Skip to main content

Recession Boosted Business Innovations: Adopting Automation and Predictive Analytics

Vikas Aggarwal

Agility, ability and velocity became the new success necessities of organizations in the past few years, especially in just gone by 2011, driven largely by recession realities and unpredictable business environment.

Recession, viewed positively, boosted innovation and increased the pace of new process development and its adoption. The IT customer is increasingly global, the realm of the IT services grows larger every day and the sprawling, distributed IT components demand intelligent ways to manage and monitor this infrastructure.

The administrative burden for managing this burgeoning infrastructure will only increase for IT departments, unless they adopt processes and software to automate most of the burden.

To automate processes, you need to integrate the different workflows seamlessly, which requires the software products to have flexible APIs. The order entry, provisioning, monitoring & billing workflows are all candidates for integration and automation. There have been significant advances even within the monitoring and management solutions to reduce the administrative burden with the use of templates, threshold baselining and creating of service models.

The other innovation has been in the field of data analytics. The IT customer's demands have always been dynamic, and IT departments have reacted by provisioning for the peak demand, resulting in wasted idle compute resources. Even the usage of application resources is dynamic by the hour and day of week, and it is increasingly important for IT departments to understand the behavior pattern of their network and applications in addition to the computing resources.

The number of users, the response times, the queued messages, the database query rate -- all vary by time of day and understanding the usage pattern and deviations from it helps isolate the root cause of IT service performance degradation much faster, and ultimately, higher customer satisfaction. More importantly, using APM behavior patterns greatly reduces the amount of false alarms for IT Operations, and lower TCO.

Automation and analytics are smart product features focusing on reducing the administrative burden in today's distributed Cloud environments. Keeping business necessities in mind, these innovative features are pragmatically relevant and a must for all IT departments in today's business environment.

Vikas Aggarwal is CEO of Zyrion.

The Latest

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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Recession Boosted Business Innovations: Adopting Automation and Predictive Analytics

Vikas Aggarwal

Agility, ability and velocity became the new success necessities of organizations in the past few years, especially in just gone by 2011, driven largely by recession realities and unpredictable business environment.

Recession, viewed positively, boosted innovation and increased the pace of new process development and its adoption. The IT customer is increasingly global, the realm of the IT services grows larger every day and the sprawling, distributed IT components demand intelligent ways to manage and monitor this infrastructure.

The administrative burden for managing this burgeoning infrastructure will only increase for IT departments, unless they adopt processes and software to automate most of the burden.

To automate processes, you need to integrate the different workflows seamlessly, which requires the software products to have flexible APIs. The order entry, provisioning, monitoring & billing workflows are all candidates for integration and automation. There have been significant advances even within the monitoring and management solutions to reduce the administrative burden with the use of templates, threshold baselining and creating of service models.

The other innovation has been in the field of data analytics. The IT customer's demands have always been dynamic, and IT departments have reacted by provisioning for the peak demand, resulting in wasted idle compute resources. Even the usage of application resources is dynamic by the hour and day of week, and it is increasingly important for IT departments to understand the behavior pattern of their network and applications in addition to the computing resources.

The number of users, the response times, the queued messages, the database query rate -- all vary by time of day and understanding the usage pattern and deviations from it helps isolate the root cause of IT service performance degradation much faster, and ultimately, higher customer satisfaction. More importantly, using APM behavior patterns greatly reduces the amount of false alarms for IT Operations, and lower TCO.

Automation and analytics are smart product features focusing on reducing the administrative burden in today's distributed Cloud environments. Keeping business necessities in mind, these innovative features are pragmatically relevant and a must for all IT departments in today's business environment.

Vikas Aggarwal is CEO of Zyrion.

The Latest

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

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...