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Data Matters More Than Ever in AIOps

New study reveals key hurdles on the road to AIOps
Bhanu Singh

Like any new, potentially disruptive technology, artificial intelligence for IT operations, or AIOps has quickly become a trend, and slowly become a reality. It's only been a few years since Gartner coined the term, and yet, 30% of IT teams in large enterprises will roll out AIOps initiatives by 2023. These IT practitioners are still in experimentation mode with artificial intelligence in many cases, and still have concerns about how credible the technology can be. They have concerns over the results of these implementations, and worry about maintaining service availability and uptime during migration.

Because AIOps is still in its infancy, there hasn't been much reporting on what these concerns specifically are. A recent study from OpsRamp targeted these IT managers who have implemented AIOps, and among other data, reports on the primary concerns of this new approach to operations management.

The Devil is in the Data

The report cites data accuracy as the chief concern for IT pros when it comes to AIOps. Two-thirds (67%) of those surveyed revealed it as their top priority. This could be for a variety of reasons, including:

Data Sources: In a world of distributed, hybrid, multi-cloud infrastructure, it's more difficult than ever to capture data on every level of an organization. Different cloud providers report in different ways. And point tools provide analytics across a host of different metrics. It's next to impossible to compare data sources together for a true contextual view of the organization.

Data Quality: Even when that data is captured, these IT teams aren't necessarily sure that it's accurately reflecting the truth about a system. Modern data can be fragmented, hidden, unparsed or too distributed to make sense.

Data Volume: Today's enterprise infrastructure produces an overwhelming amount of metrics on usage, capacity, performance, availability, security, and more. It's easy to get lost in the noise.

Data Consistency: It's impossible to say, under the crushing weight of data today, that IT teams are seeing consistent reporting and results across the organization. But until data is consistent, it can't be actionable.

Data Culture: This is perhaps the biggest change the world of IT operations will resist as it continues to adopt AIOps. Most organizations today are still process-driven, focusing maniacally on improving, tweaking, and changing the process to get a different result. Tomorrow's AIOps-driven organization will become data-driven, putting that same focus on refining data for better outcomes.

Improving Accuracy by Changing Culture

Becoming a data-driven organization means shifting priorities from process milestones to data-based ones, where data manipulation and governance are critical. It's building an organization where data modeling is as important as product development, and where data drives business outcomes. It's where there's as much focused placed on algorithms as applications. Once this culture is installed, where the focus becomes accuracy, consistency, and context, can an operations team truly trust the data. And this is where AIOps can truly come to life.

Data accuracy isn't the only concern when it comes to AIOps adoption, but it's definitely on the minds of IT managers and infrastructure professionals. Where they once just struggled to find skilled practitioners and leading-edge technology to solve problems, they now must also juggle a focus on data. It's clear that enterprises will need more time to build trust in the relevance and reliability of AIOps recommendations. This also represents an opportunity for AIOps vendors to provide solutions that drive improved accuracy, cleaner data, and greater control. AIOps promises to transform how IT operations is managed and maintained. It's likely to do the same for data.

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Azul

Data Matters More Than Ever in AIOps

New study reveals key hurdles on the road to AIOps
Bhanu Singh

Like any new, potentially disruptive technology, artificial intelligence for IT operations, or AIOps has quickly become a trend, and slowly become a reality. It's only been a few years since Gartner coined the term, and yet, 30% of IT teams in large enterprises will roll out AIOps initiatives by 2023. These IT practitioners are still in experimentation mode with artificial intelligence in many cases, and still have concerns about how credible the technology can be. They have concerns over the results of these implementations, and worry about maintaining service availability and uptime during migration.

Because AIOps is still in its infancy, there hasn't been much reporting on what these concerns specifically are. A recent study from OpsRamp targeted these IT managers who have implemented AIOps, and among other data, reports on the primary concerns of this new approach to operations management.

The Devil is in the Data

The report cites data accuracy as the chief concern for IT pros when it comes to AIOps. Two-thirds (67%) of those surveyed revealed it as their top priority. This could be for a variety of reasons, including:

Data Sources: In a world of distributed, hybrid, multi-cloud infrastructure, it's more difficult than ever to capture data on every level of an organization. Different cloud providers report in different ways. And point tools provide analytics across a host of different metrics. It's next to impossible to compare data sources together for a true contextual view of the organization.

Data Quality: Even when that data is captured, these IT teams aren't necessarily sure that it's accurately reflecting the truth about a system. Modern data can be fragmented, hidden, unparsed or too distributed to make sense.

Data Volume: Today's enterprise infrastructure produces an overwhelming amount of metrics on usage, capacity, performance, availability, security, and more. It's easy to get lost in the noise.

Data Consistency: It's impossible to say, under the crushing weight of data today, that IT teams are seeing consistent reporting and results across the organization. But until data is consistent, it can't be actionable.

Data Culture: This is perhaps the biggest change the world of IT operations will resist as it continues to adopt AIOps. Most organizations today are still process-driven, focusing maniacally on improving, tweaking, and changing the process to get a different result. Tomorrow's AIOps-driven organization will become data-driven, putting that same focus on refining data for better outcomes.

Improving Accuracy by Changing Culture

Becoming a data-driven organization means shifting priorities from process milestones to data-based ones, where data manipulation and governance are critical. It's building an organization where data modeling is as important as product development, and where data drives business outcomes. It's where there's as much focused placed on algorithms as applications. Once this culture is installed, where the focus becomes accuracy, consistency, and context, can an operations team truly trust the data. And this is where AIOps can truly come to life.

Data accuracy isn't the only concern when it comes to AIOps adoption, but it's definitely on the minds of IT managers and infrastructure professionals. Where they once just struggled to find skilled practitioners and leading-edge technology to solve problems, they now must also juggle a focus on data. It's clear that enterprises will need more time to build trust in the relevance and reliability of AIOps recommendations. This also represents an opportunity for AIOps vendors to provide solutions that drive improved accuracy, cleaner data, and greater control. AIOps promises to transform how IT operations is managed and maintained. It's likely to do the same for data.

Hot Topics

The Latest

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

In March, New Relic published the State of Observability for Media and Entertainment Report to share insights, data, and analysis into the adoption and business value of observability across the media and entertainment industry. Here are six key takeaways from the report ...

Regardless of their scale, business decisions often take time, effort, and a lot of back-and-forth discussion to reach any sort of actionable conclusion ... Any means of streamlining this process and getting from complex problems to optimal solutions more efficiently and reliably is key. How can organizations optimize their decision-making to save time and reduce excess effort from those involved? ...

As enterprises accelerate their cloud adoption strategies, CIOs are routinely exceeding their cloud budgets — a concern that's about to face additional pressure from an unexpected direction: uncertainty over semiconductor tariffs. The CIO Cloud Trends Survey & Report from Azul reveals the extent continued cloud investment despite cost overruns, and how organizations are attempting to bring spending under control ...

Image
Azul