More important to us is that we also use all that available data to prevent this problem from happening again in the future. To tackle this, we turn to advanced analytics and combine the historic data of the storage component with data science techniques to make predictions of its future state. This predictive or proactive way-of-working enables us to react to this issue before it actually occurs. Main benefit is that this proactive alerting ensures that IT issues do not result in business impact for our customers, such as the degraded application due to the storage issues. A nice side benefit for our own team is that no DevOps engineer must be awoken at night during stand-by because of an issue that could have been prevented.
But there is more benefit in using data for predictive IT-maintenance. It enables us to transform unplanned work into planned work, thus changing from fire-fighting mode to fire-prevention mode which is less stressful and more reliable for those involved. The predictive alerting is one example. Another example is using our data for capacity planning, to ensure we order the right capacity at the right time. We also use data for rightsizing: predicting CPU/memory usage for virtual machines and automatically downscaling or upscaling them just before it is needed. And we use data to simulate changes before we actually carry them out, thus predicting the impact of a certain change on user performance and making sure to plan additional changes along that one change.
An IT data lake to stay in control of your IT environment.
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