“By 2019, 25% of global enterprises will have strategically implemented an AIOps platform supporting two or more major IT operations functions” - Gartner
As traditional analytics and the data it is meant to analyze grows more dynamic and complex, the role of analytics as we know it is shifting from a tool that drives decision making by delivering data insights, to a role that is driving business processes via both recommending the most appropriate actions to take regarding issue resolution as well as triggering actions to resolve issues in an automatic fashion.
With technology advancing at such a breakneck pace in order to make the lives of IT Ops simpler and more efficient, cloud environments are only getting more complex, and will continue to do so, as data streams from an increasing number of sources with the explosion of IoT, relational databases, CRM and Application log data just to name a few. I’m talking about lots of data, both structured and unstructured. Monitoring in real time is critical in order for AIOps tools to alert IT immediately so as to minimize system downtime and/or support anomaly detection.
The ability to monitor from end to end in real time is paramount so at the end, humans are positioned to make proactive critical decisions. The core of AIOps platforms is that you have machines taking over a huge chunk of the repetitive work that sometimes takes data teams hours. This takes the burden out of human hands and puts the humans in control at the end, when the most vital decisions can make or break an organization’s IT operation.
Before you go out and make the decision to purchase an AIOps solution, you should ask yourself these 4 questions:
1. Does the solution monitor and detect IT issues in real time?
The area surrounding the term real time is a bit cloudy (no pun intended) , and true real time refers to the updates or frequency of retrievals of data points in order to present new information where it feels instantaneous. Universal standards put this time at a second. This translates to the time between when a data point is introduced into the monitoring systems and the creation of that data point (alert, event, metric, etc...) This timing should be one second or less.
2. Does the solution have the ability to analyze historical data?
While ITOA focuses on historic data, many AIOps solutions provide the ability to ingest the plethora of historic data in addition to real time data. You want a solution that has the ability to harness the power that previous customer data provides along with other sources and provide you with data-driven insights that will inform the organization on the best path to resolution.
3. How soon can I realize value from an AIOps solution?
You'd like to purchase a solution that uses the right mix of algorithms and methods that leaves no environmental stone unturned in uncovering any anomalies for example. Some of this mix may require longer term analysis, but a large number of these anomalies should be detectable within a short time period. Detection quality should improve over time as you feed data that will empower learning and the system can analyze longer time periods. Feeding the solution historical data is a big part of giving the system what it needs to learn so it can make the most appropriate and quickest decisions.
4. Will the solution barrage my IT staff with alerts?
You want to implement a solution that will intelligently reduce the number of incidents which is the root cause of alerts. A solution that utilizes an optimal trigger for alerts should use machine learning to prioritize incidents based on crowd sourced feedback and the number of incidents the team receiving the alerts is able to handle. This negates a lot of the "boy who cried wolf" alerts that lead to alert overload.
These are just some of the critical questions that come to mind when selecting an AIOps solution. You really need to ask yourself these as well as any others you feel are most relevant to your business. Ask the vendor, then ask their customers for a more complete perspective and build your short list from there so you may dive deeper. Feel free to chime in below with any questions you feel businesses should ask when looking to purchase an AIOps solution.
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Loom Systems delivers an AIOps-powered log analytics solution, Sophie,
to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and
recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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