Data has always been important for technology companies. But in 2026, its role has become much bigger. The rise of artificial intelligence has made good data a basic need for almost every serious AI system. A smart model cannot do much if the data behind it is old, wrong, incomplete or hard to understand.
This has created a new wave of data infrastructure startups. These companies do not simply help businesses store or move data. They help companies clean it, check it, organize it, explain it and make it ready for AI.
The market is also changing fast. Large data companies are joining forces, new startups are raising large amounts of money, and AI companies are paying more for special datasets. All of this points to one simple idea: clean data still wins.
AI Has Raised the Value of Good Data
For many years, companies built data systems mainly for reports, dashboards and business analysis. A human analyst could see a strange number and ask an engineer to check it. There was usually a person in the process who could catch an error before it caused serious harm.
AI agents change this model.
An AI agent can use company data on its own. It can search records, read reports, make a decision and take an action. It may do all of this at machine speed and across many systems. If the data is wrong, the agent may also make the wrong choice.
This creates a much bigger need for data that is fresh, reliable, governed and easy for machines to understand.
Fivetran and dbt Labs made this point very clear after their merger in June 2026. The combined company said AI agents need data that is reliable, fresh, governed and accessible across an enterprise. The two companies now aim to provide a data foundation for trusted AI agents.
The message is simple. The AI model is only one part of the system. The quality of the data behind that model can decide how useful the final result will be.
Fivetran and dbt Labs Show Where the Market Is Going
One of the biggest developments in the data infrastructure market this year was the completion of the Fivetran and dbt Labs merger.
The deal became official on June 1, 2026. The combined company said it serves more than 100,000 data teams around the world. Fivetran brings data movement and integration, while dbt brings data transformation, testing, business logic and data models.
The two businesses fit together in a very clear way. Fivetran helps move data from different systems into a common environment. dbt helps companies define and test that data so teams can trust it.
This matters because companies do not just need more data. They need data with clear meaning.
For example, a company may have one system that calls a customer “active” after a purchase and another system that uses the same word only after a subscription renewal. An AI agent that does not understand this difference can reach the wrong conclusion.
Data models, business rules and shared definitions can help solve this problem.
The merger also shows another trend in the market. Data companies are moving closer to a full data lifecycle. Instead of separate tools for data movement, transformation, quality and AI context, buyers want systems that work together.
Clean Data Is More Than Error-Free Data
When people hear the words “clean data,” they may think about removing duplicate names or fixing incorrect numbers. That is still important, but the meaning of clean data has become much wider.
Modern AI systems need data that is accurate, fresh and complete. They also need to know where the data came from and what each field means.
This is why data quality now connects with governance, lineage, observability and the semantic layer.
Lineage tells a company where data came from and what happened to it before it reached its final location. Governance sets rules around access and use. A semantic layer gives common business meaning to important data.
Together, these systems give AI more context.
Fivetran and dbt Labs have even introduced an open-source Agents Schema. It places metric definitions, semantic models, lineage and business documentation in a shared schema that AI agents can use as context.
That is an important shift. Data infrastructure is no longer only about storage and movement. It is also about helping machines understand what the data means.
Startups Are Trying to Automate Data Work
This change has opened space for new startups.
Upriver, for example, is focused on AI agents for data engineering. Its recent work covers agents that can detect pipeline failures, collect context, find root causes and automate parts of data workflows across tools such as dbt, Airflow and Snowflake.
This approach addresses a common problem in modern data teams. Data systems can become very complex, and engineers may spend a large part of their time fixing broken pipelines, checking data quality and tracing problems across different systems.
The next generation of tools wants to reduce that manual work.
Instead of a system that only tells an engineer that something is wrong, the new model aims to explain the problem and help fix it.
That could make data teams faster and also reduce the cost of maintaining large data systems.
Data Observability Is Also Changing
Data observability has become another major part of the market.
Tools in this category help companies check whether data is fresh, complete and within expected limits. Matia, for example, lets users monitor freshness, data volume, schema changes and custom conditions. Its system can create monitors for table freshness and row counts, while users can also set their own checks.
This matters because data problems often remain hidden until someone needs the data.
A broken application may show an error right away. A broken data pipeline may quietly send incorrect information to a report or AI system for hours or even days.
Observability can help companies find these problems earlier.
The market is also seeing more overlap between data quality, data observability, governance and data catalogs. As these categories move closer together, startups may need to offer more than a simple alert system.
