Four observability trends IT leaders should have on the radar in 2023

Here are four significant observability trends we’ll see shaping IT leaders’ agendas in 2023.

Just as the world began to emerge from the immediate effects of an unprecedented global healthcare crisis, it faced yet another emergency. Soaring energy costs and rising inflation have created strong macroeconomic headwinds that force organizations to prioritize efficiency and cost reduction.

However, organizational efficiency can’t come at the expense of innovation and growth. Otherwise, organizations will quickly fall behind their rivals in an increasingly competitive market. The need to strike this balance correctly will be the dominant factor underlying digital transformation initiatives in the year ahead. In light of this, here are my predictions for the most significant observability trends we’ll see shaping IT leaders’ agendas in 2023.

Four observability trends for 2023

Observability trend no. 1: “Trustworthy AI” will emerge as a required attribute for organizations that need to automate increasingly complex digital ecosystems.

As organizations strive to do more with less and forge ahead through rising macroeconomic headwinds, automation will be critical in 2023. Greater automation will enable organizations to free skilled resources to focus on tasks that deliver the greatest value. As a result, teams can accelerate the pace of digital transformation and innovation instead of cutting back.

However, the growing awareness of the potential for bias in artificial intelligence will be a barrier to widespread automation in business operations, IT, development, and security. Organizations can’t drive automated runbooks with AI that confuses the symptoms of a problem with its root cause, that prioritizes lower-risk issues over ones that have true business impact, or that implements the wrong solutions.

Without trustworthy AI, human operators will continue to feel compelled to manually validate any answers their AI-powered solutions provide. This will negate efficiency gains and hinder efforts to automate business, development, security, and operations processes. Therefore, trustworthiness will emerge as a prerequisite for any AI solution through its ability to provide precise and explainable answers instead of statistical guesses.

Observability trend no. 2: Observability, security, and business analytics will converge as organizations strive to tame the data explosion.

The continued explosion of data coming from multicloud and cloud-native environments, coupled with the increased complexity of technology stacks, will lead organizations to seek new, more efficient ways to drive intelligent automation in 2023. It’s not just the huge increase in payloads transmitted. It’s also the exponential volumes of additional data, which can be harnessed to gain better observability, enhanced security, and deeper business insights.

However, the prevalence of siloed monitoring tools that offer insights into a single area of the technology stack or support an isolated use case has impeded progress in accessing this value, making it difficult to retain the context of data. It also results in departmental silos, as each team remains focused on its piece of the puzzle, rather than combining data to reveal the bigger picture.

To address this, observability, security, and business analytics will converge as organizations consolidate their tools. Teams will seek to move from myriad isolated and hard-to-manage do-it-yourself tools to multi-use, AI-powered analytics platforms that offer business, development, security, and operations teams the insights and automation they need. This convergence will help to tame clouds and the data explosion and drive intelligent automation across multiple areas, from cloud modernization to regulatory compliance and cyber forensics.

Observability trend no. 3: DevSecOps matures into SecDevBizOps as cyber-insurance demands that every innovator is responsible for minimizing risk.

Mitigating cyber-risk will become front-of-mind for everyone involved in innovation, as growing maturity in the insurance industry makes it imperative to treat security as a shared responsibility. Organizations taking out cyber-insurance policies will be required to demonstrate that every innovator in the business can conduct due diligence and manage the risk associated with their actions.

There will be a growing focus on solutions that enable teams to mature their DevOps and BizDevOps-centric strategies into a more holistic SecDevBizOps approach, combining security, development, and IT practices with business analytics. This will lead to increased investment in observability platforms that support cross-departmental processes and ensure everyone has the answers they need to be accountable for delivering secure innovation.

Observability trend no. 4: Data context-driven automation will emerge as a priority for organizations looking to mature basic AIOps into more precise AISecOps.

Organizations will increasingly realize, to be effective, the platforms they use to automate software delivery pipelines and support AIOps need to be data context-driven. That means they need the ability to unify data and its context in a single source of truth, where it can be transformed into precise answers and intelligent automation. This will be key to ensuring that the AI powering automation can distinguish between cause and effect to make smarter and more timely decisions.

Organizations have struggled to maintain this context as the growing complexity of dynamic cloud architectures and increasingly distributed digital journeys have generated an explosion of data and disparate analytics tools. In the coming year, however, organizations will shift their focus from consolidating tools for driving efficient AIOps to embracing platforms that support more advanced AISecOps (AI for security and operations). This will enable teams to break down the silos between observability, business, and security data and bring it together with topology and dependency mapping. As a result, teams will be able to retain the relationship between data streams and unlock the full context needed to drive more powerful and precise automation and deliver seamless digital experiences.