In our previous blog post, we discussed how investments in AIOps platforms have been justified on the basis of their ability to decrease mean time to problem resolution and the resultant cost reduction. But one of the challenges most IT Ops teams face is in the approach of deploying AIOps solutions for business-critical use cases: Where do we start? Some use AIOps to analyze unstructured data in order to identify higher-level correlations that traditional IT monitoring tools wouldn’t be capable of.

In our previous blog post, we discussed how we are approaching an important inflection point in the cloud migration timeline. Certain legacy applications will remain on owned infrastructure for the foreseeable future, but the scale and agility offered by cloud platforms offers competitive and operational advantages that most organizations cannot ignore. As cloud adoption became mainstream, many enterprises saw fewer objections to migrating their infrastructure to cloud.

IT monitoring solutions haven’t kept pace with the rapid evolution of modern IT infrastructures. Many organizations still rely on disparate and siloed monitoring tools built on legacy framework that present a fragmented view of IT operations.

In our previous blog post, we discussed how AIOps functionality has been used primarily in support of IT Ops processes that enable monitoring or observation of IT infrastructure, application behavior or digital experience.

In our previous blog post, we discussed the three core capabilities that constituted AIOps solutions: data ingestion and handling, machine learning analytics, and remediation. With an exponential increase in the amount of data generated by all these devices and siloed tool sets, the job of IT Ops can only get more challenging.