5 Ways to Gain Operational Insights on Big Data Analytics

Michael Segal
Learn more about NetScout

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.

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5 Ways to Gain Operational Insights on Big Data Analytics

Michael Segal
Learn more about NetScout

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.

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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 ...

Enterprise Edge AI Reaches Inflection Point

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 ...

AI Is Hitting Operational Limits

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 ...

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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 ...

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