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The New Normal for IT Ops Deepens Need for AI - Part 1

Will Cappelli
Moogsoft

The global pandemic has radically changed how enterprise IT services are consumed, both in the short and long term. Here's how AIOps can help IT Ops teams.

The current crisis has upended all aspects of our personal and work lives, and IT Ops pros aren't the exception. The abrupt shift to remote work has created unprecedented challenges for IT Ops teams, while increasing pressure on them to prevent outages and provide service assurance.

Specifically, new consumption patterns of enterprise IT services have put stress on systems, architectures and topologies at all stack layers. In response, IT Ops teams must rapidly implement structural and management changes to address both temporary and permanent shifts.

In this turmoil, AIOps has emerged as a lifeline. By streamlining and automating IT operations, AIOps helps IT leaders collaborate remotely and act quickly and precisely to maintain business-critical digital services — during the pandemic and beyond.

Let's look in more detail at these challenges and at how AIOps can help IT Ops teams cope and succeed.

AIOps: A Definition

An AIOps solution must have these five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:

■ Data selection: Identifying and surfacing the most relevant information.

■ Pattern discovery: Correlating and finding relationships between events across your tool stack.

■ Inference: Identifying root causes and recurring issues.

■ Collaboration: Notifying appropriate operators, and facilitating collaboration.

■ Automation: Automating remediation

In a real world setting, an AIOps solution ingests heterogeneous data from many different sources. Using entropy algorithms, it removes noise and duplication, and selects only the truly relevant data. It then groups and correlates this relevant information using various criteria, like text, time and topology.

Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events. It then communicates the result of that analysis to a collaborative environment, which will support automated responses to what has been discovered.

As such, an AIOps solution plays the role of organizing and integrating what an organization's domain-specific IT monitoring and management tools do, intelligently integrating the stack's functionalities. AIOps should act as the brain that brings together these tools, and becomes a coordinating, central layer.

Transitioning to the New Normal

As the workforce shifts to remote work, user behaviors will change and different elements of the IT infrastructure, both in-house and publicly sourced, will be stressed. This will result in new, quickly-evolving types of incidents and outages. With AIOps, IT Ops teams can detect and analyze genuinely novel anomalies which can cause incidents and outages rapidly and stealthily.

Cross-regional and intra-regional team collaboration among IT operations and NOC organizations will need to be reinforced virtually as the implicit supports derived from physical co-presence are removed. AIOps can enable and guide virtual collaborative observation, analysis and response efforts, helping IT Ops teams collaborate and communicate despite being physically dispersed.

Sharp and unpredictable levels of staff reduction due to illness and self-isolation will force IT operations and NOC organizations to "do more with less" on both the side of signal observation and the side of signal response. Here again AIOps can help IT Ops teams to respond by both dynamically filtering noisy alert streams, and integrating and automating platforms that support various aspects of incident and problem management.

Go to The New Normal for IT Ops Deepens Need for AI - Part 2

Will Cappelli is Field CTO at Moogsoft

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The New Normal for IT Ops Deepens Need for AI - Part 1

Will Cappelli
Moogsoft

The global pandemic has radically changed how enterprise IT services are consumed, both in the short and long term. Here's how AIOps can help IT Ops teams.

The current crisis has upended all aspects of our personal and work lives, and IT Ops pros aren't the exception. The abrupt shift to remote work has created unprecedented challenges for IT Ops teams, while increasing pressure on them to prevent outages and provide service assurance.

Specifically, new consumption patterns of enterprise IT services have put stress on systems, architectures and topologies at all stack layers. In response, IT Ops teams must rapidly implement structural and management changes to address both temporary and permanent shifts.

In this turmoil, AIOps has emerged as a lifeline. By streamlining and automating IT operations, AIOps helps IT leaders collaborate remotely and act quickly and precisely to maintain business-critical digital services — during the pandemic and beyond.

Let's look in more detail at these challenges and at how AIOps can help IT Ops teams cope and succeed.

AIOps: A Definition

An AIOps solution must have these five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:

■ Data selection: Identifying and surfacing the most relevant information.

■ Pattern discovery: Correlating and finding relationships between events across your tool stack.

■ Inference: Identifying root causes and recurring issues.

■ Collaboration: Notifying appropriate operators, and facilitating collaboration.

■ Automation: Automating remediation

In a real world setting, an AIOps solution ingests heterogeneous data from many different sources. Using entropy algorithms, it removes noise and duplication, and selects only the truly relevant data. It then groups and correlates this relevant information using various criteria, like text, time and topology.

Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events. It then communicates the result of that analysis to a collaborative environment, which will support automated responses to what has been discovered.

As such, an AIOps solution plays the role of organizing and integrating what an organization's domain-specific IT monitoring and management tools do, intelligently integrating the stack's functionalities. AIOps should act as the brain that brings together these tools, and becomes a coordinating, central layer.

Transitioning to the New Normal

As the workforce shifts to remote work, user behaviors will change and different elements of the IT infrastructure, both in-house and publicly sourced, will be stressed. This will result in new, quickly-evolving types of incidents and outages. With AIOps, IT Ops teams can detect and analyze genuinely novel anomalies which can cause incidents and outages rapidly and stealthily.

Cross-regional and intra-regional team collaboration among IT operations and NOC organizations will need to be reinforced virtually as the implicit supports derived from physical co-presence are removed. AIOps can enable and guide virtual collaborative observation, analysis and response efforts, helping IT Ops teams collaborate and communicate despite being physically dispersed.

Sharp and unpredictable levels of staff reduction due to illness and self-isolation will force IT operations and NOC organizations to "do more with less" on both the side of signal observation and the side of signal response. Here again AIOps can help IT Ops teams to respond by both dynamically filtering noisy alert streams, and integrating and automating platforms that support various aspects of incident and problem management.

Go to The New Normal for IT Ops Deepens Need for AI - Part 2

Will Cappelli is Field CTO at Moogsoft

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

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 ...