The Hidden Cost of Misrouted Data

Enterprise data volumes are doubling every two years, but security and observability budgets remain mostly flat (or in the worst-case scenario, are declining). As teams struggle to keep up, the challenge isn’t just the amount of data, it’s the inefficiency of how that data is collected, processed, and routed.

Most organizations rely on a patchwork of agents, forwarders, and legacy collectors like Syslog to ingest telemetry from across the environment. These tools were never designed for today’s hybrid, multi-tool ecosystems. They lack the performance, flexibility, and visibility required to support high-scale data environments, let alone empower different teams with the context they need.

The result is expensive overlap and operational drag. The same logs might be collected twice, routed through redundant pipelines, or dumped into the wrong tool entirely. Each misrouted packet adds cost without contributing signal. And when analysts can’t access the data they need in the platform they use, critical threats go undetected and valuable insights are lost in transit.

How Data Complexity Crowds Out Clarity

The impact of poor data routing goes far beyond inefficiency. Sending duplicate or noisy data to multiple destinations increases both cost and confusion. Each redundant log inflates storage and compute expenses without improving visibility or detection. Analysts spend more time sorting through repetitive events  and noisy alerts than investigating what matters.

At the same time, many teams are forced to omit valuable data sources entirely to stay within ingest or budget limits. Firewalls, identity systems, and custom applications often generate high-value signals, yet they are excluded because routing them into existing tools adds too much overhead. These gaps leave dangerous blind spots where threats can hide undetected.

Patchworking multiple ingestion tools only compounds the problem. Different teams depend on different analytics platforms, but outdated pipelines and point solutions rarely integrate cleanly. To simplify operations, leaders restrict who can use which tools and what data they can access. This limits collaboration and reduces visibility across security, DevOps, and observability teams. The result is a fragmented picture of risk where no single team can see the full story.

How AI-Native Data Pipelines Collect Once and Route for Optimum Impact

Traditional forwarding architectures were built for a world with fewer tools, simpler schemas, and far less telemetry volume. Today’s environments demand something different. AI-native data pipelines rethink the entire flow of security and observability data by collecting once, understanding the structure and context of each event, and routing that data to the destinations where it delivers the most value.

An AI-native approach begins by analyzing data as it enters the pipeline. Instead of blindly forwarding logs to a single platform, the pipeline evaluates what type of signal it contains and determines which tools are best suited to process it. High-value security events can be routed to SIEM and SOAR, operational telemetry can flow to observability platforms, and long-tail or raw data can be preserved in low-cost cloud storage for investigation or compliance. This ensures that each tool receives data aligned to its strengths and avoids the waste created by duplicating collection across multiple agents and collectors.

AI-native pipelines also play a critical role in shaping data into formats that downstream tools consume effectively. By normalizing schemas and applying transformations in motion, pipelines reduce the friction of onboarding new analytics platforms and help teams maintain consistent data quality across the entire environment. Instead of building one-off parsers for every destination, teams can rely on the pipeline to align formats and create a common foundation for analytics.

AI adds an additional layer of intelligence by identifying repetitive or low-value patterns in real time. These insights allow the pipeline to summarize high-volume logs before they ever reach expensive indexing engines for massive data reduction. This reduces overall ingest, decreases noise, and increases the likelihood that real signals surface more quickly across the teams responsible for detection, response, and system reliability.

AI-native pipelines also accelerate the onboarding of new tools and destinations. New destinations can be added without launching new agents, rewriting multiple forwarding paths, or rebuilding ingestion logic for each team that needs to analyze security data. This gives organizations the freedom to adopt new tools, test new platforms, or migrate between vendors without disrupting how data is captured or how teams do their work.

What Observo AI Delivers

Observo AI applies the principles of AI-native data pipelines to real-world environments so teams can collect once and route data with precision. The platform evaluates telemetry at ingest, enriches it in motion, and distributes it to the tools where it delivers the most value. This removes the waste created by redundant collection and fragmented routing paths. Customers often reduce redundant data collection by 80 percent or more simply by consolidating the patchwork of forwarders and agents into a single pipeline.

