Machines don’t die, but they do have a lifespan. Their lifespan is predicated on two things: (1) availability of replacement parts and (2) the availability of people who know how to use and fix them.
Yes, at the end of the day, the useful life of a machine is dependent on people: people maintaining the machines, replacing the parts, and giving the machine a reason for being by utilizing them. So how do you know when is it time to just let go? Like when Kate had to let Leo go in "Titanic".
Here is a look at the career arc of IT professionals who currently know and maintain legacy systems for many insurance companies:
Started in the 60’s… Retired or retiring very soon.
Started in the 70’s… Retiring soon.
Started in the 80s… Often in the top pay bracket or maybe starting to consider retirement.
Started in the 90’s… Is not very happy about working to maintain old systems, is probably taking classes to branch out into what they consider a more long term viable part of the IT universe, likely has a profile on Monster and Indeed.
Started in the 00’s...Doesn't know the legacy systems you are operating on and are surprised when you tell them mainframes are still a thing.
Because the average age of a person who began their IT career in the 80’s is now 60, there is a short window of time which must be utilized to pivot out of the various legacy systems and into a modern platform.
The potential consequence for holding onto legacy systems could be catastrophic, considering that in most smaller insurance companies may have as few as one person with the knowledge needed to maintain a legacy system. The military maxim of “Two is one and one is none” applies here. If that guy has a heart attack on a 7-day Caribbean cruise , what will your company do without him?
The window to move on from many of these legacy systems is starting to close. Because in reality sometimes the grass is greener especially when it comes to matters related to M&A. The p1 service line is specifically designed to assist you by bridging the gap between your data management past and future.