2026 DataOps Predictions - Part 2

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms.

AGENT-READY DATA STACK

The enterprise data stack will become "agent-ready" by default. By the end of 2026, connectivity, governance, and context provisioning for AI agents will be built into every serious data platform. SQL and open protocols like MCP will sit side by side, allowing both humans and machines to query, act, and collaborate safely within the same governed data plane. 
Tyler Akidau
CTO, Redpanda

The era of the purely human-built application is officially over. Up to now, AI was an add-on, a feature we used to assist. In the coming year, we will witness the critical pivot where enterprise applications become agentic by default, delegating core, multi-step logic and autonomous action to AI agents. This is the single biggest architectural shift in software development since the move to the cloud, and it means the data infrastructure must evolve from passive storage to a proactive, reasoning partner — aka databases become agentic as well. The success of the agentic era hinges entirely on the database's ability to interact with application agents providing contextually grounded data with ultra-low latency and very high throughout.
Vikas Mathur
Chief Product Officer, MariaDB

THE PUSH-BUTTON ERA OF DATA PLATFORMS

The "Push-Button" Era of Data Platform Capabilities: Complex capabilities that currently require extensive engineering will become push-button features. RAG implementations, multi-engine orchestration, and AI-powered optimizations will be available out-of-the-box rather than requiring months of custom development.
Jags Ramnarayan
Cloud CTO, MariaDB

AI-READY DATA

AI-Ready Data Will Become a Board-Level Priority: "AI-ready" data has been in the headlines for the last few years, because early adopters of AI received a wake-up call: AI is only as powerful as the data that feeds it. Beyond that, they realized that making data "AI-ready" was not necessarily easy. AI-ready data, organizations realized, has to be: high-quality and unified, semantically enriched with business context, delivered to large language models (LLMs) in real time, and subject to active data governance. In 2026, AI-ready data will move into the boardroom and become a top strategic asset.
Paul Moxon
SVP Data Architecture and Chief Evangelist, Denodo

DATA EXPLAINABILITY

Explainable data and models will become mandatory in regulated processes:  Explainability will extend beyond models to include data provenance and  transformation  transparency.  In  2026, regulators in sectors like finance, healthcare and public services will expect organizations to  demonstrate  not only how an AI decision was made, but which data it relied on, how that data was acquired and processed , and who was accountable at each step. 
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

DATA CONTROL TOWER

The data catalog will evolve into the Data Control Tower: Today's data catalogs are static inventories. In 2026, they will become active control planes for enterprise data, cataloging not just what data exists but how it is used, by whom, and for what purpose. These systems will guide agents and users to trusted sources, verify data lineage and integrity in real time, and ensure usage aligns with governance policies. The Data Control Tower will bridge human oversight with machine-driven execution, giving enterprises full visibility and control across the data lifecycle. For CIOs, this marks the rise of a new operational layer where data sensemaking, compliance, and context drive responsible, scalable outcomes.
Juan Sequeda
Principal Researcher, ServiceNow

ONTOLOGY

Ontology Will Replace CMDB (Configuration Management Database) as the Enterprise Source of Truth: In the next 24–36 months, static CMDBs will give way to dynamic ontology-based reference systems that continuously reflect the real-time state of the enterprise. Ontologies will capture relationships, intents, and outcomes — turning configuration data into living intelligence that powers reasoning, explainability, and autonomous decision-making. This shift will remove one of the most persistent bottlenecks in enterprise operations.
Casey Kindiger
CEO, Grokstream

THE METADATA LAYER

The Metadata Layer Will Become the Next Battleground for Data Leadership: In 2026, the metadata layer will emerge as the critical control plane for modern data architecture. As open table formats like Apache Iceberg gain widespread adoption, and open source catalogs continue to mature, the abstraction of metadata from storage and compute has become not just possible — but essential. The organizations leading in data are no longer those with the biggest lakehouses, but those who can unify governance, discovery, and access across fragmented data ecosystems. The metadata layer is now where trust, transparency, and agility are won or lost. It's the battleground for data leadership, and open standards are the strategic advantage. In 2026, this architectural shift will be the key differentiator, separating the market leaders from those left behind.
Chris Child
VP of Product, Data Engineering, Snowflake

