AI-ready data for agents by starting with one business decision instead of a massive enterprise ontology.

An AI agent can only tell its data isn’t fit for purpose if the context layer records what analysts know. Here are the six fitness checks it needs.

Five takeaways from Money20/20 on how credit unions are approaching AI, member experience, and trusted data.

AI needs more than access to enterprise data. It needs to understand what that data means and how business concepts connect. Learn how data ontology provides this context, why it matters for AI, and how DataOS turns ontology into an operational layer built on governed data products.

At DIR Discover 2026, Texas agencies asked about data governance, lineage, and access. Here’s what we heard and what it means for public sector AI.

At Cypher 2026, we explored how DataOS brings business context and governance to enterprise data so AI can deliver answers businesses can trust.

Golden questions get an analytics agent to launch, but not to trust. Learn how to build an evaluation set, score correctness, and certify each domain.

Most LLM cost advice stops at the model. This checklist covers the data-layer fixes teams skip (caching, semantic layers, governance).

DataOS achieves FedRAMP Class D (High) certification through Second Front, giving federal agencies a faster path to a secure, AI-ready data foundation.

Discover why outcome-first, right-to-left thinking helps businesses accelerate results, maximize data and AI investments, and avoid technology spending that fails to deliver value.

A good metric reflects purpose, drives action, and has an owner. Here is how good and bad metrics differ, and what Wells Fargo’s cross-sell target teaches.

Data readiness for AI execution means more than clean data. Here’s the seven-stage data architecture teams need, and why public sector agencies stall midway.

Ten priorities, one data foundation. See how DataOS, TX-RAMP Level 2 certified, helps state agencies deliver on NASCIO’s Top 10 State CIO Priorities, from AI to cloud.

Build business acumen in data teams by connecting analytics to customer value, revenue, and business goals. Learn four questions that drive better decisions.

Usage-based pricing can reward activity over results. Learn why data and AI vendors need to align consumption with customer outcomes, not just usage.

Bad data drives up LLM API costs through repeated queries and wasted tokens. Learn how data governance and DataOS reduce unnecessary AI spend.

Learn how ontologies, knowledge graphs, semantic layers, and context layers differ—and how each supports governed, reliable enterprise AI.

Key takeaways from our Mumbai roundtable with 11 enterprise leaders on AI context, cost, data readiness, and moving from experiments to business outcomes.

AI is accelerating experimentation, but speed without trusted data can amplify risk. Learn how governed, reusable data helps teams reach conviction faster.

Customer 360 AI often fails on stale, fragmented data. Here’s why the customer 360 architecture breaks under AI agents, and what fixes personalization at scale.

Government agencies face the same AI and data trends as the private sector, often in a sharper form. A public sector read of the findings from The Modern Data Company's 2026 interim report.

The Modern Data Company Launches DataOS for Public Sector at the State of Texas DIR Innovation Lab for Demonstrations

Learn why financial institutions need a context layer to give AI the meaning, history, policies, and lineage required for defensible decisions.

AI is raising the standard for enterprise data. Explore why trusted data, engineered context, traceability, and governance are essential for AI agents.

Token prices keep falling, but AI bills keep rising. See where LLM tokens are spent, how the data layer enables LLM token optimization, and how to audit your own spend.

Move beyond consulting-led data modernization with a reusable, governed data foundation that accelerates enterprise AI while reducing cost and dependency.

Learn how data products differ from datasets and pipelines—and why ownership, governance, quality, and context make data reusable and AI-ready.

New survey findings show 57.3% of organizations are piloting or running AI agents, while only 8.4% say their data is production-ready.

Named AI cost overruns at Uber and Microsoft show why spending caps alone cannot govern agent costs. Runtime attribution is what actually works.

Explore how enterprises can manage token costs, improve AI ROI, and use governed data products and semantic models to build efficient AI systems.

Company Brain needs a semantic layer, governance, and lineage before agents can act on it. Here's the half of the stack data platforms already own.

Learn why data debt goes beyond broken pipelines and stale documentation, and how lost context, diluted ownership, and unquestioned metrics can weaken decision-making in the AI era.

Learn how modern data architecture powers high-performing Agentic AI by providing trusted data, context, governance, and seamless execution.

The Modern Data Company Achieves TX-RAMP Level 2 Certification for DataOS.

Data products fail not because engineers build wrongly, but because business intent erodes before it reaches them. Here is the structural fix.

Most data products are measured by usage instead of outcomes. Here is how metric trees, built on DataOS, connect your data products to the business results that matter.

Learn why rising AI costs aren’t just about token prices, and how governed, contextualized data can make AI agents cheaper, faster, and more reliable.

Manufacturing teams lack a shared language across quality, procurement, and plant ops. Here's what a real quality command center fixes.

AI token prices are falling, but enterprise AI costs are rising. Learn how agentic AI, Jevons Paradox, and token economics are reshaping AI architecture.

