What Can AIOps Do For IT Ops? - Part 5

APMdigest asked the top minds in the industry what they think AIOps can do for IT Operations. Part 5 is all about data.

Start with What Can AIOps Do For IT Ops? - Part 1

Start with What Can AIOps Do For IT Ops? - Part 2

Start with What Can AIOps Do For IT Ops? - Part 3

Start with What Can AIOps Do For IT Ops? - Part 4

DATA-DRIVEN ITOPS

AIOps is not a product. It's about the mental shift we saw in DevOps with developers using tools from operations and vice versa. Add AI to the mix and you'll see the DevOps persona using data science tools, like Jupyter Notebooks, and data-scientists implementing DevOps tooling, like operators. AIOps is culture — it can help Operations to become even more data-driven.
Marcel Hild
Manager AIOps, Office of the CTO, Red Hat

AIOps can help ITOps to become a data-driven organization by integrating independent, distributed, siloed teams and processes through the lens of data flow in the context of customer impact and value alignment. It can significantly improve the process of issue identification, knowledge, and resolution thereby improving customer and employee experience across multiple domains of IT operation management. It improves cost and value of business as it applies contextual data to drive proactive insightful actions to improve ROI and customer satisfaction.
Bhanu Singh
VP Product Development and Cloud Operations, OpsRamp

GAINING VALUE FROM BIG DATA

IT Operations teams play a crucial role in maintaining business' applications and end users' digital experiences. These teams take on the responsibility of monitoring all of the data pertaining to the apps and quickly identify and address any hiccups that could impact customers. Incorporating AIOps into a full-stack observability platform supports digital assets and teams can automate many responsibilities as well as handle a larger data set across the IT stack. AIOps will handle the tedious tasks of keeping track of the data and give IT Ops teams an overview of what's important and where they should focus to ultimately impact their bottom line.
Joe Byrne
Regional CTO, Cisco AppDynamics

IT architectures generate a significant amount of data that is often bypassed and discarded without detailed analysis while monitoring. This data, with the assistance of AIOps, can help fill the performance visibility gaps and predict anomalies. AIOps takes the structured and unstructured data and processes it into meaningful information that helps preempt any probable future events that may impact availability and performance. By leveraging this information, it also helps avoid future outages and delays that businesses may face by formulating complex automated decisions based on various learning techniques.
Arun Balachandran
Sr. Marketing Manager, ManageEngine

The true power of AIOps lies in the ability to consume and analyze the ever-increasing data generated by IT —and present it in a practical, actionable way. Whether it's looking at infrastructure and application data, IT service management (ITSM) data or business system data, AIOps helps IT operations teams go beyond the manual processes of sorting through deep arrays of data to find meaningful information. AIOps allows IT Operations teams to cut through the noise by quickly surfacing information that helps minimize downtime and maximize performance.
Ranjan Goel
VP, Product Management, LogicMonitor

MAKING DATA ACTIONABLE

IT organizations are under continuous pressure to keep applications running, manage various infrastructure components, and deliver faster results at lower cost. While businesses are undergoing digital transformation, IT operation teams need to outpace the demand by adopting AIOps. The real value of AIOps is the ability to take events and metrics from various systems, correlate, reduce, and identify "needles in the haystack". There is a large volume of data produced, the key is to analyze and present it in a way that is actionable. These actions are a combination of automated and manual tasks that should be managed via a service management (ITSM) tool with the appropriate change controls. AIOps platforms reduce the amount of human involvement needed for the data analysis, surfacing insights that allow IT operations to make faster decisions.
Randy Randhawa
SVP of Engineering, Virtana

IMPROVING DATA QUALITY

AI augmented intelligence in data preparation can improve data quality by surfacing and automatically correcting anomalies in data feeds.
David P. Mariani
CTO and Founder, AtScale

BUILDING BETTER MODELS

AI can assist data engineers in building better models by suggesting table relationships and producing histograms that show frequency distributions for field values. IT leaders that embrace AIOps can completely transform how their organizations make decisions.
David P. Mariani
CTO and Founder, AtScale

AIOps allows for real-time, continuous data acquisition, providing outcome data for model updates and insights as part of an ongoing feedback loop. By triggering events that enable data scientists to easily update and deploy new models, AIOps creates a ripple effect throughout the application ecosystem and enterprise at large. The ripple effect results in greater agility and reliability in response to the volatility, uncertainty, complexity, and ambiguity of digital transformation.
Alan Young
CPO, InRule

FAST QUERY RESPONSE

Nevermind robots writing code. One AIOps dimension that can get overlooked is how AI can be used to prepare data for analysis and data science algorithms by automating some data engineering tasks. More and more developers are tasked with creating "data apps" and data engineers that do this work are in short supply. AI can automatically find the best strategy to optimize data storage by indexing, aggregating, and querying to ensure sub-second query response times on very large datasets. Developers can't really call their creations successful if they slow to a crawl as soon as data volumes rise. And they are certain to rise.
Li Kang
VP, North America, Kyligence

