Organizations are discovering that IT is a strategic partner in support of business and mission-critical outcomes. This makes finding the right IT solution for the right workload, at the right time — and at the right price — paramount. IT systems are expected to be more agile now than ever, while also delivering and tracking, and not to mention finding, new digital services.

Fortunately, IT transformation is enabling organizations to deliver better business strategy and outcomes, but what about the infrastructure and application layers that truly drive these outcomes?

IT transformation is adding a new paradigm to IT operations. This means having a wide variety of flexible services on demand with each tailored to an application that directly supports a specific outcome. When searching for the right platform, chief information officers need to consider factors such as: How can the organization monitor the risk and health of these business services, and how are metrics like average time to detect and repair associated with IT operations? And, is the organization achieving the business outcome with an acceptable service level that can be measured?

Algorithmic IT operations, or AIOps, answers these questions.

AIOps uses artificial intelligence and machine learning to diagnose and resolve IT issues, in addition to automating lower-level infrastructure jobs. A modern AIOps platform combines big data of all IT components, application/infrastructure relationship context, and machine learning functionality to process data across the IT infrastructure in real-time. This provides the organization with actionable insights and an ecosystem of connect platforms that enhance an enterprise’s overall operations like never before.

But for an organization to jump-start its AIOps transformation, it must first take several preliminary steps to undergo the transformation process.

The foundational element of AIOps is visibility.

Organizations cannot measure, support, or manage what they cannot see. Simple ping and simple network management protocol-based discovery or monitoring tools are no longer adequate in today’s IT environments. This makes it essential for an organization to acquire information across all pieces of its infrastructure, including cloud, network, computing, storage, applications and unified communications, all in real-time. With instant visibility, IT ops teams can better support ephemeral resources, like containers and short-lived virtual machine instances. However, this visibility must extend beyond the infrastructure layer to the application and services layers as well, which requires continuous monitoring of all IT elements that interact with the enterprise.

The second element of AIOps is context.

This means taking a deeper dive into data to understand how every element across the IT infrastructure works together and interact. Context provides executives and stakeholders with the opportunity to understand these multi-layered processes that help organizations achieve their end goals. In today’s transformative IT environment, a workload can transition from a local server to a cloud faster now than ever. It’s also important to understand the relationships between services and logical systems as well. This way, context can deliver actionable insights and relevant information on events, performance, and service assurance without the unnecessary noise or “event storms” that plague legacy monitoring solutions.

The final piece of AIOps is automation.

By leveraging actionable insights through automation, decision makers can understand the risks to their business services. IT operators can also automate repetitive tasks. To accomplish this, organizations should automate their platforms for everything — from discovery and monitoring, to IT service management and asset management. For instance, we are already at the point where we can automate certain actions to a predetermined trigger or set of triggers, creating tickets without human input. But automation can go further. If the same ticket from the same set of triggers is generated multiple times, machine learning can make these situations a non-issue by learning how humans have responded in the past and automatically taking that course of action in the future. This would effectively remove humans from the situation entirely and creating a more seamless remediation that occurs at machine speed.

Critical to the three core tenants of AIOps is an accurate and updated configuration management database (CMDB). The CMDB is the single most important resource to keep track of configuration items (CI) and the relationships among them. This is because missing or inaccurate data in the CMDB can lead to difficulties with root cause analysis and longer outages. So while visibility, context and automation make up AIOps, a strong CMDB is the key — it provides a source for information discovery, location, and is ultimately a major player in overall business outcome.

To transform the CMDB to a dynamic, “living” platform, IT leaders should look to automate the CI discovery, device identification, attribute collection, and real-time relationship data through a common model. Once an organization de-silos its data, it can apply machine learning to automate the data in real-time, while also detecting and reacting to issues in the IT infrastructure. This process will lead to significant savings in time and money, and an increase in the speed to detect and respond to incidents.

If your organization is committed to a digital strategy and IT transformation, then improved business outcomes are dependent on modern mission critical applications and IT services. Every application is only as good as the infrastructure and technology underneath it, making AIOps is the future of IT transformation.

Lee Koepping is a principal architect and Greg Mundell is an account executive with ScienceLogic.

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