Learn why AI needs more than accessible data. Explore how embedded business context and data products make enterprise data easier for AI to understand and trust.

Explore why enterprise AI gets answers wrong and how governed, context-native data products help reduce hallucinations, improve trust, and make AI more reliable.

The Modern Data Company and Leidos partner to organize fragmented federal data into actionable insights.

AI agents thrive on context, not just data. Explore how DataOS delivers governed, semantically rich data products that improve AI accuracy while reducing cost and complexity.

Banks are sitting on a powerful data stack-but can't activate it yet. See how adding the missing layer changes what's possible.

Context is the new currency for AI readiness. Learn how a context layer helps AI agents understand enterprise data reliably and at scale.

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.

Data complexity is killing ROI. Discover how agile, reusable architectures reduce friction and unlock real value from your existing data stack.

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

Data modeling challenges persist because most strategies are built wrong. Explore how to redesign your approach and deliver real business value.

Building a data product machine is less about tech and more about strategy. Here's how to reduce complexity and move faster with your data.

Rigid data architecture is the silent killer of innovation. See why evolutionary design keeps data-driven organizations scalable, flexible, and ready.

Just-in-Time has come to data. See how data products replace slow, batch pipelines with agile, on-demand insights for faster business decisions.

Slow data pipelines kill innovation. See how data products accelerate development cycles and unlock faster, more confident decision-making.

Recognized in Gartner's Magic Quadrant for Data Integration—see how the platform transforms enterprise data management and what recognition means for you.

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.

Inside MIT CDOIQ: leaders focused on data products and data mesh as the foundation for AI and business value. Here's what you need to know.

Chaining data products creates compounding value—connecting systems, breaking silos, and enabling use cases no single product could power alone.

Effective data products start with purpose, quality, and design. See what separates data products that get used from those that collect dust.

Retail loyalty starts with knowing your customer. See how activating customer data drives personalization and better experiences at scale.

Choosing a data management solution is complex. Learn how to evaluate data lakes, warehouses, and modern systems based on your actual business needs.

Data lakes, warehouses, lakehouses—each has trade-offs. Explore the pros and cons to choose the right data management architecture for your needs.

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.

Healthcare organizations collect the data—but struggle to use it. See how modern data platforms reinvigorate analytics and improve patient care.

Data debt compounds like technical debt—and costs more than most realize. Learn how to identify, manage, and reduce it before it stalls growth.

A smarter approach to technical debt payoff-structured prioritization, legacy system visibility, and a clear path to smoother data migration.

Technical debt doesn't get cheaper with time. Start paying it down today with practical steps to reduce risk and modernize without disruption.

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.

Self-service analytics promises a lot—but scaling it safely is harder than it looks. Explore what it actually takes to make it work.

Your most-read posts of 2022—Customer 360, data integration, and the enterprise data challenges you were most focused on solving.

CDPs struggle with silos, governance, and tech debt. See how a data operating system fixes the gaps and makes your customer data platform actually work.

Banking and capital markets are competing on customer experience. See how real-time data and analytics power the next generation of financial services.

System outages during the holidays are costly and avoidable. Learn why data systems fail and what organizations can do to stay resilient.

Gartner's automation prediction revisited—how data science, citizen analysts, and automation actually evolved vs. early industry expectations.

Fragmented public health data costs lives. See how a data operating system creates the interoperability and standards needed for real crisis response.

Gartner's 2022 data trends mapped to real enterprise outcomes—how data fabric, AI, and cloud systems moved from prediction to production.

HIPAA-compliant cloud is doing more than protecting data—it's driving healthcare innovation, improving patient care, and enabling new capabilities.

Gartner's 2023 strategic tech trends decoded—how digital immune systems, AI TRiSM, and observability translate into enterprise performance gains.

The true cost of data at scale—how platforms managing billions of interactions balance performance, cost, and complexity. Is your ROI stacking up?

AI or automation—does the difference matter? Understand the key distinctions and how businesses are using both to drive faster, smarter decisions.

Digital transformation in manufacturing starts with data. See how smart factories are boosting efficiency, cutting costs, and driving real ROI.

Manufacturing's next competitive edge is data. Learn how predictive maintenance, efficiency gains, and smarter decisions start with better data.

Cybersecurity gaps often start with data. Explore why a data operating system is becoming essential for stronger, smarter enterprise security.

Supply chain failures cost billions annually. See how real visibility across suppliers reduces risk, eliminates bottlenecks, and builds resilience.

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.

When data strategy and business strategy are misaligned, decisions suffer. Here's how to close the gap and drive real efficiency.

Modern at the AI & Big Data Expo—how decision ops platforms improve discovery, governance, and data activation across the enterprise lifecycle.

Supply chain data management has moved beyond visibility—it's now about value. See how intelligent data systems improve resilience, speed, and decisions.

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.

Six functions every modern data platform must deliver—and how getting them right reduces fragmentation, improves efficiency, and unifies your data.

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.

