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Imagine you’re driving along a two-lane highway. Just ahead, there’s a car doing 35 mph. You pull out to overtake, step on the gas, and – nothing. The engine is as sluggish as a horse-drawn cart. Finally, you inch past the other car, narrowly missing an oncoming tractor-trailer. Shaken, you make a mental note to take your car to the dealer tomorrow morning.
Welcome to predictive analytics. You avoided catastrophe this time, but next time could be different. Unless you fix the problem, you’re taking your life in your own hands. That sluggish engine is a leading indicator, giving you advance warning of more serious issues down the road. That’s what predictive analytics is all about – forecasting future events, behaviors and outcomes based on current and historical data.
Predictive analytics has been around for a long time, with applications such as weather forecasting and credit scoring emerging in the 1950s. However, IT management software vendors only started to use predictive analytics in the early 2000s, with companies such as ProactiveNet, Integrien and Netuitive positioning predictive analytics as a way to forecast future IT network outages and degradations.
Why First-Generation Predictive Analytics Failed
Unfortunately, these first-generation predictive analytics solutions have a big problem. They tell you there is a potential issue, but they don’t help you to identify and fix it. Instead, they simply measure the normal performance of your IT environment and then tell you when there is an anomaly. It’s the equivalent of that sluggish engine – you know it’s not normal behavior based on previous performance, but you don’t know what the actual problem is, and you certainly don’t know how to repair it.
The result? With first-generation predictive analytics, you still spend huge amounts of time diagnosing and remediating service outages, and meanwhile the clock is ticking. And, by the time first-generation predictive analytics spots an anomaly, it’s often too late. The service is already in trouble. With application downtime costing $100,000 an hour or more, predictive analytics needs to prevent outages and accelerate MTTR to deliver business value. And, first-generation predictive analytics falls short on both counts.
Why Next-Generation Predictive Analytics Works
Fortunately, things have moved on. Next-generation predictive analytics delivers where first-generation solutions don’t. Using machine learning, these platforms predict specific service failures – and their root cause – rather than just reporting generic performance anomalies. They can even recommend the best way to remediate these failures. That means you have actionable information, so you identify and fix issues before they affect your services. And, next-generation predictive analytics gives you business context around these future failures, so you can prioritize them based on their business impact.
How does next-generation predictive analytics do this?
Next-generation platforms don’t just rely on isolated performance metrics. Instead, they combine events and metrics from multiple monitoring sources. Using event data is critical, since it allows these platforms to pinpoint service issues. Then, they use machine learning to identify patterns in this data. For example, they can identify when a particular combination of low-severity events is typically followed by a specific high-severity event a few hours later. When the platform sees this pattern again, it proactively predicts the probability that the high-severity event will happen again.
Intelligent Event Enrichment
Next-generation predictive analytics uses your ITSM data to enrich predicted events, bridging the gap between IT Service Management and IT Operations. This allows it to predict the business impact of events, and to determine the best way to remediate them. For instance:
- By analyzing similar previous incidents, it can predict the probable incident priority, business impact and urgency – so you focus on the most important incidents first.
- By looking at change requests, it can see when event patterns happen during a planned change window – so you only respond to predicted events with a real business impact.
- By looking at previous remediation actions and knowledge bases, it can recommend the best options for fixing a service issue – dramatically accelerating MTTR.
What’s the Result?
With next-generation predictive analytics, you can predict future service outages and take action before they impact your business. And, when you do have a service outage, you remediate it more quickly because you know the root cause and how to fix it. You minimize service downtime, reduce business disruption, increase customer satisfaction, and lower your IT operations costs. It’s everything that first-generation technology failed to deliver.
And, that’s why you need next-generation predictive analytics. If you’d specific information about Evanios’ solution, download the Actionable Predictive Analytics for IT Operations brochure.