AIOps is a powerful alliance formed between DevOps, analytics, machine learning (ML), and artificial intelligence (AI). Many of the goals of AIOps mirror last decade’s push for “big data,” the goal of which was to break down silos and make insights more readily available across the organization. AIOps further enhances both analytics and IT DevOps workflows by enabling automation of both insights and of simple remediation measures.
AIOps can Complete the Digital Transformation Journey Many Enterprises Began More Than a Decade Ago
A full digital transformation, from the perspective of AIOps, means integrating data from all important systems so that insights are readily available. Aggregating business service and IT operational data from across silos allows IT operations and service organizations to readily obtain visibility into how processes actually work, who owns what, and what the experience of the typical customer (internal or external) might be. With such insights, IT can automate remedial actions close to the source of pain, all without the need for a lengthy investigation, permission requests, or identifying product ownership.
“The entire corporate ecosystem of data is needed to paint a rich digital picture of what’s going on in our environment as a whole,” observed Rich Lane, a Senior AIOps analyst for Forrester, in a recent webinar delivered in partnership with Numerify.
The capabilities of modern AIOps systems vastly outpace legacy monitoring systems in terms of process efficiency, and they are able to do so while giving IT leaders both a universal end-to-end view and also the perspective of someone on the outside looking in. All three capabilities are crucial in a business environment that demands an increasingly quick pace, even as business activities become more complex and more distributed – both physically and digitally – than ever.
AIOps Enables Digital Transformation to be More Productive and Beneficial
AIOps promises both greater visibility and stability. One of the most immediate benefits is that IT operations teams no longer need to go digging for the proverbial, “needle in a haystack,” when something breaks. Unifying all business data under one umbrella while pairing intelligence with automation allows for more efficient IT processes, especially in the context of remediation.
In the recent past, siloed systems meant huge gaps in data, even in organizations making a “big data” push. Visibility was frequently limited by domain, and toolsets were largely kept out of the hands of those who weren’t directly using them.
But in the right now, everything must be contextual. Otherwise, IT will be forced to chase problems around blind corners, problems that might need tedious permissions to continue past to further the pursuit.
More realistically, IT needs to be capable of visualizing how the actions of one system have a ripple effect on others. Modern business revenue generation is wholly reliant on digital systems, after all, either directly or indirectly. Lacking visibility can mean only noticing critical insights after-the-fact, often through word-of-mouth.
Aggregating data across domains and throughout the DevOps pipeline can tie all productivity and monitoring tools into one meaningful picture. An alert in the Ops side of things can mean that assignment groups automatically begin by looking at common sources of pain, such as recent changes. Then, they can identify correlations to a recent CI/CD push and dig for errors in relevant log files. Most of this process can occur through automation, and assignment group leadership can quickly notify someone on the Dev side if the CMDB reveals they have ownership.
The steps of the above remediation process travel with a much faster velocity than typical IT fact-finding, permission-hunting missions. Building semi-automated tickets means no one is chasing down the wrong thing, and identifying who has ownership means fewer bridge calls, war rooms, and finger-pointing exercises.
Multi-Domain Visibility Throughout the Enterprise Brings AIOps Benefits to All
Pulling all data into a big lake also allows for other domains to benefit, not just IT services.
For instance, AI analysis can develop a trend-based model for estimating when a critical cloud-based platform is running low on resources. If performance loads tick up slowly, there’s a high chance that no preset thresholds will be crossed, nor might manual analysis pick up on the trend.
However, AI models capable of identifying unusual behavior contrary to benchmarks can show very clearly when resources are being used up faster than expected. Infrastructure teams can respond by dedicating more resources in advance, spinning up instances in anticipation of stressed load capacity. This can be done on an experimental basis, allowing teams to monitor the situation using ML models and respond on-the-fly. In turn, they are prevented from having to expend excess resources to respond to a crisis, and they can deploy resources in a “just-in-time” fashion to ride the wave, in effect, of ongoing performance trends.
AIOps tools can be put in the hands of those whose desks sit far away from IT, metaphorically speaking. With access to end-to-end data, monitoring teams can develop domain-specific dashboards. They can then give access to marketing teams, for instance, to give them instant views of ongoing sales trends and how those trends correlate with ongoing app performance numbers. And compliance officers can be alerted instantly to potential SLA violations, rather than having to discover them weeks after-the-fact during an audit.
The collective result is a more connected organization empowered by its digital tools rather than beholden to them. Automation of remediation steps also allows individuals who have been assigned to “just keep things running” to be reassigned to positions that better fit their talents.
Looking back at IT, there is a vastly reduced need to manually identify ownership, manually request permissions to investigate things like the CMDB, and a dramatically lower need for “war room” summits when something goes awry. Not only is MTTR reduced in this environment, but proactive insights and automated remediation can increase uptime, making enterprises more reliable, more stable, and more functional than ever before.
The Journey to AIOps from the Here and Now
Enterprises need to ensure that AIOps solutions have the ability to offer effective and trustworthy insights, requiring the following characteristics:
- Clean Data – They must have data that is accurate, comes from the most direct/trusted source, and that has meta-information available to enable rapid organization. Since data is never pristine at source, an AIOps platform must have the ability to cleanse and augment data as needed.
- Break Through Siloes – An AIOps platform must be able to be aggregated into a “data lake” or similar architecture so that it can be examined with little latency and obscurement.
- Data Normalization – The AIOps platform must be able to normalize data for direct apples-to-apples comparisons without introducing biases, skewing, or other sources of statistical or analytical error
- Apply Smart Algorithms – Machine learning and AI can join forces to perform trends, identify relevant factors for meaningful KPIs, score problem-based metrics for “next best action” recommendations, and automate remediation of low-level issues. At the very least, algorithms must be capable of highlighting problems and alerting the proper CI owner.
With these necessities in mind, enterprises can look for an AIOps solution that offers:
- Quick time to value
- Forward-thinking (future-proofed) extensibility
Those seeking to adopt AIOps solutions must be able to make the case for ROI both in the near-term and far into the future. The latter criterion listed above is especially important if the enterprise is just starting its transition to cloud, containerization, and other technologies.
With fewer people needed to “just keep the tools running,” and more people able to access insights in near-real-time, organizations can complete their digital transformation and come out the other side much more intelligent – and profitable – than they ever imagined.
Learn more about AIOps and how it can enable digital transformation goals from our recent webinar, with contributions by Forrester analyst Rich Lane: “How AIOps helps IT Change and Service Management be more reliable and nimble“