Observability is key in the AI era

Observability is more critical than ever at a time when organizations are embracing generative AI and other AI technologies

Commissioned The adage that you can't manage what you can't measure remains polarizing.

Some leaders lean on analytics to improve business processes and outcomes. For such folks, aping Moneyball is the preferred path for execution.

Others trust their gut, relying on good old-fashioned human judgement to make decisions.

Regardless of which camp you IT leaders fall into - planting a foot in both is fashionable today - it's becoming increasingly clear that analyzing the wealth of data generated by your IT estate is critical for maintaining healthy operations.

Analytics in a multicloud world

Analyzing sounds easy enough, given the wealth of tools designed to do just that, except that there is no one tool to measure everything happening in your datacenter. Moreover, more data is increasingly generated outside your datacenter.

The growing sprawl of multicloud environments, with applications running across public and private clouds, on-premises, colocation facilities and edge locations, has complicated efforts to measure system health. Data is generated in multiple clouds and regions and multiple sources, including servers, storage and networking appliances.

Add to that the data created by hundreds or thousands of apps running within corporate datacenters, as well as those of third-party hosts, and you can understand why data volumes are not only soaring but becoming more unwieldy. Data is poised to surpass more than 221,000 exabytes and hit a compound annual growth clip of 21 percent by 2026, according to IDC research.

Mind you those lofty stats were released before generative AI (GenAI) rocked the world last year, with text, video, audio and software code expanding the pools of unstructured data across organizations worldwide. Unstructured data will account for more than 90 percent of the data created each year, the IDC report found. Again - that's before GenAI became The Thing in 2023.

One approach to measure everything

If only there were an app for measuring the health of such disparate systems and their data. The next best thing? Observability, or a method of inferring internal states of infrastructure and applications based on their outputs. Observability absorbs and extends classic monitoring systems to help IT pinpoint the root cause of issues by pushing intelligence to surface anomalies down to the endpoint.

When it comes to knowing what's going on in your IT estate, observability is more critical now than ever thanks to the proliferation of AI technologies - GenAI in particular. Like many AI tools, GenAI learns from the data it's fed, which means trusting that data is crucial.

Companies implementing LLMs or SLMs may use proprietary data to improve the efficacy of their solutions. They also want to prevent data loss and exfiltration. This puts a premium on observability, which provides a sort of God's Eye View of IT system health.

Observability stacks typically include monitoring tools that continuously track system metrics, logs, traces and events, sniffing out bottlenecks across infrastructure and applications. Data collection tools, such as sensors, software agents and other instruments track telemetry data.

Modern observability stacks leverage AIOps, in which organizations use AI and machine learning techniques to analyze and interpret system data. For example, advanced ML algorithms can detect anomalies and automatically remediate issues or escalate them to human IT staff as necessary.

Moreover, observability and AIOps are taking on growing importance amid the rise of GenAI services, which are black boxes. That is, no one knows what's happening inside them or how they arrive at their outputs.

Ideally, your AIOps tools will safeguard your GenAI and other AI technologies, offering greater peace of mind.

Observability must be observed

Automation often suggests a certain set it and forget-it approach to IT systems, but you must disabuse yourself of that notion. Even the monitoring must be monitored by humans.

For example, organizations that fail to capture data across all layers of their infrastructure or applications can succumb to blind spots that make them susceptible to system failures and downtime.

Moreover, failing to contextualize or correlate data across different systems can lead to mistaken interpretations and makes it harder for IT staff to pinpoint root causes of incidents, which can impact system reliability.

Finally, it is not unusual for thousands of incidents to be buried in billions of datapoints that an enterprise produces daily. IT departments should prioritize alerts based on their business impact. Even so, without the ability to analyze this data in real-time, generating actionable insights is impossible.

In today's digital era, organizations can ill afford to suffer downtime. Gauging system health and resolving issues before they can denigrate the performance of IT infrastructure and applications is key.

You can't watch everything. A partner can.

With so many business initiatives requiring IT resources, it can be hard for IT teams to monitor their own systems. Trusted partners can help you observe IT health.

Dell Technologies CloudIQ portfolio proactively monitors Dell hardware, including server, storage, hyperconverged infrastructure and networking technologies, as well as Dell APEX multicloud systems. Moreover, with its acquisition of Moogsoft Dell is rounding out its AIOps capabilities, supporting a multicloud-by-design strategy for running systems on premises, across clouds and on edge devices.

Sure, going with your gut works for certain things, but would you want to take that tack when it comes to your IT systems? For such a critical undertaking, holistic measurement is the key to managing your IT systems.

Learn more about by clicking this link Dell's Cloud IQ portfolio.

Brought to you by Dell Technologies.

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