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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.

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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.

The Latest

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study. The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale ...

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IBM

 

A major architectural shift is underway across enterprise networks, according to a new global study from Cisco. As AI assistants, agents, and data-driven workloads reshape how work gets done, they're creating faster, more dynamic, more latency-sensitive, and more complex network traffic. Combined with the ubiquity of connected devices, 24/7 uptime demands, and intensifying security threats, these shifts are driving infrastructure to adapt and evolve ...

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