DEATH OF THE DATABASE

Applications built primarily to store relational data are facing a dramatic decline in relevance, signaling the death of the database in 2026. AI agents and natural-language interfaces are taking over the work of capturing, retrieving, and interpreting information. Only databases that deliver transformative value and/or support analysis processes will remain central to daily workflows. As agents pull data directly from systems of engagement, like email, quoting, contracting, CRM tools are reduced to a backend database, no longer a place which users actively log into. This shift persists across enterprise software, where diminishing user logins undermine traditional per-seat pricing models and fundamentally reshape how these platforms are valued.
John Bruno
VP of Strategy, PROS

DATA PLATFORM CONSOLIDATION

To keep pace with AI-driven demands, organizations will reduce vendors and consolidate data platforms. AI-enabled tools will help streamline architectures, eliminating redundant systems and minimizing the "moving parts" in enterprise data environments.
Michael Curry
President of Data Modernization, Rocket Software

OPEN DATA FORMATS

The Year The C-Suite Embraces Open Data Formats to Future-Proof Their AI Strategy: 2026 is the year the C-suite embraces open formats as the foundation for AI. While engineers have long favored open formats for their flexibility and interoperability, business leaders have been wary — concerned about complexity and enterprise readiness. But that narrative is shifting. Open standards like Apache Iceberg™ are proving essential to simplifying data architectures, eliminating vendor lock-in, and enabling a single copy of data to power multiple engines. Open formats allow organizations to move faster, reduce costs, and stay in control of their data strategies. In a rapidly evolving AI landscape, they offer the adaptability and innovation velocity enterprises need to compete, and win.
Chris Child
VP of Product, Data Engineering, Snowflake

OPEN DATA LAKES

Centralizing AI-Ready Data in an Open Data Lake: In 2026, the biggest bottleneck to enterprise AI won't be model quality, but fragmented data. Companies still can't unify the operational, observability, and business data needed for AI to understand how machines, people, and external factors interact. Expect a rapid shift toward data lakes that support open data formats, such as Apache Iceberg, as they become the default for centralizing and governing data at scale. This move will transform today's chaotic "big data" into the consistent, connected, AI-ready foundation required for automation, prediction, and real-time decision-making."
Jacob Leverich
Cofounder and CTO, Observe

LOGICAL DATA MANAGEMENT

Logical Data Management Will Replace "One Big Lake" Strategies: For years, organizations have been attempting to consolidate data. These efforts have become increasingly effective, with the advent of cloud technologies that support highly flexible scalability and provide expanded support for disparate data types. However, in this age that is increasingly dominated by AI and the need for AI-ready data (back to #1 again), these "centralized lake ambitions" are beginning to fade. This is because some data will always reside outside of the main data lake, such as data in a secondary cloud system, and it simply takes time to replicate it. Increasingly, organizations are turning to logical data management, to access data where it lives — across multicloud, hybrid, or sovereign environments — without having to always first replicate the data into the core repository.
Paul Moxon
SVP Data Architecture and Chief Evangelist, Denodo

NATURAL LANGUAGE

Natural Language Will Dominate Database Interactions: SQL won't disappear, but it will become an artifact rather than the primary interface. Developers, analysts, and operators will interact with databases through natural language, with platforms automatically translating requests into SQL and providing explanations. Every database platform will need embedded semantic layers that understand schemas, relationships, and business terminology, plus planning capabilities to decompose complex requests into executable steps.
Jags Ramnarayan
Cloud CTO, MariaDB

INSTANT RAG

"Instant RAG" Will Become Table Stakes: RAG (Retrieval-Augmented Generation) capabilities will be built directly into database platforms rather than requiring separate systems. Platforms will natively ingest documents, embed and index them, and make them joinable with traditional row data. This convergence means a single query can seamlessly touch both documents and tables, returning answers with citations and confidence scores.
Jags Ramnarayan
Cloud CTO, MariaDB

Check back tomorrow for Data Center predictions

Related Links

Hot Topics

The Latest

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Why VM and Container Monitoring Is Not Enough for Cloud-Hosted Autonomous Agents

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Beyond Monitoring: Why IT Operations Must Evolve into Decision Operations

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...

2026 DataOps Predictions - Part 2

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms.

