“Engineers like to solve problems. If there are no problems handily available, they will create their own problems.”
Scott Adams.
As Scott Adams of Dilbert fame stated, us Engineers love to solve problems. In the world of OSS we don’t NEED to create our own because there are already so many that are handily available (although we often do make more of our own anyway for some reason).
One of the problems that awaits us on every job is the law of cascading problems. It goes something like this:
Let’s say we have data sets where our data accuracy levels are as follows:
That all sounds pretty promising doesn’t it (I’ve seen data sets that are much less reliable than that)? If we create an end-to-end circuit through all of these objects, we end up with a success rate of less than 30% (ie multiply all these percentages together).
The data accuracy of individual sets of data can lull many an OSS data newbie into a false sense of security. The combined effect of greater than 70% fallout quickly gets their attention though (and that of their bosses if they’re oblivious to the law of cascading data problems too).
How do you fix it? Go back to the source. Find ways of making your source data more accurate. There are usually no quick fixes, just lots of remediation effort.
This earlier post provides some helpful hints on how to improve your data integrity. Do you have any other suggestions to add to the list?
Have you ever had an OSS or BSS project that’s impacted by poor data quality? The problem of data integrity gets raised on almost every
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Here at Passionate About OSS, we are exactly that – Passionate About OSS (Operational Support Systems). We’re also passionate about BSS, NMS, or any other names you call the tools that help to operationalise your network.
