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Time is Money

Robin Lyon

Time is an important measurement of IT service, especially if we use transaction time. Time is well understood and begins to answer some of the fuzzy questions such as slowness and what is performance. Of course there are other great questions in IT and one of the most dreaded is: "How much does this application cost?" This question creates countless man hours of work quickly running into the diminished returns of hours spent vs. accuracy.


Here is an enumerated example:
 
1. The cost of the actual application (license, lease etc.) + depreciation as appropriate.

2. The cost of maintenance agreements.

3. The cost of the man power supporting the application (often fractions of various head count.)

4. The cost of the dedicated hardware supporting the application.

5. The proportion cost of shared hardware and software such as Databases and SAN space.

6. The proportion cost of network equipment + and then network support hours.

7. The cost of data center space + power + environment.

8. The proportional cost of management.

9. The cost of shared services such as backup and monitoring.

10. …

As you can see this becomes quite a long list and rapidly becomes time intensive. I remember one organization that spent days deciding how to divide the data center power bill into the application numbers. The humorous or sad reality is thousands of dollars of time in meetings was used to shift increments of hundreds of dollars between the applications. What was disturbing is at the end of weeks of work by most of IT, a reasonable number was returned but what it didn’t show was one of the greatest and most forgotten costs of an application, that of user time. There are good reasons for this such as "user time is not part of the IT budget" or "how could we possibly calculate that number to any accuracy?"
 
Now that we have a method to understand transaction time, we can understand the cost of slow application. A simple formula is (the number of transactions) x (the average transaction time) x (the cost of loaded headcount per time).

This is not perfect, nor do I want to make perfection the enemy of good. It is reasonable to say if a user waits more than a minute for a result, they start multitasking. This can be corrected by ignoring transactions longer than one minute for this simple formula. There are other exceptions and all can be corrected for, but let’s take an example application and figure out some numbers.

We have an application that 600 users use 60 times a day with an average transaction time of 10 seconds. That comes out to 36,000 transactions or 360,000 seconds or 100 hours. HR tells us that our loaded headcount is 40 dollars an hour so we have $4,000 per day of lost time spent waiting for application response. This is a shocking number; it often exceeds the total cost from the tedious exercise of calculating an application cost. Other ways to think of this number are $88,000 per month or 12.5 people doing nothing but waiting every single day.
 
Fortunately, with information comes opportunity. There are several beneficial ways to use this discovered cost. One way is it may help reluctant organizations understand the importance of IT and good systems. When the cost is presented to the application owner, they might want to invest in improving application performance. Assume when looking at the application performance we find most the time is spent in the database. After a bit of testing we can see a 25% increase of performance by moving to a DB cluster and the cost of doing this is $100,000. Using our $88,000 cost of time per month we calculate the DB improvement pays for its self in 5 months ($88,000 x .25 x 5 = $110,000) in increased productivity.
 
This number is also a key management number. During the year end budget and priority cycle there are several ways to decide how to assign the all too few resources given to IT. Other than compliance and obsolescence, a strong argument is improving what will gain the most productivity, and money is the understandable measure to use.
 
Businesses run by understanding costs. Application management allows IT to start speaking the same language as rest of a company – one of dollars and cents. An old basic business adage is you can’t manage what you don’t measure.

Robin Lyon is Director of Analytics at AppEnsure.

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Time is Money

Robin Lyon

Time is an important measurement of IT service, especially if we use transaction time. Time is well understood and begins to answer some of the fuzzy questions such as slowness and what is performance. Of course there are other great questions in IT and one of the most dreaded is: "How much does this application cost?" This question creates countless man hours of work quickly running into the diminished returns of hours spent vs. accuracy.


Here is an enumerated example:
 
1. The cost of the actual application (license, lease etc.) + depreciation as appropriate.

2. The cost of maintenance agreements.

3. The cost of the man power supporting the application (often fractions of various head count.)

4. The cost of the dedicated hardware supporting the application.

5. The proportion cost of shared hardware and software such as Databases and SAN space.

6. The proportion cost of network equipment + and then network support hours.

7. The cost of data center space + power + environment.

8. The proportional cost of management.

9. The cost of shared services such as backup and monitoring.

10. …

As you can see this becomes quite a long list and rapidly becomes time intensive. I remember one organization that spent days deciding how to divide the data center power bill into the application numbers. The humorous or sad reality is thousands of dollars of time in meetings was used to shift increments of hundreds of dollars between the applications. What was disturbing is at the end of weeks of work by most of IT, a reasonable number was returned but what it didn’t show was one of the greatest and most forgotten costs of an application, that of user time. There are good reasons for this such as "user time is not part of the IT budget" or "how could we possibly calculate that number to any accuracy?"
 
Now that we have a method to understand transaction time, we can understand the cost of slow application. A simple formula is (the number of transactions) x (the average transaction time) x (the cost of loaded headcount per time).

This is not perfect, nor do I want to make perfection the enemy of good. It is reasonable to say if a user waits more than a minute for a result, they start multitasking. This can be corrected by ignoring transactions longer than one minute for this simple formula. There are other exceptions and all can be corrected for, but let’s take an example application and figure out some numbers.

We have an application that 600 users use 60 times a day with an average transaction time of 10 seconds. That comes out to 36,000 transactions or 360,000 seconds or 100 hours. HR tells us that our loaded headcount is 40 dollars an hour so we have $4,000 per day of lost time spent waiting for application response. This is a shocking number; it often exceeds the total cost from the tedious exercise of calculating an application cost. Other ways to think of this number are $88,000 per month or 12.5 people doing nothing but waiting every single day.
 
Fortunately, with information comes opportunity. There are several beneficial ways to use this discovered cost. One way is it may help reluctant organizations understand the importance of IT and good systems. When the cost is presented to the application owner, they might want to invest in improving application performance. Assume when looking at the application performance we find most the time is spent in the database. After a bit of testing we can see a 25% increase of performance by moving to a DB cluster and the cost of doing this is $100,000. Using our $88,000 cost of time per month we calculate the DB improvement pays for its self in 5 months ($88,000 x .25 x 5 = $110,000) in increased productivity.
 
This number is also a key management number. During the year end budget and priority cycle there are several ways to decide how to assign the all too few resources given to IT. Other than compliance and obsolescence, a strong argument is improving what will gain the most productivity, and money is the understandable measure to use.
 
Businesses run by understanding costs. Application management allows IT to start speaking the same language as rest of a company – one of dollars and cents. An old basic business adage is you can’t manage what you don’t measure.

Robin Lyon is Director of Analytics at AppEnsure.

Hot Topics

The Latest

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...