Data Driven Intelligence

Everyone needs access to information that will quickly and accurately help them make decisions on time-sensitive matters.

For IT departments, this is no different. They need information that will quickly help them determine the root cause of an incident so they can solve it as quickly as possible. That way, the outage will not greatly impact the business and their customers.

But why stop there when you could have this information before something impactful happens? Imagine a world where your team is alerted of a potential incident 30 minutes before it even happens. You could take appropriate measures to prevent it from happening at all.

What benefits do you gain from implementing a predictive strategy like this?

• You maintain your Customer Experience. Preventing outages keeps your customers’ experience intact.
• You keep your revenue unimpacted. When the customer isn’t impacted, you prevent a decrease in revenue because of downtime (and customers leaving due to poor experience).
• You increase productivity. Internal teams have more time to spend on the organizational initiatives that help move the business forward, instead of remediating problems and getting stuck in war rooms.

Just having the data, and a place to house it isn’t going to get you to a predictive state.

You need to apply machine learning, a subset of AI, to the historical and real-time data you’ve collected and use it to help predict highly likely and potential future events.

Machine learning allows you to:

• use event data and apply analytics to reduce event noise, false alerts, and rule maintenance so you can easily identify the business-impacting problem that needs to be prioritized and addressed;
• detect anomalies by looking at your data, baselining operational patterns and using statistical measurements to determine threshold variability patterns;
• dynamically adapt thresholds to this changing behavior and anomalous activity.

Having a platform that can take a look at these patterns is where machine learning can help you. By indicating what is normal and what is not, and by flagging anomalies, the platform directs you towards a more predictive and preventive approach to IT.

You may be familiar with the term ‘Artificial Intelligence for IT Operations’ (AIOps).

Gartner defines AIOps as follows:’AIOps platforms are software systems that combine big data and AI or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.’

What does automating and replacing these tasks really mean, and where does prediction come into play?

With a solution where you can combine machine data and apply AI to your historical and streaming data, you can predict imminent outages and thereby prevent them from ever occurring.

By reaching a predictive and preventive IT state you can:

• Decrease the mean time to detect (MTTD)
• Increase the mean time between failures (MTBF)
• Decrease the mean time to investigate (MTTI)
• Decrease the mean time to resolution (MTTR)