2025 DataOps Predictions - Part 1

As part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2025.

2025: REAL-TIME DATA IS KEY FOR AI

Real-time data will be a key differentiator for competitive advantage: Industries will increasingly rely on real-time or near real-time data to maintain a competitive edge. Companies that can integrate up-to-date data into their AI systems will provide superior customer experiences with fewer issues and more personalized solutions. The ability to capture and analyze data in real-time will separate industry leaders from those who struggle to modernize their data infrastructure.
Ayman Sayed
CEO, BMC Software

Enterprises Will Augment GenAI with Real-Time Data: The true value of GenAI is realized when integrated into enterprise applications at scale. While enterprises have been cautious with trial deployments, 2025 will be a turning point as they begin to scale GenAI across critical systems like customer support, supply chain, manufacturing, and finance. This will require tools to manage data and track GenAI models, ensuring visibility into data usage. GenAI must be supplemented with specific real-time data, such as vectors and graphs, to maximize effectiveness. In 2025, leading vendors will begin rolling out applications that leverage these advancements.
Lenley Hensarling
Technical Advisor, Aerospike

MULTIMODAL DATA

Multimodal data will be very big, extracting corporate value: Back in 2004, Tim O'Reilly coined the phrase, "Data is the Intel Inside." We don't think quite as much about Intel these days, but Tim was absolutely right about data. We became obsessed with data. We've been talking about data science, being data-driven, and building data-driven organizations ever since. Artificial Intelligence is the current expression of the importance of data.

One problem with being data-driven is that most of any organization's data is locked up in ways that aren't useful. Being data-driven works well if you have nicely structured data in a database. Most companies have that, but they're also sitting on a mountain of unstructured data: PDF files, videos, meeting recordings, real-time data feeds, and more. They aren't even used to thinking of this as data; it's not amenable to SQL and database-centric "business intelligence."

That will change in 2025. It will change because AI will give us the ability to unlock this data as well as the ability to analyze it. It will be able to give structure to the information in PDFs, in videos, in meeting transcripts, and in raw data coming in from sensors. In his Generative AI in the Real World interview, Robert Nishihara asked us to think of the video generated by an autonomous vehicle. Most of that is of limited value — but every now and then, there's a traffic situation that is extremely valuable. Humans aren't going to watch hours of video to extract the value; that's a job for AI. Multimodal AI will help companies to unlock the value of data like this. We're at the start of a new generation of tools for data acquisition, cleaning, and curation that will make this unstructured data accessible.
Laura Baldwin
President, O'Reilly Media

AI DRIVES NEW FOCUS ON DATA QUALITY

AI will renew the focus on data quality, for two reasons: First, high quality data is required for training and fine-tuning models. Second, AI-powered analytics tools will offer a higher-resolution view of data, revealing previously undetected quality issues.
Ryan Janssen
CEO, Zenlytic

Enterprises that ready their data for AI will pull ahead competitively: In 2025, companies will focus on building an organized, high-quality data ecosystem to maximize AI's effectiveness and to pull ahead of their competition. This includes managing metadata through structured data catalogs, ensuring data accuracy with rigorous cleansing and validation, and establishing robust governance practices to safeguard data privacy and security. By implementing clear, ethical guidelines, organizations will create a trustworthy AI framework, empowering data scientists with easy access to reliable data for generating precise, impactful insights across business functions. Enterprises that do this will be hard to compete with. 
Scott Voigt
CEO and Founder, Fullstory

AI DRIVES DATA PIPELINE AUTOMATION

GenAI and as-code first technologies drive data pipeline automation: The ubiquitous use of Kubernetes has led to a configuration-first experience in defining data pipelines. It's as simple as selecting a container image and adding configuration. We'll increasingly see GenAI, trained on processing and execution engines generating this configuration and deploying pipelines automatically through just natural language prompts. Traditional visual ETL tooling, even low code platforms are now at risk of disruption. What a power user could do in a few days (remember you still need to learn these platforms), GenAI does in seconds, spitting out configuration for real-time pipelines. This leads to the question. What is the wider future of any UX if my interface is a prompt? Just view data results and metrics? Engineers may as well be going back to a command line!
Andrew Stevenson 
CTO, Lenses.io

AI-ENHANCED DATA MANAGEMENT AND GOVERNANCE

AI is changing how companies manage and govern their data. Organizations now use data lakehouses to support data scientists and AI engineers working with large language models (LLMs). These lakehouses simplify data access, helping teams avoid juggling multiple storage systems. AI is also helping to automate manual processes like data cleaning and reconciliation—a pain point for many professionals. As AI continues to scale, automated governance will allow companies to manage data more effectively with less manual work.
Emmanuel Darras
CEO and Co-Founder, Kestra

UNIFIED DATA ACCESS AND FEDERATION

A unified approach to data access is high on the agenda for enterprises that plan to consolidate analytics data into a single, accessible source. Data lakehouses support this by providing federated access, allowing teams across the organization to tap into the same data without duplicating it. This approach is expected to drive cross-functional analytics and reduce latency, making it easier for teams to work together on the same shared data.
Emmanuel Darras
CEO and Co-Founder, Kestra