CONNECTING DATA SILOS

IT operations departments can often struggle with manual processes and heavily siloed tools, creating tedious and fragmented workflows. The power of AIOps lies in its ability to connect these siloes by accessing various types data from multiple sources (e.g., metrics, logs & traces) as well as other contextual information (incidents, changes, application maps, users). AIOps combs through large amounts of this data to identify patterns and anomalies and predict when issues are going to occur before they impact users. IT operations departments resolve issues more quickly and accurately, stopping them before they snowball into enterprise-wide disruptions.
Jeff Hausman
SVP & GM Operations Management (ITOM, ITAM, Security), ServiceNow

HOLISTIC BUSINESS VIEW

Operations teams have become overloaded with data from rapidly expanding modern IT infrastructure. They're also dealing with shrinking budgets and increased number of devices that make it harder to keep things running smoothly. AIOps allows organizations to gather all their data in one place and build machine learning models that understand, alert, and act when needed. For example, when AIOps is paired with IT operations, a more holistic business view is established to help analyze the available telemetry, report potential issues, and provide remediation steps operators can review and implement on the spot.
Eric Thiel
Director, Developer Experience, Cisco

UNDERSTANDING HOW CHANGE IMPACTS BUSINESS

AIOps enables a big data analytics approach for IT operations, DevOps and Developers. The adoption of AIOps enables IT and business operations with a more proactive way of working by predicting and remediating performance or other bottlenecks across applications and deployments before they might negatively impact business and customers. Critical business services which are automated through key applications must be monitored through data that is produced during key tasks within these business services. Understanding different patterns or clustering data allows business and IT to understand the relationships and anomalies and act upon them. What this means: Applying big data analytics to transaction and customer data makes it easier to monitor how changes within the environment affect the business operations. Discussions and plans around application modifications, upgrades, or technology changes will be more effective and efficient as the impact will be known before choosing the path forward.
Eveline Oehrlich
Chief Research Officer, DevOps Institute

Go to What Can AIOps Do For IT Ops? - Part 6

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Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

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I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

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What Can AIOps Do For IT Ops? - Part 5

APMdigest asked the top minds in the industry what they think AIOps can do for IT Operations. Part 5 is all about data.

Start with What Can AIOps Do For IT Ops? - Part 1

Start with What Can AIOps Do For IT Ops? - Part 2

Start with What Can AIOps Do For IT Ops? - Part 3

Start with What Can AIOps Do For IT Ops? - Part 4

DATA-DRIVEN ITOPS

AIOps is not a product. It's about the mental shift we saw in DevOps with developers using tools from operations and vice versa. Add AI to the mix and you'll see the DevOps persona using data science tools, like Jupyter Notebooks, and data-scientists implementing DevOps tooling, like operators. AIOps is culture — it can help Operations to become even more data-driven.
Marcel Hild
Manager AIOps, Office of the CTO, Red Hat

AIOps can help ITOps to become a data-driven organization by integrating independent, distributed, siloed teams and processes through the lens of data flow in the context of customer impact and value alignment. It can significantly improve the process of issue identification, knowledge, and resolution thereby improving customer and employee experience across multiple domains of IT operation management. It improves cost and value of business as it applies contextual data to drive proactive insightful actions to improve ROI and customer satisfaction.
Bhanu Singh
VP Product Development and Cloud Operations, OpsRamp

GAINING VALUE FROM BIG DATA

IT Operations teams play a crucial role in maintaining business' applications and end users' digital experiences. These teams take on the responsibility of monitoring all of the data pertaining to the apps and quickly identify and address any hiccups that could impact customers. Incorporating AIOps into a full-stack observability platform supports digital assets and teams can automate many responsibilities as well as handle a larger data set across the IT stack. AIOps will handle the tedious tasks of keeping track of the data and give IT Ops teams an overview of what's important and where they should focus to ultimately impact their bottom line.
Joe Byrne
Regional CTO, Cisco AppDynamics

IT architectures generate a significant amount of data that is often bypassed and discarded without detailed analysis while monitoring. This data, with the assistance of AIOps, can help fill the performance visibility gaps and predict anomalies. AIOps takes the structured and unstructured data and processes it into meaningful information that helps preempt any probable future events that may impact availability and performance. By leveraging this information, it also helps avoid future outages and delays that businesses may face by formulating complex automated decisions based on various learning techniques.
Arun Balachandran
Sr. Marketing Manager, ManageEngine

The true power of AIOps lies in the ability to consume and analyze the ever-increasing data generated by IT —and present it in a practical, actionable way. Whether it's looking at infrastructure and application data, IT service management (ITSM) data or business system data, AIOps helps IT operations teams go beyond the manual processes of sorting through deep arrays of data to find meaningful information. AIOps allows IT Operations teams to cut through the noise by quickly surfacing information that helps minimize downtime and maximize performance.
Ranjan Goel
VP, Product Management, LogicMonitor

