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

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Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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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

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...