TRUST IN DATA

Establishing trust in data will become the top priority for leaders: In the AI era, data is no longer just a byproduct of operations; it's the foundation for resilience and innovation. Without a strong trust in the data that organizations have and use, businesses will continue to struggle to make informed decisions or leverage emerging technologies like AI. Building this trust will go beyond technology and require leaders to boost data literacy and choose a data strategy that emphasizes both capability and quality. 
Daniel Yu
SVP, SAP Data and Analytics

DATA LABELING

Microscopic lens on the source of data labeling: In technical circles, there are constant discussions around how to get the right dataset — and in turn, how to label that dataset. The reality is that this labeling is outsourced on a global scale. In many cases, it's happening internationally, and often in developing countries, with questionable conditions and levels of pay. You may have task-based workers assessing hundreds of thousands of images and being paid for the number accurately sorted. While AI engineers may be highly in demand and paid well above the market rate, there are questions about this subeconomy.
Gordon Van Huizen
SVP of Strategy, Mendix

EXTENSIVE DATA SETS

Retaining Extensive Data Sets Will Become Essential: GenAI depends on a wide range of structured, unstructured, internal, and external data. Its potential relies on a strong data ecosystem that supports training, fine-tuning, and Retrieval-Augmented Generation (RAG). For industry-specific models, organizations must retain large volumes of data over time. As the world changes, relevant data becomes apparent only in hindsight, revealing inefficiencies and opportunities. By retaining historical data and integrating it with real-time insights, businesses can turn AI from an experimental tool into a strategic asset, driving tangible value across the organization.
Lenley Hensarling
Technical Advisor, Aerospike

SMALL DATA

The past few years have seen a rise in data volumes, but 2025 will bring the focus from "big data" to "small data." We're already seeing this mindset shift with large language models giving way to small language models. Organizations are realizing they don't need to bring all their data to solve a problem or complete an initiative — they need to bring the right data. The overwhelming abundance of data, often referred to as the "data swamp," has made it harder to extract meaningful insights. By focusing on more targeted, higher-quality data — or the "data pond" — organizations can ensure data trust and precision. This shift towards smaller, more relevant data will help speed up analysis timelines, get more people using data, and drive greater ROI from data investments.
Francois Ajenstat
Chief Product Officer, Amplitude

Go to: 2025 DataOps Predictions - Part 2

Related Links

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Three Strikes and They're Gone: Why Proactive Operations is Now a Customer Retention Strategy

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

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 ...

2025 DataOps Predictions - Part 1

As part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2025.

2025: REAL-TIME DATA IS KEY FOR AI

Real-time data will be a key differentiator for competitive advantage: Industries will increasingly rely on real-time or near real-time data to maintain a competitive edge. Companies that can integrate up-to-date data into their AI systems will provide superior customer experiences with fewer issues and more personalized solutions. The ability to capture and analyze data in real-time will separate industry leaders from those who struggle to modernize their data infrastructure.
Ayman Sayed
CEO, BMC Software

Enterprises Will Augment GenAI with Real-Time Data: The true value of GenAI is realized when integrated into enterprise applications at scale. While enterprises have been cautious with trial deployments, 2025 will be a turning point as they begin to scale GenAI across critical systems like customer support, supply chain, manufacturing, and finance. This will require tools to manage data and track GenAI models, ensuring visibility into data usage. GenAI must be supplemented with specific real-time data, such as vectors and graphs, to maximize effectiveness. In 2025, leading vendors will begin rolling out applications that leverage these advancements.
Lenley Hensarling
Technical Advisor, Aerospike

MULTIMODAL DATA

Multimodal data will be very big, extracting corporate value: Back in 2004, Tim O'Reilly coined the phrase, "Data is the Intel Inside." We don't think quite as much about Intel these days, but Tim was absolutely right about data. We became obsessed with data. We've been talking about data science, being data-driven, and building data-driven organizations ever since. Artificial Intelligence is the current expression of the importance of data.

One problem with being data-driven is that most of any organization's data is locked up in ways that aren't useful. Being data-driven works well if you have nicely structured data in a database. Most companies have that, but they're also sitting on a mountain of unstructured data: PDF files, videos, meeting recordings, real-time data feeds, and more. They aren't even used to thinking of this as data; it's not amenable to SQL and database-centric "business intelligence."