The larger opportunity is a system that can find a problem, explain its cause, suggest a fix and confirm that the data is correct after the fix.
Large Funding Shows the Value of Specialized Data
The demand for good data is not limited to enterprise databases.
A major example came on September 22, 2026, when Snorkel AI raised $350 million at a $3.5 billion valuation. Reuters reported that the company had an annualized revenue run rate above $350 million, compared with about $20 million a year earlier.
Snorkel began as a research project at Stanford and first focused on software for data development. Its business has since moved toward data-as-a-service, finished datasets and reinforcement-learning environments for AI customers. Snorkel said its annualized revenue run rate had reached $375 million when it announced the new funding.
The change tells us something important about the AI market.
AI companies do not always need more generic data. They often need special data that fits a specific task. They may need expert knowledge, strong evaluation data, or carefully prepared environments for model training.
That kind of data can be difficult to create. It may require experts, detailed checks and a clear understanding of the final use case.
As AI models become more capable, the value of such data can rise.
Observability Is Moving Closer to AI
The same pattern can be seen in the wider observability market.
Coralogix raised $200 million in Series F funding in June 2026, bringing its total funding to $550 million. The company describes its platform as a data and AI platform for observability and says its tools are built for a world where AI agents and human engineers work with data together.
This shows how the boundary between data infrastructure and AI infrastructure is becoming less clear.
A modern AI system needs information about models, applications, databases, pipelines and user activity. Companies want to understand what happened when an AI system produces an unexpected result.
Observability can provide that visibility.
As a result, data infrastructure companies can become part of the larger AI operations stack.
The Market Is Also Seeing Consolidation
There is another major trend: consolidation.
Fivetran and dbt Labs chose to merge rather than remain separate companies. The deal brings data integration and data transformation under one business.
Other parts of the market have also seen large companies buy specialist vendors. TechTarget notes that Informatica became part of Salesforce, Confluent was acquired by IBM, and Dremio was acquired by SAP.
This creates a difficult market for small startups.
Large cloud and data companies already have strong distribution, large customer bases and deep technical resources. A small startup cannot always compete by offering one narrow feature.
At the same time, there is still room for independent companies. A specialist can build a strong position if it solves a difficult problem that larger platforms do not handle well.
The key may be integration. A startup does not always need to replace the main data platform. It can become an important layer that works with the systems companies already use.
Why Clean Data Still Has an Edge
The core idea behind the current market is quite simple.
AI can process huge amounts of information, but it does not automatically know which information is correct.
A model may produce a confident answer from a bad database record. An agent may take an action based on an old customer profile. A business system may contain two definitions for the same metric.
More compute does not automatically solve these problems.
Better models do not remove the need for reliable data either.
This is why clean data remains so valuable.
The definition of clean data has also changed. It now means more than correct values. It can mean data that is accurate, fresh, complete, well-defined, traceable, secure and fit for a specific AI task.
What Comes Next for Data Infrastructure Startups
The next phase of the market may focus less on basic data movement and more on automatic data reliability.
A startup that can only move data may face strong competition from large platforms. A startup that can understand why data is wrong, trace the source, fix the issue and prove that the result is correct can offer a much deeper service.
There is also a clear opportunity around AI context.
AI agents need access to business definitions, company rules, customer records, product information and historical data. They need this information in a form that they can understand and use safely.
That creates space for tools that connect data quality with context.
The same applies to data provenance. Companies now care more about where datasets came from, whether they have the right to use them and whether the information can support a commercial AI system.
In this new market, trust can become a major part of the product.
The Bigger Picture
The data infrastructure market is moving through a major change.
The old goal was to collect and store more data. The newer goal is to make that data useful, trusted and ready for AI.
The Fivetran and dbt Labs merger shows how data movement and data quality are coming closer together. The rise of AI data companies such as Snorkel shows that specialized datasets can have very high value. New tools from companies such as Upriver show how AI can help data teams handle complex engineering work. Coralogix shows how observability is also moving toward the AI era.
For startups, this creates both a challenge and an opportunity.
The challenge is that large platforms are moving into more parts of the data stack. The opportunity is that AI has created new problems that still need focused solutions.
The central lesson is clear: bad data can make advanced AI less useful, while trusted data can make the same technology far more valuable.
That is why clean data has not lost its importance in the AI era.
It has gained it.
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