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While AIOps is powerful , it ’ s only as smart as the quality and quantity of the data that teams feed into it and this is why observability is also critical .
However , while AIOps is powerful , it ’ s only as smart as the quality and quantity of the data that teams feed into it and this is why observability is also critical . IT teams need to capture detailed metrics and logs from multicloud applications and infrastructure and feed this into AIOps platforms .
This is what enables AI to give DevOps teams the insights they require to optimize applications and deliver better customer experiences .
Taming the data deluge
A challenge arises , however , from the fact that all this data is leading to organizations becoming overwhelmed by the volume of data generated by the thousands of microservices and containers that comprise a multi-cloud environment .
IT teams are finding it difficult to keep up using traditional log monitoring and analytics solutions . As a result , it ’ s becoming more difficult for organizations to ingest , store , index and analyze observability data at proper scale .
Additional challenges are created by the number of data silos that have been created as organizations have used multiple monitoring and analytics solutions for different purposes . This fragmented approach makes it difficult to analyze log data in context , which limits the value of the AIOps insights that an organization can unlock .
At the same time , organizations are often forced to move historical log data into ‘ cold storage ’ or delete it entirely to reduce costs . While this makes log analytics more cost-effective , it also reduces the impact and value it brings to modern AIOpsdriven approaches .
If log data is shifted into cold storage , organizations are unable to use AIOps platforms to query it on demand for real-time insights . The data needs to be rehydrated and reindexed before teams can run queries and gain insights , which can take hours or even days .
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