That will change in 2025. It will change because AI will give us the ability to unlock this data as well as the ability to analyze it. It will be able to give structure to the information in PDFs, in videos, in meeting transcripts, and in raw data coming in from sensors. In his Generative AI in the Real World interview, Robert Nishihara asked us to think of the video generated by an autonomous vehicle. Most of that is of limited value — but every now and then, there's a traffic situation that is extremely valuable. Humans aren't going to watch hours of video to extract the value; that's a job for AI. Multimodal AI will help companies to unlock the value of data like this. We're at the start of a new generation of tools for data acquisition, cleaning, and curation that will make this unstructured data accessible.
Laura Baldwin
President, O'Reilly Media

AI DRIVES NEW FOCUS ON DATA QUALITY

AI will renew the focus on data quality, for two reasons: First, high quality data is required for training and fine-tuning models. Second, AI-powered analytics tools will offer a higher-resolution view of data, revealing previously undetected quality issues.
Ryan Janssen
CEO, Zenlytic

Enterprises that ready their data for AI will pull ahead competitively: In 2025, companies will focus on building an organized, high-quality data ecosystem to maximize AI's effectiveness and to pull ahead of their competition. This includes managing metadata through structured data catalogs, ensuring data accuracy with rigorous cleansing and validation, and establishing robust governance practices to safeguard data privacy and security. By implementing clear, ethical guidelines, organizations will create a trustworthy AI framework, empowering data scientists with easy access to reliable data for generating precise, impactful insights across business functions. Enterprises that do this will be hard to compete with. 
Scott Voigt
CEO and Founder, Fullstory

AI DRIVES DATA PIPELINE AUTOMATION

GenAI and as-code first technologies drive data pipeline automation: The ubiquitous use of Kubernetes has led to a configuration-first experience in defining data pipelines. It's as simple as selecting a container image and adding configuration. We'll increasingly see GenAI, trained on processing and execution engines generating this configuration and deploying pipelines automatically through just natural language prompts. Traditional visual ETL tooling, even low code platforms are now at risk of disruption. What a power user could do in a few days (remember you still need to learn these platforms), GenAI does in seconds, spitting out configuration for real-time pipelines. This leads to the question. What is the wider future of any UX if my interface is a prompt? Just view data results and metrics? Engineers may as well be going back to a command line!
Andrew Stevenson 
CTO, Lenses.io

AI-ENHANCED DATA MANAGEMENT AND GOVERNANCE

AI is changing how companies manage and govern their data. Organizations now use data lakehouses to support data scientists and AI engineers working with large language models (LLMs). These lakehouses simplify data access, helping teams avoid juggling multiple storage systems. AI is also helping to automate manual processes like data cleaning and reconciliation—a pain point for many professionals. As AI continues to scale, automated governance will allow companies to manage data more effectively with less manual work.
Emmanuel Darras
CEO and Co-Founder, Kestra

UNIFIED DATA ACCESS AND FEDERATION

A unified approach to data access is high on the agenda for enterprises that plan to consolidate analytics data into a single, accessible source. Data lakehouses support this by providing federated access, allowing teams across the organization to tap into the same data without duplicating it. This approach is expected to drive cross-functional analytics and reduce latency, making it easier for teams to work together on the same shared data.
Emmanuel Darras
CEO and Co-Founder, Kestra

TRUST IN DATA

Establishing trust in data will become the top priority for leaders: In the AI era, data is no longer just a byproduct of operations; it's the foundation for resilience and innovation. Without a strong trust in the data that organizations have and use, businesses will continue to struggle to make informed decisions or leverage emerging technologies like AI. Building this trust will go beyond technology and require leaders to boost data literacy and choose a data strategy that emphasizes both capability and quality. 
Daniel Yu
SVP, SAP Data and Analytics

DATA LABELING

Microscopic lens on the source of data labeling: In technical circles, there are constant discussions around how to get the right dataset — and in turn, how to label that dataset. The reality is that this labeling is outsourced on a global scale. In many cases, it's happening internationally, and often in developing countries, with questionable conditions and levels of pay. You may have task-based workers assessing hundreds of thousands of images and being paid for the number accurately sorted. While AI engineers may be highly in demand and paid well above the market rate, there are questions about this subeconomy.
Gordon Van Huizen
SVP of Strategy, Mendix

EXTENSIVE DATA SETS

Retaining Extensive Data Sets Will Become Essential: GenAI depends on a wide range of structured, unstructured, internal, and external data. Its potential relies on a strong data ecosystem that supports training, fine-tuning, and Retrieval-Augmented Generation (RAG). For industry-specific models, organizations must retain large volumes of data over time. As the world changes, relevant data becomes apparent only in hindsight, revealing inefficiencies and opportunities. By retaining historical data and integrating it with real-time insights, businesses can turn AI from an experimental tool into a strategic asset, driving tangible value across the organization.
Lenley Hensarling
Technical Advisor, Aerospike

SMALL DATA

The past few years have seen a rise in data volumes, but 2025 will bring the focus from "big data" to "small data." We're already seeing this mindset shift with large language models giving way to small language models. Organizations are realizing they don't need to bring all their data to solve a problem or complete an initiative — they need to bring the right data. The overwhelming abundance of data, often referred to as the "data swamp," has made it harder to extract meaningful insights. By focusing on more targeted, higher-quality data — or the "data pond" — organizations can ensure data trust and precision. This shift towards smaller, more relevant data will help speed up analysis timelines, get more people using data, and drive greater ROI from data investments.
Francois Ajenstat
Chief Product Officer, Amplitude

Go to: 2025 DataOps Predictions - Part 2

Related Links

Hot Topics

The Latest

Three Strikes and They're Gone: Why Proactive Operations is Now a Customer Retention Strategy

IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

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 ...