Skip to main content

Information is Power, But Only If ...

Robin Lyon

IT has access to an amazing amount of data. Often we collect hundreds of data points on one server such as individual processor load, thread state, disk throughput both in and out etc. We then store this in a bin and use this to create a metric called something similar to server performance. When it comes time to provide reports (weekly, monthly and so on) IT then assigns some poor person the job of collating this information. This is usually done by running a report and importing it into a spread sheet and then combining various servers and metrics into some grouping and calling it an application. Then some numbers are calculated and saved in the spreadsheet to create a performance over time graph. The same is done with database numbers, application performance, network statistics etc. This process is then repeated by levels of management combining more numbers into a single number to represent service performance to allow reporting to more senior levels of management.

Given that IT is all about automating processes, this has struck me as somewhat backwards.

Data Management and IT – Operational Intelligence

IT by and large is staffed by realists – the type that don’t respond well to marketing, want solutions and have little time for repetition.

A second reality is that IT is a fledgling science. While it has a century under its’ belt, it has not developed some niceties like the common taxonomy of biology; every company creates its own rankings and groupings of IT functions. Quite often a great deal of resources are used in creating the custom taxonomy.

To add to the frustration of IT managers everywhere, different off the shelf applications also present data in the taxonomy that is coded specific to that application. It becomes more and more difficult to extract and combine data in a meaningful way.

An IT user friendly application should allow its user base to create rules for the grouping of data for reports. By allowing atomic bits of data, such as unused server capacity for a select group of servers, it now can report on the unused server capacity for an application. Using this application data as a new data point, the well-designed application will allow another ad hoc grouping to provide information on an over-all service.

This process of using groups to create other groups goes on as needed until the application is configured to match the taxonomy the company has designed. Instead of complex calculations each month, a one-time setup is created and automation is achieved.

By allowing different data elements to be members of more than one group, we can avoid a second common pitfall such as the question of factoring the time of DNS queries or a multi-application database server.

IT needs to save time, and its internal applications need to accept the reality of reporting against an ever changing data set that is custom to each company that uses it.

Robin Lyon is Director of Analytics at AppEnsure.

Hot Topics

The Latest

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 ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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 ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

Information is Power, But Only If ...

Robin Lyon

IT has access to an amazing amount of data. Often we collect hundreds of data points on one server such as individual processor load, thread state, disk throughput both in and out etc. We then store this in a bin and use this to create a metric called something similar to server performance. When it comes time to provide reports (weekly, monthly and so on) IT then assigns some poor person the job of collating this information. This is usually done by running a report and importing it into a spread sheet and then combining various servers and metrics into some grouping and calling it an application. Then some numbers are calculated and saved in the spreadsheet to create a performance over time graph. The same is done with database numbers, application performance, network statistics etc. This process is then repeated by levels of management combining more numbers into a single number to represent service performance to allow reporting to more senior levels of management.

Given that IT is all about automating processes, this has struck me as somewhat backwards.

Data Management and IT – Operational Intelligence

IT by and large is staffed by realists – the type that don’t respond well to marketing, want solutions and have little time for repetition.

A second reality is that IT is a fledgling science. While it has a century under its’ belt, it has not developed some niceties like the common taxonomy of biology; every company creates its own rankings and groupings of IT functions. Quite often a great deal of resources are used in creating the custom taxonomy.

To add to the frustration of IT managers everywhere, different off the shelf applications also present data in the taxonomy that is coded specific to that application. It becomes more and more difficult to extract and combine data in a meaningful way.

An IT user friendly application should allow its user base to create rules for the grouping of data for reports. By allowing atomic bits of data, such as unused server capacity for a select group of servers, it now can report on the unused server capacity for an application. Using this application data as a new data point, the well-designed application will allow another ad hoc grouping to provide information on an over-all service.

This process of using groups to create other groups goes on as needed until the application is configured to match the taxonomy the company has designed. Instead of complex calculations each month, a one-time setup is created and automation is achieved.

By allowing different data elements to be members of more than one group, we can avoid a second common pitfall such as the question of factoring the time of DNS queries or a multi-application database server.

IT needs to save time, and its internal applications need to accept the reality of reporting against an ever changing data set that is custom to each company that uses it.

Robin Lyon is Director of Analytics at AppEnsure.

Hot Topics

The Latest

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 ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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 ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...