AGENT-READY DATA STACK

The enterprise data stack will become "agent-ready" by default. By the end of 2026, connectivity, governance, and context provisioning for AI agents will be built into every serious data platform. SQL and open protocols like MCP will sit side by side, allowing both humans and machines to query, act, and collaborate safely within the same governed data plane. 
Tyler Akidau
CTO, Redpanda

The era of the purely human-built application is officially over. Up to now, AI was an add-on, a feature we used to assist. In the coming year, we will witness the critical pivot where enterprise applications become agentic by default, delegating core, multi-step logic and autonomous action to AI agents. This is the single biggest architectural shift in software development since the move to the cloud, and it means the data infrastructure must evolve from passive storage to a proactive, reasoning partner — aka databases become agentic as well. The success of the agentic era hinges entirely on the database's ability to interact with application agents providing contextually grounded data with ultra-low latency and very high throughout.
Vikas Mathur
Chief Product Officer, MariaDB

THE PUSH-BUTTON ERA OF DATA PLATFORMS

The "Push-Button" Era of Data Platform Capabilities: Complex capabilities that currently require extensive engineering will become push-button features. RAG implementations, multi-engine orchestration, and AI-powered optimizations will be available out-of-the-box rather than requiring months of custom development.
Jags Ramnarayan
Cloud CTO, MariaDB

AI-READY DATA

AI-Ready Data Will Become a Board-Level Priority: "AI-ready" data has been in the headlines for the last few years, because early adopters of AI received a wake-up call: AI is only as powerful as the data that feeds it. Beyond that, they realized that making data "AI-ready" was not necessarily easy. AI-ready data, organizations realized, has to be: high-quality and unified, semantically enriched with business context, delivered to large language models (LLMs) in real time, and subject to active data governance. In 2026, AI-ready data will move into the boardroom and become a top strategic asset.
Paul Moxon
SVP Data Architecture and Chief Evangelist, Denodo

DATA EXPLAINABILITY

Explainable data and models will become mandatory in regulated processes:  Explainability will extend beyond models to include data provenance and  transformation  transparency.  In  2026, regulators in sectors like finance, healthcare and public services will expect organizations to  demonstrate  not only how an AI decision was made, but which data it relied on, how that data was acquired and processed , and who was accountable at each step. 
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

DATA CONTROL TOWER

The data catalog will evolve into the Data Control Tower: Today's data catalogs are static inventories. In 2026, they will become active control planes for enterprise data, cataloging not just what data exists but how it is used, by whom, and for what purpose. These systems will guide agents and users to trusted sources, verify data lineage and integrity in real time, and ensure usage aligns with governance policies. The Data Control Tower will bridge human oversight with machine-driven execution, giving enterprises full visibility and control across the data lifecycle. For CIOs, this marks the rise of a new operational layer where data sensemaking, compliance, and context drive responsible, scalable outcomes.
Juan Sequeda
Principal Researcher, ServiceNow

ONTOLOGY

Ontology Will Replace CMDB (Configuration Management Database) as the Enterprise Source of Truth: In the next 24–36 months, static CMDBs will give way to dynamic ontology-based reference systems that continuously reflect the real-time state of the enterprise. Ontologies will capture relationships, intents, and outcomes — turning configuration data into living intelligence that powers reasoning, explainability, and autonomous decision-making. This shift will remove one of the most persistent bottlenecks in enterprise operations.
Casey Kindiger
CEO, Grokstream

THE METADATA LAYER

The Metadata Layer Will Become the Next Battleground for Data Leadership: In 2026, the metadata layer will emerge as the critical control plane for modern data architecture. As open table formats like Apache Iceberg gain widespread adoption, and open source catalogs continue to mature, the abstraction of metadata from storage and compute has become not just possible — but essential. The organizations leading in data are no longer those with the biggest lakehouses, but those who can unify governance, discovery, and access across fragmented data ecosystems. The metadata layer is now where trust, transparency, and agility are won or lost. It's the battleground for data leadership, and open standards are the strategic advantage. In 2026, this architectural shift will be the key differentiator, separating the market leaders from those left behind.
Chris Child
VP of Product, Data Engineering, Snowflake