The story of our brand is the story of Modern. See how a new way of thinking about data and results drove a bold refresh from the inside out.

Modern is evolving beyond data fabric. See how our bold rebrand signals a new era in enterprise data architecture and what's possible now.

Digital transformation needs a data model to match. See why strong modeling is the foundation for clarity, governance, and enterprise consistency.

Customer lifetime value is hard to measure—and harder to improve. See how a unified data platform helps you close the gap and build real loyalty.

What MIT CDOIQ revealed about the future of data—key trends in data quality and how a data operating system reshapes enterprise data strategy.

66% of customers expect better experiences—and fragmented data operations are what's holding most companies back. See how unified ops changes that.

CPG data trends for 2022 all point to one thing: simplify. See how leading consumer goods companies are getting faster with real-time data insights.

Retail BI is delivering more value than ever. Discover how data-driven insights are improving stores, personalization, and the shopping experience.

Learn how to assess the value of enterprise data, unlock it from silos, and turn unused information into better decisions, innovation, and growth.

A new era of data governance is here. See how a data operating system enables secure access, compliance, and flexible data flow at enterprise scale.

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.

Three ways we think differently about data—self-serve pipelines, unified ownership, and adaptive data products that drive real decisions.

Legacy systems aren't the problem—your current architecture might be. See why what you build next matters more than what you replace.

The core principles of a modern data platform—self-service, agility, and scalability—and how they translate into infrastructure that delivers.

Incomplete shipment data costs billions. See how better data sharing solves distribution inefficiencies and unlocks real-time supply chain insights.

Data silos and reporting delays cost operations teams time and money. Learn how to unlock your data for faster, more reliable operational reporting.

Inventory chaos is expensive. Discover how real-time data and AI-powered forecasting reduce costs, improve accuracy, and fix supply chain gaps.

Retailers can't afford a breach. Learn how a security-first data architecture protects customer data, breaks silos, and enables personalization at scale.

Retail legacy systems carry hidden costs that compound over time. Learn how to reduce them, unlock data value, and modernize without a full replacement.

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

Healthcare data is growing faster than organizations can manage. See how unified data systems improve patient care, privacy, and decision-making.

Security without integrity isn't enough. Discover why healthcare needs both to ensure accurate records, protect patients, and drive better outcomes.

Data lakes are entering their next phase. Explore how modern architectures improve governance, real-time streaming, and access for enterprise scale.

Four pillars, one operating system for data. See how the right foundation drives scalability, automation, governance, and real-time business insights.

Supply chains are a top attack target. Learn how Zero Trust security protects IoT devices, networks, and critical industrial systems from growing threats.

Services alone won't solve modern data complexity. See why enterprises need a unified solution—not more fragmented tools and costly integrations.

Most enterprise data never gets used. Learn how regular data health checks improve completeness, accuracy, and usability across your organization.

Collaborative intelligence makes businesses more agile and innovative. Explore how shared data and human insight together future-proof the enterprise.

Most companies collect data but struggle to act on it. Explore four ways to build analytics capabilities that drive real, confident decisions.

The drawback to this model is that the raw data will not have the security and quality protocols applied and it will be incumbent upon the user or application accessing the raw data to apply any security and quality filters.

Data fabric is changing how enterprises handle privacy—enabling secure, responsible sharing without sacrificing insights or user trust.

A bulletproof data strategy isn't just technical—it requires governance, culture, and collaboration. Here's how to build one that sticks.

Poor data health has a real cost—in missed analytics, bad decisions, and wasted resources. See what better quality, timeliness, and access unlock.

Discover how DataOS can both transform your data governance and enable real-time insights without disrupting operations.

Siloed apps and fragmented systems make integration a constant headache. Discover why data integration worries don't have to be permanent.

Data can be risky. Governance in the era of big data is a challenge. However, all departments should become shareholders of the data they need to make decisions.

Most enterprises have more data than they know what to do with. Learn why turning data into real-time value is still where companies get stuck.

Four lessons from 2020 on resilience, innovation, and what it actually takes to make data-driven decisions in a fast-changing environment.

Smart cities run on data. Explore how technology and analytics are transforming urban design and city management into more livable environments.

With 2020 nearing its end, many businesses are reflecting on the past year -- the good, the not-so-good, the successes, the victories, and the “oh, we still need to do that”.

Startups are moving at the speed of real-time data because they can — they’re new and don’t have years of legacy systems that are both necessary and causing bottlenecks.

We're telling you right now that you can't keep your data locked up. Your teams need it, and without it, you won't find insight and value from your data, only information.

Not all data fabrics are equal—many are just tools rebranded. Explore what a real data fabric looks like and why it matters for enterprise strategy.

Data is a 30% tech problem and 70% people problem. A successful data company can’t be built without immense empathy towards the customer and the people issues within customer organizations.

Normalization is a data management strategy designed to structure data in a series of normal forms. This is more than cleaning up duplicates or finding missing data.