When we engage with new clients, one of the top issues is that their technology spending is not aligned with their business needs. While it is also common to see technology that isn’t working right, we find that this is usually an indicator that an organization’s technology spending is not tied to business needs. This misalignment results in money being wasted because it is spent in the wrong places or spending is held back until there’s an emergency, at which time, the solution is usually a patch and more expensive. When we work with clients, we seek to start with a solid understanding of business objectives and drivers. From those drivers, we factor cost vs reward to figure out if various solutions are justified or will be a waste of money. The beauty of this is that we avoid wasting money and, if we determine to move forward with a solution, we have a clear understanding of exactly why we are doing it and what we are getting out of the solution.

When business spending (technology or otherwise) is not properly tied to business needs, it can easily become a money pit where you dump money in with no idea of whether or not it is justified. This, often, leads to tension when spending money and you often spend money on the wrong things and don’t spend it on the right things. In the technology realm, this does lead to technology systems that end up in disrepair because the right money isn’t being spent on the right things. Sometimes, the answer is to spend more money, sometimes it is to spend less money, but, always, it is to make sure you are spending your money on the right things.

I have seen a number of Return-on-Investment (ROI) calculators online but they are never really clear how they figured their number, they ALWAYS justify their solution and it usually weights factors that I don’t value the same way they do. In this blog, I am going to go through three different technology spending decisions that we commonly go through with clients. I will lay out some of the factors we find are valuable. It is my hope that this helps you factor some of your spending, technology or otherwise, to make sure that it is actually giving you an appropriate return.

If you don’t want to get into the details below, please at least take away that ALL of your spending, technology or otherwise, should be tied to business drivers and a clear understanding of the cost and benefit. When you do this, you can be intentional about spending your money in the areas that give you appropriate returns while abandoning the areas that just suck your money without an appropriate return. If you would like to talk more about this and how we can help you make sure that your technology spending is aligned with your business objectives and is properly cost justified, please contact us.

Dual Monitors:

This is a relatively simple evaluation but still has a number of factors that could complicate it. We’ll start with an assumption that moving to dual monitors would cost $300. We’ll also start with the assumption that your average fully burdened employee cost is $15/hr. We have found that adding a second monitor can add 1 hour of productivity per day to a power user and easily adds 15 minutes of productivity of day to even the most basic user. While we could run two separate calculations for these different options, we’ll just average them and say that we get 30 minutes of productivity gain per day when we go to dual monitors. There are some other factors such as increased desk space cost, increased power usage and the morale boost that you get from an employee when you invest in them but we find that these factors are relatively small and tend to wash themselves out. We could also factor the employee cost at their production value instead of their cost but that is an even more aggressive number so we’ll stay conservative. 30 minutes per day equates to 2.5 hours per week which equals 10 hours per month. Multiplied by the $15/hr burden cost, we see that a dual monitor setup returns you $150/month. Your ROI is at 2 months. Again, you can factor these numbers differently but, even with relatively conservative numbers, I’ll take a 2 month ROI on an investment that will have an average lifespan of 5 years any day.

Workstation Lifecycling:

A common conversation we have with clients is that their workstations still work so why not just keep them till they die. We followed this thought process for a while until we were watching the difference in performance between a 4 year old computer and a brand new one. As workstations age, technology advances and the aging workstations are less capable of handling the increasing demands. We have found that, a 3 year old computer causes anywhere from 15 – 30 minutes of increased waiting per day, depending on the user level. We also find that, for every year after the 3rd year, we add about another 15 minutes per day of increased waiting. If we extrapolate that forward, a 3 year old computer creates 5 – 10 hours per month of increased waiting. A 4 year old computer creates 10 – 15 hours per month and a 5 year old computer creates 15 – 20 hours of increased waiting per month. If we average the wait in the middle, and use the hourly burden rate from the first example, compared to a new computer, a 3 year old computer costs $112.50/mo, a 4 year old computer costs $187.50 and a 5 year old computer costs $262.50/mo. If we say that a new computer costs $1,000, replacing a 3 year old computer gives us an 8.8 month ROI, the 4 year old computer gives us a 5.3 month ROI and the 5 year old computer gives us a 3.8 month ROI. Feel free to play with these numbers however fits you, create different brackets or classes of users or whatever works.

Backup Recovery Time Objectives:

When setting up data backups, we start by evaluating the odds that a client will have a failure that requires us to restore their data, the time to restore data and the cost to the client for that downtime. We find that 95%+ of data restores end up being a few files and we can quickly do that whether their data is stored locally or offsite, in our datacenter. The main calculation we perform is in relation to what we call an intermediate catastrophic loss. To us, this is a loss where a substantial amount of data is lost on their server but the facility is still intact. In this case, having a copy of the data backed up locally can substantially speed up the recovery time but it comes at a cost. To figure out whether or not this is justified we do the math. On average, we find that recovering a substantial amount of our client’s data from our datacenter will take 2 days while recovering it locally will take, say, 4 hours. Let’s assume that the company runs 8a – 5p. In this case, the worst case scenario is that we’d see 12 hours of downtime. If the cost of downtime is $1,500/hour, we are looking at a $18,000 cost for the increased recovery time. Factoring that this would statistically happen once every 15 years, that comes to a predicted loss of $1,500/yr IF we do not store a copy of the backup locally. Once we have these numbers together, all we have to do is factor the annual cost of keeping a local backup of the data. From there, assuming there aren’t additional factors to consider (ie. Regulatory compliance, SLA contracts with clients, etc.), it is just a math problem to figure out if keeping a local copy of your backup data makes sense or not.

If you would like help evaluating some decisions that you are working on and/or you would like to talk more about your technology needs, please contact us.