Skip to main content

5 Ways to Gain Operational Insights on Big Data Analytics

Michael Segal

We are starting to see an age where speed-of-thought analytical tools are helping to quickly analyze large volumes of data to uncover market trends, customer preferences, gain competitive insight and collect other useful business information. Likewise, utilizing ‘big data’ creates new opportunities to gain deep insight into operational efficiencies.

The realization by business executives that corporate data is an extremely valuable asset, and that effective analysis of big data may have a profound impact on their bottom line is the key driver in the adoption of this trend. According to IDC, the big data and analytics market will reach $125 billion worldwide in 2015, which will help enterprises across all industries gain new operational insights.

Effective integration of big data analytics within corporate business processes is critical to harness the wealth of knowledge that can be extracted from corporate data. While a variety of structured and unstructured big data is stored in large volumes on different servers within the organization, virtually all this data traverses the network at one time or another. Analysis of the traffic data traversing the network can provide deep operational insight, provided there is an end-to-end holistic visibility of this data.

To ensure holistic visibility, the first step is to select a performance management platform that offers the scalability and flexibility needed to analyze large volumes of data in real-time.

The solution should also include packet flow switches to enable passive and intelligent distribution of big data that traverses the network to the different location where the data is analyzed.

Here are five ways IT operations can use Big Data analytics to achieve operational efficiencies:

1. Holistic end-to-end visibility

A holistic view, from the data center and network to the users who consume business services, helps IT see the relationships and interdependencies across all service delivery components; including applications, network, servers, databases and enabling protocols in order to see which user communities and services are utilizing the network and how they’re performing.

2. Big Data analysis based on deep packet inspection

Deep packet analysis can be used to generate a metadata at an atomic level which provides comprehensive, real-time view of all service components, including physical and virtual networks, workloads, protocols, servers, databases, users and devices to help desktop, network, telecom and application teams see through the same lens.

3. Decreased downtime

A Forrester survey shows 91% of IT respondents cite problem identification as the number one improvement needed in their organization’s IT operations. As applications and business services’ complexity increases, reducing costly downtime will hinge on proactively detecting service degradations and rapid triage to identify its origin, which can be done through the right performance management platform.

4. Capacity planning

Accurate evidence is vital when it comes to making capacity planning decisions for your network and business processes. Benefits of metadata at an atomic level will aid in understanding the current and future needs of your organization’s services, applications and its community of users in order to identify how resources are being consumed.

5. Hyper scalability

Big data analytic tools that can scale to increasing data traffic flows provide key vantage points throughout your IT environment and offer rapid insight to meet the monitoring needs of high-density locations in data center and private/hybrid cloud deployments to help organizations achieve consistent service quality and operational excellence.

Network traffic Big Data analytics, made possible by today’s service performance management platforms, is changing the scope and quality of IT operational efficiencies. These platforms and technologies are not only protecting organizations against service degradations and downtime, but also serve to add new dimensions and context around interactive data making corporate data today an extremely valuable asset.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

5 Ways to Gain Operational Insights on Big Data Analytics

Michael Segal

We are starting to see an age where speed-of-thought analytical tools are helping to quickly analyze large volumes of data to uncover market trends, customer preferences, gain competitive insight and collect other useful business information. Likewise, utilizing ‘big data’ creates new opportunities to gain deep insight into operational efficiencies.

The realization by business executives that corporate data is an extremely valuable asset, and that effective analysis of big data may have a profound impact on their bottom line is the key driver in the adoption of this trend. According to IDC, the big data and analytics market will reach $125 billion worldwide in 2015, which will help enterprises across all industries gain new operational insights.

Effective integration of big data analytics within corporate business processes is critical to harness the wealth of knowledge that can be extracted from corporate data. While a variety of structured and unstructured big data is stored in large volumes on different servers within the organization, virtually all this data traverses the network at one time or another. Analysis of the traffic data traversing the network can provide deep operational insight, provided there is an end-to-end holistic visibility of this data.

To ensure holistic visibility, the first step is to select a performance management platform that offers the scalability and flexibility needed to analyze large volumes of data in real-time.

The solution should also include packet flow switches to enable passive and intelligent distribution of big data that traverses the network to the different location where the data is analyzed.

Here are five ways IT operations can use Big Data analytics to achieve operational efficiencies:

1. Holistic end-to-end visibility

A holistic view, from the data center and network to the users who consume business services, helps IT see the relationships and interdependencies across all service delivery components; including applications, network, servers, databases and enabling protocols in order to see which user communities and services are utilizing the network and how they’re performing.

2. Big Data analysis based on deep packet inspection

Deep packet analysis can be used to generate a metadata at an atomic level which provides comprehensive, real-time view of all service components, including physical and virtual networks, workloads, protocols, servers, databases, users and devices to help desktop, network, telecom and application teams see through the same lens.

3. Decreased downtime

A Forrester survey shows 91% of IT respondents cite problem identification as the number one improvement needed in their organization’s IT operations. As applications and business services’ complexity increases, reducing costly downtime will hinge on proactively detecting service degradations and rapid triage to identify its origin, which can be done through the right performance management platform.

4. Capacity planning

Accurate evidence is vital when it comes to making capacity planning decisions for your network and business processes. Benefits of metadata at an atomic level will aid in understanding the current and future needs of your organization’s services, applications and its community of users in order to identify how resources are being consumed.

5. Hyper scalability

Big data analytic tools that can scale to increasing data traffic flows provide key vantage points throughout your IT environment and offer rapid insight to meet the monitoring needs of high-density locations in data center and private/hybrid cloud deployments to help organizations achieve consistent service quality and operational excellence.

Network traffic Big Data analytics, made possible by today’s service performance management platforms, is changing the scope and quality of IT operational efficiencies. These platforms and technologies are not only protecting organizations against service degradations and downtime, but also serve to add new dimensions and context around interactive data making corporate data today an extremely valuable asset.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...