With data, as in life, it’s easy to lose sight of the critical in the hurly-burly of the important. Important data is like a roof over your head or food on the table. Most people wouldn’t want to imagine life without it, but under extreme circumstances, we can do without for short periods of time. Critical data is like the air we breathe, lose it for even a short while, and the impact is disastrous.
Analogies aside, it’s clear we should take care of our critical data. But how do we differentiate critical data from good ol’ important data?
If you don’t already have a method you use, consider a risk matrix. It’s simple and easy to use. We use a risk matrix for releasing our OPC Software and for Health and Safety.
A risk matrix compares the likelihood of an event with its impact to see if action needs to be taken. Here’s an example:
Clearly anything falling in the critical section needs to be looked at, and action needs to be taken to reduce the likelihood of the event or impact.
Okay. We need to take action. Good start. What form should that action take? Do we tackle the likelihood or impact first? Before we get too ahead of ourselves: how much to spend?
That’s a subject for another post. In the meantime, I suggest taking a peak at a hub-and-spoke architecture.