MAKING DATA ACTIONABLE

IT organizations are under continuous pressure to keep applications running, manage various infrastructure components, and deliver faster results at lower cost. While businesses are undergoing digital transformation, IT operation teams need to outpace the demand by adopting AIOps. The real value of AIOps is the ability to take events and metrics from various systems, correlate, reduce, and identify "needles in the haystack". There is a large volume of data produced, the key is to analyze and present it in a way that is actionable. These actions are a combination of automated and manual tasks that should be managed via a service management (ITSM) tool with the appropriate change controls. AIOps platforms reduce the amount of human involvement needed for the data analysis, surfacing insights that allow IT operations to make faster decisions.
Randy Randhawa
SVP of Engineering, Virtana

IMPROVING DATA QUALITY

AI augmented intelligence in data preparation can improve data quality by surfacing and automatically correcting anomalies in data feeds.
David P. Mariani
CTO and Founder, AtScale

BUILDING BETTER MODELS

AI can assist data engineers in building better models by suggesting table relationships and producing histograms that show frequency distributions for field values. IT leaders that embrace AIOps can completely transform how their organizations make decisions.
David P. Mariani
CTO and Founder, AtScale

AIOps allows for real-time, continuous data acquisition, providing outcome data for model updates and insights as part of an ongoing feedback loop. By triggering events that enable data scientists to easily update and deploy new models, AIOps creates a ripple effect throughout the application ecosystem and enterprise at large. The ripple effect results in greater agility and reliability in response to the volatility, uncertainty, complexity, and ambiguity of digital transformation.
Alan Young
CPO, InRule

FAST QUERY RESPONSE

Nevermind robots writing code. One AIOps dimension that can get overlooked is how AI can be used to prepare data for analysis and data science algorithms by automating some data engineering tasks. More and more developers are tasked with creating "data apps" and data engineers that do this work are in short supply. AI can automatically find the best strategy to optimize data storage by indexing, aggregating, and querying to ensure sub-second query response times on very large datasets. Developers can't really call their creations successful if they slow to a crawl as soon as data volumes rise. And they are certain to rise.
Li Kang
VP, North America, Kyligence

CONNECTING DATA SILOS

IT operations departments can often struggle with manual processes and heavily siloed tools, creating tedious and fragmented workflows. The power of AIOps lies in its ability to connect these siloes by accessing various types data from multiple sources (e.g., metrics, logs & traces) as well as other contextual information (incidents, changes, application maps, users). AIOps combs through large amounts of this data to identify patterns and anomalies and predict when issues are going to occur before they impact users. IT operations departments resolve issues more quickly and accurately, stopping them before they snowball into enterprise-wide disruptions.
Jeff Hausman
SVP & GM Operations Management (ITOM, ITAM, Security), ServiceNow

HOLISTIC BUSINESS VIEW

Operations teams have become overloaded with data from rapidly expanding modern IT infrastructure. They're also dealing with shrinking budgets and increased number of devices that make it harder to keep things running smoothly. AIOps allows organizations to gather all their data in one place and build machine learning models that understand, alert, and act when needed. For example, when AIOps is paired with IT operations, a more holistic business view is established to help analyze the available telemetry, report potential issues, and provide remediation steps operators can review and implement on the spot.
Eric Thiel
Director, Developer Experience, Cisco

UNDERSTANDING HOW CHANGE IMPACTS BUSINESS

AIOps enables a big data analytics approach for IT operations, DevOps and Developers. The adoption of AIOps enables IT and business operations with a more proactive way of working by predicting and remediating performance or other bottlenecks across applications and deployments before they might negatively impact business and customers. Critical business services which are automated through key applications must be monitored through data that is produced during key tasks within these business services. Understanding different patterns or clustering data allows business and IT to understand the relationships and anomalies and act upon them. What this means: Applying big data analytics to transaction and customer data makes it easier to monitor how changes within the environment affect the business operations. Discussions and plans around application modifications, upgrades, or technology changes will be more effective and efficient as the impact will be known before choosing the path forward.
Eveline Oehrlich
Chief Research Officer, DevOps Institute

Go to What Can AIOps Do For IT Ops? - Part 6

Related Links

Hot Topics

The Latest

AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Move Fast Without the Usual Quality Penalty

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

When the Observability Bill Arrives for Agentic AI

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

How Edge Intelligence Will Redefine Secure Connectivity

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

Production Inference Shifts Decisively to Private Cloud

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

Why DevOps Teams Build GenAI Tooling While Most Organizations Still Rely on Manual Monitoring

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Why Cloud Reliability Comes Down to People and How to Improve It

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

Why IT Keeps Getting Handed an AI Training Problem It Can't Solve Alone

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

Bringing IBM i Into the Modern Observability Stack: The Missing Piece of Enterprise AIOps

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...