Key takeaway
Treating all telemetry data uniformly, with equal value and and priority, results in unsustainable costs and reduced agility.
Most enterprises continue to route all telemetry data — regardless of purpose — into expensive, high-performance storage platforms. But this legacy model fails to distinguish between the needs of immediate operational use and long-term exploratory analysis, driving unnecessary spend and reducing flexibility.
Different data consumers require different data types, formats, and tooling access to extract business value.
Operational users (e.g., SOC analysts, SREs) require low-latency access and full indexing for real-time detection and dashboards. Investigative and exploratory users (e.g., threat hunters, data scientists) can tolerate longer query times and prefer flexible formats like JSON or Parquet. Audit and compliance users prioritize historical completeness over performance. Because of these differing needs, a single-tier approach creates contention, compromises, and cost overruns.
Policy-based governance is essential to support governed data sharing.
As telemetry use cases expand, including the rise of agentic AI systems, data architectures must support fine-grained access controls, lineage, and data portability. Current tool-centric architectures are ill-equipped to deliver the governance, scale, and agility needed across diverse consumers and workloads.
The performance and security of today’s enterprise depends on telemetry data. The logs, metrics, and traces emitted by modern architectures, legacy environments, and now AI-driven applications have made this data more important than ever. However, telemetry data’s growing value is dulled by the mounting challenges of managing and analyzing it at scale. The traditional approach of treating all data as equal quickly exhausts budgets and paradoxically slows down the very analytics meant to protect and optimize the business.
On the other hand, a strategic data tiering approach aligns storage performance, cost, and governance to the usage requirements of each dataset. This methodology is urgently needed because it transforms telemetry from a cost center into a competitive advantage by ensuring the right data is available at the right speed and cost for each business use case.
Introduction
IT and Security organizations are collecting more telemetry data than ever before — from endpoints, servers, applications, cloud services, and network infrastructure. This data holds enormous potential to fuel threat detection, incident response, service reliability, performance optimization, and increasingly, automation and AI. But without a deliberate strategy for how telemetry is managed, shared, and stored, most organizations find themselves in a familiar trap of high costs, low agility, and underutilized data.
The traditional approach of centralizing all telemetry in a single analytics platform no longer scales. Every team wants access to the data, but not all data is equally urgent or valuable. Different users have different requirements for performance, retention, tooling, and governance. The result is architectural sprawl, duplicated effort, rigid vendor lock-in, and growing complexity.
To solve this, leading organizations are adopting a tiered telemetry architecture that aligns data storage and access with actual business needs. This strategy classifies data into four distinct tiers:
Operational tier: Real-time data powering alerts, dashboards, and automated responses with high-speed, high-cost, and short retention.
Investigation tier: Search-optimized data used in incident response, forensics, and troubleshooting with medium performance and cost.
Exploration tier: Flexible, semi-structured data used for trend analysis, threat hunting, and machine learning with lower cost and longer retention.
Archive tier: Infrequently accessed data retained for compliance, audits, or retrospective analysis with the lowest cost and longest retention.
Each tier reflects a different combination of urgency, value, and user expectations. Notably, this data tiering strategy supports moving data between tiers as its utility and value changes over time.
Implement a tiered data architecture aligned to business outcomes and data value
Control costs, maximize data utility, and support lifecycle-aware data strategy.
Most enterprises still rely on a monolithic telemetry architecture that treats all data equally — capturing, processing, and storing everything in a single high-performance tier. The closest many come to rationally organizing their data is by temperature — hot, warm, or cold data. While this archaic model may satisfy the needs of basic real-time monitoring and alerting, it is financially unsustainable and operationally rigid. A more effective strategy aligns data storage, access, and tooling with the actual business value and time-sensitivity of each dataset.
A tiered architecture classifies telemetry into logical layers based on how frequently the data is needed for varying users, tools, and use cases.
What’s often missing from telemetry strategies, however, is a rational lifecycle that moves data between these tiers as its value and usage profile changes. For example, data initially stored in the Operational tier for alerting might later be rehydrated to the Investigation tier for forensics, then shifted to the Archive for regulatory retention, or to the Exploration tier to train AI models.
