Most businesses have backup facilities in place to help them in the event of a data breach or physical disaster that renders their offices or data unusable
But how many know that they can retrieve that data and have their business up and running again in minutes?
Server room floods, ransomware, fires – however your data is damaged, lost or digitally encrypted – do you know how quickly you can retrieve it or even if you can? iland found in a recent survey that just 50% of businesses are testing their disaster recovery (DR) plans only annually or at less frequent intervals, while seven percent did not test their DR at all. Of the organisations testing less frequently, half said their disaster recovery plan may be inadequate based on their most recent DR test, while 12% encountered issues that would result in sustained downtime. Zero respondents said that their DR test was completely or moderately successful. Everyone reported experiencing issues.
So, with most companies remaining badly behind the curve, what steps are needed to ensure that you can retrieve your data after a data breach or disaster?
Understanding your data
The datasets of organisations are huge, but the ability to retrieve 100s of terabytes in minutes is like having a spare car in your garage just in case your main one doesn’t work – it’s expensive to have it all waiting on the off chance you need it. And the faster you need it back, the more it costs.
Therefore, a core aspect of a DR strategy is to prioritise the data that is most critical to the business and focus your efforts around protecting that data first. To understand your data, look at your entire estate and define what’s critical to your business operations. Prioritise it in order of how it would impact customer delivery most if lost. It will give you a focus, and in turn, you can develop measures to minimise data loss in the event of a cyber-attack or disaster. You can also catalogue it by how much data can be lost by invoking a recovery (RPO) and its priority for recovery (RTO).
Obviously, there is a cost implication for any backup and with datasets increasing, it can be very expensive to store all…