DEATH OF THE DATABASE

Applications built primarily to store relational data are facing a dramatic decline in relevance, signaling the death of the database in 2026. AI agents and natural-language interfaces are taking over the work of capturing, retrieving, and interpreting information. Only databases that deliver transformative value and/or support analysis processes will remain central to daily workflows. As agents pull data directly from systems of engagement, like email, quoting, contracting, CRM tools are reduced to a backend database, no longer a place which users actively log into. This shift persists across enterprise software, where diminishing user logins undermine traditional per-seat pricing models and fundamentally reshape how these platforms are valued.
John Bruno
VP of Strategy, PROS

DATA PLATFORM CONSOLIDATION

To keep pace with AI-driven demands, organizations will reduce vendors and consolidate data platforms. AI-enabled tools will help streamline architectures, eliminating redundant systems and minimizing the "moving parts" in enterprise data environments.
Michael Curry
President of Data Modernization, Rocket Software

OPEN DATA FORMATS

The Year The C-Suite Embraces Open Data Formats to Future-Proof Their AI Strategy: 2026 is the year the C-suite embraces open formats as the foundation for AI. While engineers have long favored open formats for their flexibility and interoperability, business leaders have been wary — concerned about complexity and enterprise readiness. But that narrative is shifting. Open standards like Apache Iceberg™ are proving essential to simplifying data architectures, eliminating vendor lock-in, and enabling a single copy of data to power multiple engines. Open formats allow organizations to move faster, reduce costs, and stay in control of their data strategies. In a rapidly evolving AI landscape, they offer the adaptability and innovation velocity enterprises need to compete, and win.
Chris Child
VP of Product, Data Engineering, Snowflake

OPEN DATA LAKES

Centralizing AI-Ready Data in an Open Data Lake: In 2026, the biggest bottleneck to enterprise AI won't be model quality, but fragmented data. Companies still can't unify the operational, observability, and business data needed for AI to understand how machines, people, and external factors interact. Expect a rapid shift toward data lakes that support open data formats, such as Apache Iceberg, as they become the default for centralizing and governing data at scale. This move will transform today's chaotic "big data" into the consistent, connected, AI-ready foundation required for automation, prediction, and real-time decision-making."
Jacob Leverich
Cofounder and CTO, Observe

LOGICAL DATA MANAGEMENT

Logical Data Management Will Replace "One Big Lake" Strategies: For years, organizations have been attempting to consolidate data. These efforts have become increasingly effective, with the advent of cloud technologies that support highly flexible scalability and provide expanded support for disparate data types. However, in this age that is increasingly dominated by AI and the need for AI-ready data (back to #1 again), these "centralized lake ambitions" are beginning to fade. This is because some data will always reside outside of the main data lake, such as data in a secondary cloud system, and it simply takes time to replicate it. Increasingly, organizations are turning to logical data management, to access data where it lives — across multicloud, hybrid, or sovereign environments — without having to always first replicate the data into the core repository.
Paul Moxon
SVP Data Architecture and Chief Evangelist, Denodo

NATURAL LANGUAGE

Natural Language Will Dominate Database Interactions: SQL won't disappear, but it will become an artifact rather than the primary interface. Developers, analysts, and operators will interact with databases through natural language, with platforms automatically translating requests into SQL and providing explanations. Every database platform will need embedded semantic layers that understand schemas, relationships, and business terminology, plus planning capabilities to decompose complex requests into executable steps.
Jags Ramnarayan
Cloud CTO, MariaDB

INSTANT RAG

"Instant RAG" Will Become Table Stakes: RAG (Retrieval-Augmented Generation) capabilities will be built directly into database platforms rather than requiring separate systems. Platforms will natively ingest documents, embed and index them, and make them joinable with traditional row data. This convergence means a single query can seamlessly touch both documents and tables, returning answers with citations and confidence scores.
Jags Ramnarayan
Cloud CTO, MariaDB

Check back tomorrow for Data Center predictions

Related Links

Hot Topics

The Latest

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Why VM and Container Monitoring Is Not Enough for Cloud-Hosted Autonomous Agents

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Beyond Monitoring: Why IT Operations Must Evolve into Decision Operations

Enterprise IT environments have never been more observable ... Yet many organizations still grapple with outages, lengthy incident resolution cycles, and increasing complexity. Most teams do not suffer from a shortage of data. They struggle to determine what deserves attention and what action to take next ... Enterprise IT operations must move beyond monitoring and visibility. The next stage of maturity is decision operations, an approach that helps teams make faster, better-informed decisions ...