Crucially, each tier delivers different value to the business:
Operational data protects uptime and SLAs.
Investigative data accelerates incident resolution and root cause analysis.
Exploratory data unlocks proactive threat detection, capacity planning, and innovation.
Archived data mitigates compliance risk and provides historical context.
A Cribl-enabled architecture allows for dynamic tier transitions in which data can flow flexibly across storage systems and formats without duplication or vendor lock-in. Instead of being forced to choose between cost and access, organizations can adaptively route and promote data based on current need, business policy, and tooling preference.
By adopting a tier-aware data engine and embedding lifecycle intelligence into telemetry pipelines, IT and Security leaders can finally treat telemetry as a strategic asset — balancing immediate performance with long-term insight and sustainability.
Establish a governance control plane to complement the telemetry data plane
Enable secure, auditable, and scalable data access across tools, teams, and business units.
As telemetry data becomes more central to business operations, it becomes more widely consumed across security, IT, engineering, compliance, and executive teams. Governing its access, usage, and retention becomes a strategic imperative. Yet most organizations still treat governance as an afterthought, enforcing it inconsistently at the tool or storage layer, which leads to fragmentation, risk, and manual oversight.
To address this, enterprises must build a governance control plane that operates both independently of, and in concert with, the data plane. This control plane is the centralized system of record for data access policies, retention rules, usage auditing, and lineage tracking — regardless of where the telemetry data is stored or how it is consumed. It defines who can access what data, when, and under what conditions, and ensures these policies are enforced uniformly across all data tiers and analytics tools.
The governance control plane should integrate with identity and access management (IAM), provide dynamic policy evaluation (e.g., via attribute-based access control), and support audit trails for every data interaction. It must be abstracted from individual tools or formats, allowing policies to persist even as storage technologies, query engines, or AI frameworks evolve.
By decoupling governance from infrastructure, IT and Security leaders can confidently expand data access to new users and systems — knowing that appropriate controls are consistently applied and transparently auditable. This architecture not only enables safe data sharing and collaboration, but also ensures regulatory compliance and minimizes risk in an era of increasing data volume and velocity.
Design telemetry architectures for diverse and emerging consumers—including AI agents
Future-proof data infrastructure to serve new business use cases, automation initiatives, and compliance demands.
Modern telemetry data has outgrown its original use of real-time alerting for IT and Security operations. Today, it powers fraud detection teams, compliance audits, cloud cost analytics, and executive reporting dashboards. Soon, AI agents will join these stakeholders — autonomously triaging incidents, generating remediation plans, and surfacing risks in real time.
To support this growing ecosystem, telemetry architectures must treat data as a cross-functional asset. That means storing data in open, queryable formats (e.g., Parquet, JSON), and enriched with semantic context like MITRE ATT&CK classifications, asset ownership, and sensitivity labels. It also requires exposing data through APIs and lakehouse engines that support both ad hoc human analysis and automated machine workflows.
By decoupling data access from specific tools and enabling governed, policy-driven interfaces, IT and Security leaders can ensure every team — and every agent — can safely extract value from telemetry, without duplicating infrastructure or violating policy. Designing for all consumers today means avoiding technical debt and rework tomorrow.
Conclusion
Telemetry data is one of the most underutilized strategic assets in the enterprise. Yet most IT and Security organizations remain locked into legacy architectures that treat all data the same, regardless of its value, purpose, or audience. This outdated approach drives unsustainable costs, limits flexibility, and hampers the organization’s ability to respond to change.
A tiered telemetry architecture — backed by a composable data engine, a centralized governance control plane, and a lifecycle-aware strategy — enables organizations to break out of this model. It empowers leaders to route data to the right place at the right time, make it accessible to the right users and systems, and adjust the infrastructure as business priorities evolve.
With machine consumers like AI agents joining the mix, and data-driven decision-making becoming table stakes across departments, now is the time to modernize. Enterprises that adopt this model will not only reduce waste and risk, butl also unlock new insights, speed time-to-resolution, and future-proof their telemetry infrastructure against the next wave of innovation.
Cribl provides the foundation for this transformation. The sooner enterprises shift to a flexible, tier-aware architecture, the sooner they’ll start realizing the full value of their telemetry data.