From fixing issues to fueling innovation: The growing business case for observability

This year, embracing a leading observability practice will not only be a key priority for organizations but an essential competitive differentiator. Recent data shows that leading organizations with mature observability practices spend 38 percent more of their time on innovation, in contrast with organizations early on in their observability journey. This greater amount of time to focus on product innovation can equate to significant benefits for an organization, such as increased developer productivity, improved operational efficiency and more importantly winning market share.

2024 has shown us that the impact and business value of observability is expanding. It is evolving from a reactive practice to a proactive one where organizations not only use observability for troubleshooting issues but now also to inform their customer experience strategy and to fuel faster innovation.

For the C-suite, observability plays a key role in upholding brand reputational protection through minimizing downtime, helping to satisfy customers and improving digital resilience. From reactively finding and fixing problems to now influencing the product roadmap - it’s clear the ROI of observability has evolved. Let’s dive into how it will continue to below: 

Observability will be essential in the AI era of software development

Observability data will no longer be an afterthought in the software development cycle and instead will be leveraged from the beginning to help organizations improve user experience. This rings even more true today as organizations transition into AI-driven development, in which more code will be generated by AI, and into AI-driven digital systems, where the probabilistic nature of AI output will affect the signals needed to understand those systems’ performance and reliability. By embedding observability earlier in the software development lifecycle, companies can ensure infrastructure and applications, even those built through AI, are monitored from the beginning so engineers can easily trace down any potential errors caused by AI before they negatively impact the customer experience.

Outcomes will be more closely tied to customer experience

Unplanned downtime can inflict significant financial damage to organizations, in the form of regulatory fines, diminished shareholder value, overtime wages and more. Another cost of downtime that is harder to measure is the erosion of customer trust. When a company experiences an outage, it can result in damaged public perception and diluted customer loyalty. And these negative impacts can take some time to overcome -- Splunk’s Hidden Cost of Downtime report showed it can take up to 60 days for a company’s brand health to recover after a downtime incident.

To mitigate such lingering reputational damage, organizations will increasingly look to their ITOps and engineering teams to measure observability outcomes differently and connect system performance to key metrics like customer retention rates, Net Promoter Scores and customer satisfaction. As a result, observability will evolve into a widely valued strategic asset, allowing businesses to use it as a practice to align technical performance with business objectives.

AI will help connect business risk and observability data

At this point, using AI (including machine learning) within observability tools is table stakes. Another way that AI will continue to enhance observability is by enabling practitioners to bridge the gap between business risk and their observability data. Through AI, organizations will be able to easily correlate, summarize and better understand their own data to then drive more strategic, data-driven decisions. 

Business leaders are beginning to understand this -- AI elevates observability to the next level by helping to more quickly assess risks and give the C-suite full insight into what is happening (and what could be improved) within their organization. By leveraging AI-powered analytics, companies can more quickly detect anomalies and predict disruptions to their site or systems well before their customers notice. Combining AI with observability data to assess risk will help business leaders move beyond reactive issue resolution and instead anticipate challenges early on.

The evolving ROI of observability

In the coming year, observability will not only maintain center stage, it will also become more closely integrated into key areas that drive value and revenue across a business. And with that, the ROI of observability will continue to evolve as well as it becomes more closely tied to positive outcomes in product innovation, customer retention rates and an organization's overall digital resilience.

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Patrick Lin is currently the SVP and GM of Observability at Splunk, a Cisco company. He joined Splunk through the acquisition of SignalFx in 2019, where he was the Chief Product Officer. Prior to SignalFx, Lin held a variety of PM leadership roles, including VP Product Management at VMware, the pioneer in x86 virtualization software. Patrick holds an MBA from INSEAD, and MS, BS and BA degrees in Electrical Engineering and East Asian Studies from Stanford University.