With Observo AI, organizations see immediate impact on both cost and operational efficiency. Infrastructure and ingestion spend can drop by as much as 50 percent because only high-value data flows into expensive analytics platforms. Low-value or raw logs can be routed to data lakes or low-cost cloud storage, lowering long-term retention costs by up to 90 percent. At the same time, normalization, schema alignment, and enrichment happen automatically, so every destination receives clean, ready-to-use data without custom parsers or manual rework.

Observo AI also improves the speed at which teams adopt new tools. New SIEMs, observability platforms, or data lake destinations can be onboarded in minutes using visual routing controls and built-in schema intelligence. This reduces manual routing logic and pipeline maintenance by nearly 70 percent, giving teams more time to focus on security and performance rather than plumbing. By matching schema 100 percent regardless of source or destination, Observo AI gives every team access to consistent, high-quality data across the entire environment.

The end result is a smarter, more efficient data architecture. Teams gain sharper insights because the right data reaches the right tools at the right time. Leaders gain flexibility to evolve their stack without rebuilding ingestion. And organizations save money while improving visibility, coverage, and readiness for whatever comes next.

Real-World Example: Informatica Routes Data to Multiple Security and Observability Tools in Multi-Cloud Environment

Informatica saw their daily telemetry volume climb to 60TB across four cloud providers, the challenge was not only the size of their data but the complexity of routing it to the right tools without overwhelming their Elastic-based security and observability platforms. With more than 70 application log schemas and a mix of cloud-native and custom sources, maintaining visibility had become costly and difficult.

Using Observo AI as an intelligent preprocessing layer, Informatica shifted from a model of collecting everything and pushing it into a single destination to a model where data was evaluated once and routed strategically. High-value security signals continued flowing into Elastic for detection and investigation. Operational and verbose logs were automatically summarized or sent to lower-cost cloud data lakes, reducing strain on storage and compute. This approach allowed Informatica to maintain full visibility while lowering cloud costs by more than 20 percent and reducing application log volume by more than 60 percent.

Smart routing also improved the performance of the tools they depend on most. With repetitive and low-value events removed before ingestion, Elastic dashboards became faster, query performance improved, and both security and observability teams gained clearer, more actionable views of their environment. By eliminating redundant pipelines and removing Logstash from the critical path, Informatica achieved a leaner, more efficient architecture that is easier to maintain and easier to scale.

The improvements in routing and reduction are also shaping Informatica’s next phase. As they expand their use of Observo AI across metrics and traces and deploy pipelines globally, the company is evaluating the Observo Edge Collector to consolidate collection, reduce agent sprawl, and simplify remote configuration at scale. This gives their teams a single, consistent way to collect once and route data to multiple destinations without adding operational overhead. The experience demonstrates how smarter data routing can reduce waste, improve performance, and create a more adaptable observability ecosystem as organizations grow.

“We looked at multiple solutions, but Observo AI stood out. Not just for the performance and cost savings, but because their team worked closely with us to tune the platform for our needs. Within weeks, they helped us onboard dozens of schemas—and the platform has been running smoothly ever since.” - Kirti Parida, DevOps Architect, Informatica

Route Data Smartly and Reduce TCO Across Your Stack

Most organizations send too much data to the wrong tools, which inflates costs and slows down the teams who depend on accurate insights. Observo AI helps security, DevOps, and observability teams collect once, understand each event in context, and route data to the platforms where it delivers the greatest impact.

Customers are reducing redundant collection by more than 80 percent, lowering infrastructure and ingest costs by up to 50 percent, and improving tool performance by sending only high-value, normalized, enriched data to SIEM, SOAR, observability platforms, and cloud data lakes.

Find out for yourself. Request a demo with our engineers to learn how AI-native pipelines can help your team deliver the right data to the right tools at the right time.

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