Losing $$ Due to Ticket Times? Hack Response Time Using Data

Collin Firenze

Without the proper expertise and tools in place to quickly isolate, diagnose, and resolve an incident, a quick routine error can result in hours of downtime – causing significant interruption in business operations that can impact both business revenue and employee productivity. How can we stop these little instances from turning into major fallouts? Major companies and organizations, take heed:

1. Identify the correlation between issues to expedite time to notify and time to resolve

Not understanding the correlation between issues is detrimental to timely resolutions. With a network monitoring solution in place, lack of automated correlation can generate excess "noise." This then requires support teams to act on numerous individualized alerts, rather than a single ticket that has all relevant events and information for the support end-user.

The correlated monitoring approach provides a holistic view into the network failure for support teams. Enabling support teams to analyze the network failure by utilizing the correlated events to efficiently identify the root cause will provide them the opportunity to promptly execute the corrective action to resolve the issue at hand.

Correlation consolidates all relevant information into a single ticket allowing support teams to largely reduce their staffing models, with only one support engineer needed to act on the incident as opposed to numerous resources engaging on individualized alerts.

2. Constantly analyzing raw data for trends helps IT teams proactively spot and prevent recurring issues

Aside from the standard reactive response of a support team, there is substantial benefit in the proactive analysis of raw data from your environment. By being proactive, trends and failures can be identified, followed by corrective and preventative actions taken to ensure support teams are not spending time investigating repeat issues. This approach not only creates a more stable environment with fewer failures, but also allows support teams to reduce manual hours and cost by avoiding "wasted" investigation on known and reoccurring issues.

Within a support organization, a Problem Management Group (PMG) is often implemented to fulfill the role of proactive analysis on raw data. In such instances, a PMG will create various scripts and calculation that will turn the raw data into a meaningful representation of the data set, to identify areas of concern such as:

■ Common types of failures

■ Failures within a specific region or location

■ Issues with a specific end-device type or model

■ Reoccurring issues at a specific time/day

■ Any trends in software or firmware revisions.

Once the raw data is analyzed by the PMG, the results can be relayed to the support team for review so a plan can be formalized to take the appropriate preventative action. The support team will work to present the data and their proposed solution, and seek approval to execute the corrective/preventative steps.

3. Present data in interactive dashboards and business intelligence reports to ensure proper understanding

Not every support team has the benefit of a PMG. In this specific circumstance, it's important that the system monitoring tools are fulfilling the role of the PMG analysis, and presenting the data in an easy-to-understand format for the end-user. From a tools perspective, the data analysis can be approached from both an interactive dashboard perspective, as well as through the use of business intelligence reports.

Interactive dashboards are a great way of presenting data in a format that caters to all audiences, from administrative and management level, and technical engineers. A combination of both graphs (i.e. pie charts, line graphs, etc.) and summarized metrics (i.e. Today, This Week, Last 30 days, etc.) are utilized to display the analyzed data, with the ability to filter capabilities to allow the end-user to view only desired information without the interference of all analyzed data which may not be applicable to their investigation.

In fact, a more "customizable" approach to raw data analysis would be a Business Intelligence Reporting Solution (BIRS). Essentially, the BIRS collects the raw data for the end-user, and provides drag and drop reporting, so that any desired data elements of interest can be incorporated into a customized on-demand report. What is particularly helpful for the user is the easy ability to save "filtering criteria" that would be beneficial to utilize repeatedly (i.e. Monthly Business Review Reports).

With routine errors, the main goal is to stay ahead of them by using data to identify correlations. Through effective event correlation, and by empowering teams with raw data, you can ensure that issues are quickly mitigated and don't pose the risk of impacting company ROI and system availability.

Collin Firenze is Associate Director at Optanix.

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Losing $$ Due to Ticket Times? Hack Response Time Using Data

Collin Firenze

Without the proper expertise and tools in place to quickly isolate, diagnose, and resolve an incident, a quick routine error can result in hours of downtime – causing significant interruption in business operations that can impact both business revenue and employee productivity. How can we stop these little instances from turning into major fallouts? Major companies and organizations, take heed:

1. Identify the correlation between issues to expedite time to notify and time to resolve

Not understanding the correlation between issues is detrimental to timely resolutions. With a network monitoring solution in place, lack of automated correlation can generate excess "noise." This then requires support teams to act on numerous individualized alerts, rather than a single ticket that has all relevant events and information for the support end-user.

The correlated monitoring approach provides a holistic view into the network failure for support teams. Enabling support teams to analyze the network failure by utilizing the correlated events to efficiently identify the root cause will provide them the opportunity to promptly execute the corrective action to resolve the issue at hand.

Correlation consolidates all relevant information into a single ticket allowing support teams to largely reduce their staffing models, with only one support engineer needed to act on the incident as opposed to numerous resources engaging on individualized alerts.

2. Constantly analyzing raw data for trends helps IT teams proactively spot and prevent recurring issues

Aside from the standard reactive response of a support team, there is substantial benefit in the proactive analysis of raw data from your environment. By being proactive, trends and failures can be identified, followed by corrective and preventative actions taken to ensure support teams are not spending time investigating repeat issues. This approach not only creates a more stable environment with fewer failures, but also allows support teams to reduce manual hours and cost by avoiding "wasted" investigation on known and reoccurring issues.

Within a support organization, a Problem Management Group (PMG) is often implemented to fulfill the role of proactive analysis on raw data. In such instances, a PMG will create various scripts and calculation that will turn the raw data into a meaningful representation of the data set, to identify areas of concern such as:

■ Common types of failures

■ Failures within a specific region or location

■ Issues with a specific end-device type or model

■ Reoccurring issues at a specific time/day

■ Any trends in software or firmware revisions.

Once the raw data is analyzed by the PMG, the results can be relayed to the support team for review so a plan can be formalized to take the appropriate preventative action. The support team will work to present the data and their proposed solution, and seek approval to execute the corrective/preventative steps.

3. Present data in interactive dashboards and business intelligence reports to ensure proper understanding

Not every support team has the benefit of a PMG. In this specific circumstance, it's important that the system monitoring tools are fulfilling the role of the PMG analysis, and presenting the data in an easy-to-understand format for the end-user. From a tools perspective, the data analysis can be approached from both an interactive dashboard perspective, as well as through the use of business intelligence reports.

Interactive dashboards are a great way of presenting data in a format that caters to all audiences, from administrative and management level, and technical engineers. A combination of both graphs (i.e. pie charts, line graphs, etc.) and summarized metrics (i.e. Today, This Week, Last 30 days, etc.) are utilized to display the analyzed data, with the ability to filter capabilities to allow the end-user to view only desired information without the interference of all analyzed data which may not be applicable to their investigation.

In fact, a more "customizable" approach to raw data analysis would be a Business Intelligence Reporting Solution (BIRS). Essentially, the BIRS collects the raw data for the end-user, and provides drag and drop reporting, so that any desired data elements of interest can be incorporated into a customized on-demand report. What is particularly helpful for the user is the easy ability to save "filtering criteria" that would be beneficial to utilize repeatedly (i.e. Monthly Business Review Reports).

With routine errors, the main goal is to stay ahead of them by using data to identify correlations. Through effective event correlation, and by empowering teams with raw data, you can ensure that issues are quickly mitigated and don't pose the risk of impacting company ROI and system availability.

Collin Firenze is Associate Director at Optanix.

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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