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

Crisis Communications: When the Outage Hits, Your Communications Can't Be "Investigating"

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The Real Reason Enterprise AI Stalls: It Doesn't Fit in the Box

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Turning Foresight into Resilience: Reclaiming Prevention in the Age of Exposure

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

MLOps Meets DataOps: Creating Unified Backbone for AI

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

2026 Network Monitoring Trends

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

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

Crisis Communications: When the Outage Hits, Your Communications Can't Be "Investigating"

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The Real Reason Enterprise AI Stalls: It Doesn't Fit in the Box

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Turning Foresight into Resilience: Reclaiming Prevention in the Age of Exposure

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

MLOps Meets DataOps: Creating Unified Backbone for AI

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

2026 Network Monitoring Trends

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...