In the past, IT operations (ITOps) teams were run by independent departments. With the advent of DevSecOps (Development, Security, Operations), integration of development and ITOps has made it easier for teams to work across departments. The growing popularity of hybrid cloud environments accelerated the growth of applications and rapid agile deployments. The increase of hybrid cloud environments in an enterprise generates significant sources of diverse data, including version control systems, code commits, CI/CD operations, automatic scans, policies, test automation systems, change requests, infrastructure and inventory, application logs, among others. In complex IT operations settings, failures are guaranteed to occur unexpectedly, making it difficult for teams to identify the root cause.
One would expect these diverse data sources to provide insight into ITOps from multiple angles. The reality is that the data from varied sources is not necessarily organized, correlated, or centralized, which makes incident resolution challenging. Site reliability engineers (SRE) who are responsible for IT operations deal with analyzing crowded dashboards populated by disparate monitoring tools and diverse data sources without a correlated and centralized source of truth. Despite having a well architected monitoring framework, it can be a herculean task to detect issues that eventually contribute to customer-impacting incidents.
As the volume of management tools has increased exponentially, IBM Cloud Pak for Watson AIOps, an integrated management platform, helps to methodically organize disparate data sources. Cloud Pak for Watson AIOps uses AI technologies to predict and detect events proactively and reactively. The platform sifts through the chaotic data collected, which includes data from diverse monitoring tools and other data sources. Better organization of data and streamlined development and deployment processes yield more useful results using AIOps. Hence, in the process of deploying Cloud Pak for Watson AIOps, there will be a natural evolution of data organization, data pre-processing, and process streamlining. This solution promotes a cultural shift in traditional IT operations because data processes will require less interference and oversight from IT teams.
Despite the intelligence and insights that an AIOps tool can provide, these solutions do not come without challenges as teams implement automation into daily tasks. Before deploying any AIOps solution, teams need to take stock of the types of data available and define from which sources to extract events that occurred. For some data types, model training can be automatic and implicit to the process. For some data types, in order to generate a good model, it is important for a data engineer to be able to filter data based on the incidents in the data, have insights into the data to validate the data for training, scope the data to train only for a smaller set of applications, among other things. Additionally, teams will need to determine data volumes and how to present the data for event extraction within the platform.
IBM Cloud Pak for Watson AIOps allows teams to integrate third-party tools in support of the data ingestion process. As teams go through the process, they can identify how the process must change to fully optimize a seamless end-to-end AIOps solution. The value in tackling data sources ingested into the platform is its ability for the event detection algorithm to avoid calling attention to duplicative events, grouping correlated incidents, and then distinguishing an actionable alert for the SRE to process. An efficient AIOps solution should adapt to seasonality, changing workloads, changing nature of the data, and evolve in a seamless way without much interference from administrators.
Deployment of the Cloud Pak for Watson AIOps platform allows enterprises to derive insights from multiple sources of data, such as logs, metrics, and events. AI technologies detect hidden anomalies that are normally difficult to detect using rules and, in some cases, detects incident-causing anomalies, several hours before the incident occurs. As the AIOps platform evolves, a culture shift becomes inevitable because of the discipline it brings to the processes in various phases of the product lifecycle spanning development, builds, testing, CI/CD, DevSecOps, production deployments, and IT operations.
Learn more about how to integrate IBM Cloud Pak for Watson AIOps by reading the blogs, articles, and other content on the IBM Cloud Pak for Watson AIOps hub on IBM Developer.