Why data onboarding is still a major challenge in logging and monitoring (and what it’s costing you)

Getting data into your logging and monitoring platform should be simple. But anyone who’s spent time in "Getting Data In" (GDI) forums knows the reality: onboarding new data sources often becomes slow, manual, and unexpectedly complex—even for experienced admins, particularly when dealing with numerous or custom data sources.

The challenges are rarely about the platforms themselves. They're about the complexity of the imported data, the diversity of data sources, and the manual effort it takes to make everything work. In fact, according to discussions across communities, data onboarding is consistently ranked as one of the most time-consuming and error-prone tasks for admins.

In this piece, we’ll look at:

• The hidden costs of manual onboarding
• When manual processes become a hindrance
• The complexity of today’s data sources
• Why traditional onboarding techniques fail
• How onboarding impacts visibility and team morale
• What a shift toward automation could look like
• The evolving role of the Splunk admin

The hidden costs of manual onboarding

Manual data onboarding isn’t just tedious — it’s expensive. Admins often spend hours configuring props.conf and transforms.conf, troubleshooting field extractions, and fine-tuning source types. These ad hoc efforts can lead to long-term technical debt and inconsistent implementations across the organization.

Meanwhile, delayed or broken onboarding directly slows down critical operations. Monitoring and security use cases suffer, teams lack timely insights, and time is lost chasing down parsing errors. Under constant pressure, staff burn out or move on — and their undocumented fixes leave behind fragile setups that break easily and often.

When manual processes become a hindrance

For small environments with stable, predictable data sources, manual onboarding might seem sufficient. But that illusion fades quickly as the environment grows.

Backlogs build up. Admins find themselves repeatedly reworking configs due to upstream changes. And across the business, teams sit idle waiting for data that isn’t available yet. When the onboarding process becomes a bottleneck, it doesn't just slow down IT — it impacts the entire organization’s ability to respond to incidents, make decisions, or meet compliance.

That’s when automation becomes critical. The moment you’re spending more time onboarding than analyzing data, it’s time to scale the process.

The complexity of today’s data sources

Modern environments are incredibly diverse. A large enterprise might depend on hundreds or even thousands of sources: cloud services, SaaS platforms, IoT devices, custom apps, and legacy systems. Each speaks its own dialect of log data.

Formats change unexpectedly. Documentation is often lacking. A vendor update can silently break your field extractions. Or an IP range change requires reconfiguring your entire pipeline. The pace of change means even “working” setups can degrade quickly — and require ongoing vigilance to stay reliable.

Why traditional onboarding techniques fail

Most teams still rely on manual methods — hand-written props, transforms, and regex-based field extractions. While flexible, these techniques simply don’t scale. Each new source requires deep expertise, careful QA, and often tribal knowledge that lives only in one admin’s head.

As environments expand, manual onboarding turns into a patchwork of inconsistencies. Slightly different treatments of similar data lead to broken dashboards, missed alerts, and unclear ownership. Minor changes upstream can ripple downstream as silent failures, unnoticed until the moment they matter most.

The impact: slower insights, stalled teams, and rising costs

When onboarding falters, visibility suffers. Broken field extractions mean broken dashboards. Delayed ingestion leads to missed alerts and widening compliance gaps. But the true cost isn't just in time—it’s in lost opportunities. Teams spend hours or days on manual data onboarding, and when those resources are stretched thin, key tasks fall through the cracks. With each delay, the risk of costly errors increases. Plus, as admins move on, valuable knowledge walks out the door, leaving organizations with a steep learning curve.

This inefficiency adds up to mounting costs—both in terms of labor and the business impact of slower decision-making.

The shift toward automation and resilience

Leading teams are shifting toward a more scalable model: one that treats onboarding like any other software lifecycle — versioned, testable, and resilient.

They’re standardizing field mappings. Building reusable templates. Creating CI/CD-style pipelines for onboarding validation. Some are even using AI-powered classification tools to detect sourcetypes or auto-generate parsing logic.

The goal isn’t just faster onboarding — it’s repeatability, resilience, and clarity. It’s about getting out of reactive firefighting and into proactive data engineering.

The evolving role of the administrator

In this new model, the Splunk admin’s role shifts. No longer a gatekeeper wrestling with config files, they become an architect of reliable ingestion pipelines and a steward of data quality.

They focus on designing scalable processes, not reworking broken ones. They empower teams to self-serve when possible. And they bring observability closer to the business — not buried under layers of parsing logic.

That’s where the real value lies. Not in managing props.conf — but in helping the organization trust its data and move faster with it.