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Log Data Now Outranks Traditional Data Sources for Network Operations Management

Jim Frey

As network managers, engineers, and operators strive to protect the integrity and performance of enterprise networks, they are faced with an onslaught of data and metrics. They must wade quickly and carefully through this deluge in order to perform monitoring, troubleshooting, and planning. With recent trends moving technology toward software-defined and programmable infrastructure, as well as the parallel convergence of IT operations across multiple technology domains, network log data is being increasingly both used and appreciated. But proper and effective use of network log data is not without its challenges.

Enterprise Management Associates (EMA) released its latest research report entitled Log Analytics for Network Operations Management which takes a detailed look at the ways in which network log data is being harvested, analyzed, and used for network operations management. Based on the experiences and findings of over 190 enterprise practitioners, log analytics best practices are provided.

Some of the key findings in this study include:

■ 96% of participants indicated that network log data was of average importance or higher within their overall hierarchy of network management data sources, and 64% felt is “More important than most” or “Most important”.

■ 75% of shops are either currently using a central log analysis system or are planning to consolidate the multiple tools they have into a single system.

■ The biggest challenge most face when using network log data is “Knowing what to look for” and consequently the most highly valued feature for log analytics is “Fast search”.

■ Over 90% of organizations are applying one or more forms of advanced analytics in the processing of network log data, such as root cause analysis, proactive alerting, threat identification, and performance trending.

■ Over 80% of organizations are using network log data to support higher level BSM/ITSM initiatives, most particularly for IT service quality monitoring (61%).

EMA has been tracking the role that network log data plays in network management disciplines for years. What is most striking is that log data now outranks traditional network management data sources such as SNMP, NetFlow, and packet analysis as most heavily used and valued for multiple use cases. EMA recommends that networking professionals add log data to their portfolio of viewpoints while also seeking a means to closely integrate and align that viewpoint with others in order to get the most impactful results.

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Log Data Now Outranks Traditional Data Sources for Network Operations Management

Jim Frey

As network managers, engineers, and operators strive to protect the integrity and performance of enterprise networks, they are faced with an onslaught of data and metrics. They must wade quickly and carefully through this deluge in order to perform monitoring, troubleshooting, and planning. With recent trends moving technology toward software-defined and programmable infrastructure, as well as the parallel convergence of IT operations across multiple technology domains, network log data is being increasingly both used and appreciated. But proper and effective use of network log data is not without its challenges.

Enterprise Management Associates (EMA) released its latest research report entitled Log Analytics for Network Operations Management which takes a detailed look at the ways in which network log data is being harvested, analyzed, and used for network operations management. Based on the experiences and findings of over 190 enterprise practitioners, log analytics best practices are provided.

Some of the key findings in this study include:

■ 96% of participants indicated that network log data was of average importance or higher within their overall hierarchy of network management data sources, and 64% felt is “More important than most” or “Most important”.

■ 75% of shops are either currently using a central log analysis system or are planning to consolidate the multiple tools they have into a single system.

■ The biggest challenge most face when using network log data is “Knowing what to look for” and consequently the most highly valued feature for log analytics is “Fast search”.

■ Over 90% of organizations are applying one or more forms of advanced analytics in the processing of network log data, such as root cause analysis, proactive alerting, threat identification, and performance trending.

■ Over 80% of organizations are using network log data to support higher level BSM/ITSM initiatives, most particularly for IT service quality monitoring (61%).

EMA has been tracking the role that network log data plays in network management disciplines for years. What is most striking is that log data now outranks traditional network management data sources such as SNMP, NetFlow, and packet analysis as most heavily used and valued for multiple use cases. EMA recommends that networking professionals add log data to their portfolio of viewpoints while also seeking a means to closely integrate and align that viewpoint with others in order to get the most impactful results.

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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 ...

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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 ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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