Ever noticed that when you’re talking to someone it sometimes seems that what you’re saying and what the other person understands are quite a bit different? Quite frankly, it happens more often than you’d think.
One of my favorite comparisons when it comes to data exchange, my de facto area of expertise, is language—how we humans communicate and exchange ideas, feelings and ultimately, any kind of information.
You see, if we would all speak the same language and all the words would mean the same thing for everyone, life would be incredibly easy and simple. Sometimes perhaps boring, but ultimately we would advance much faster and efficiently as a species if we would all speak the same language. But we don’t—and the simplest example that comes to mind is when I say “red apple” and my french clients say “pomme rouge”.
Now, it would be bad enough that an english speaking person wouldn’t really understood what “pomme rouge” meant, but even if he would, the red apple might might be imagined as a huge, juicy apple as for me, it would be a perfectly red, round apple I once saw in a cartoon. So you see, even if the words describe the same thing, they mean different things for either of us.
And that same applies for systems. Regardless if we’re talking about insurance, travel, health or other verticals, systems (and mostly legacy ones) were not created to “talk” to each other. And even if they do, the importance and the meaning of the data is not the same for different systems. I call this the “common denominator” challenge—having the data talk to each other and mean the same thing.
Another big challenge when it comes to data exchange is redundancy. Not once I’ve seen systems go down due to lack of confirmation of receipt of data. When two different systems are exchanging data redundancy is imperative—not only for saving that data, but because all the downstream information depends on it and corrupt data will not only take the business to a halt but could increase costs in a short time.
A third challenge and one I’d emphasize on is the making-sense-of-it issue. Once you have all your data in the same format and it means the same thing for everyone, you need to make sense of it. What does this data mean? Does it make sense? Is it actually useful or usable in this format? Unless you’re a guru when it comes to deciphering data, you’d better make sure your data exchange system offers you usable and easy to understand data.
Forth—and this is absolutely paramount—is speed. It’s no secret the we live in the age of information. And information nowadays travels with the speed of light (in fibers, that is) and your system better be on par with it. I’ve seen loss of information due to inability to process it fast enough and the loss of revenue caused by this is absolutely insane. Internet packages have a failsafe in place to check for data loss, but all computers on the Internet “speak” the same language. Different information system, built at different times by different people in different places don’t.
At number five I’d like to place using the data in business intelligence-issue. You constantly hear buzzwords like “big data”, but do you know that the second issue after making sense of it is actually using it in business intelligence? Gaining competitive advantage in business is today linked exclusively on how fast and efficiently you use your data. And that might just be the difference between making it or breaking it.
These are just a few of the more obvious issues when it comes to data exchange and in the following weeks I’ll get more into detail with examples, verticals and client interviews on how we solved their data exchange problems.
For those of you who don’t yet know me, my name is Yves de Burbure de Wesembeek and I run Gloobus, a (truly) global software agency that builds state-of-the-art software solutions for the insurance, travel, health and hospitality industries. Our flagship product, the Gloobus Service Bus (GSB) is one of the fastest and most versatile and powerful data exchange systems in the world. If you’re struggling with data from various sources, let’s talk.
