IT and security teams are drowning in telemetry while budgets barely move. For years, the instinctive move was simple: just ship everything into your favorite analytics platform and figure it out later. That used to work. It doesn’t anymore.
If you keep treating every log line as one opaque blob, you’ll keep paying premium prices to store noise, exposing sensitive data you don’t actually need, and locking yourself tighter into a single vendor’s view of the world.
Data decomposition is how you break that habit.
What is data decomposition?
In a security and observability context, data decomposition means breaking complex records into smaller, meaningful building blocks so you can apply different policies to each piece instead of treating the whole event the same everywhere.
Think in terms of:
Entities – user, device, app, account
Attributes – IP, hostname, role, region, event type, status code
Sensitive elements – names, emails, account numbers, tokens, financial or health identifiers
Contextual metadata – source system, environment (prod/dev), geo, business unit, compliance domain
Once you’ve decomposed data into these parts, you can:
Mask or tokenize sensitive identifiers while keeping operational fields visible
Keep a compact subset hot for detection, dashboards, and search
Push bulky, low-signal payloads (debug logs, verbose JSON, full request bodies) straight to cheap object storage
This becomes the structural foundation for everything we care about at Cribl: routing, reduction, masking, tiering, schema-on-need, and search-in-place.
The problem with “just keep everything”
Cloud object storage made it tempting to say, “We’ll just keep everything forever; storage is cheap.” For IT and security data, that’s no longer true:
Telemetry volume grows aggressively while budgets stay mostly flat
Storing everything in high‑performance analytics platforms is financially unsustainable
Centralizing all data into one vendor stack creates silos, lock‑in, and makes it harder to use telemetry as a multi‑purpose enterprise asset
The real goal isn’t “keep everything.” It’s:
Keep everything you truly need, in the right place, at the right granularity.
Data decomposition is how you reach that goal.
How to recognize “valuable” data
Decomposition is powerful when it’s paired with a clear view of value—especially for IT and security telemetry where the classic 3 Vs (volume, variety, value) really apply.
1. Tie data to real decisions
For each field or group of fields, ask:
What decision does this actually help us make?
Typical answers:
Security – detect threats, accelerate investigations, satisfy audit requirements
Reliability – find root cause during incidents, monitor SLOs, catch regressions
Business – understand customer journeys, usage patterns, risk exposure
If you can’t connect a field to a realistic decision or incident scenario, it’s a candidate to:
Drop
Aggregate
Sample
Or send straight to low‑cost storage with no hot index
In Cribl terms: don’t index what you don’t use, but do keep it cheaply accessible in open formats for when you change your mind.
2. Respect that value changes over time
Cribl’s work on data tiering makes this obvious:
Recent data is often more valuable and accessed more frequently
During an outage or breach, almost all related data temporarily becomes high value
As data ages and incidents close, most fields drop in value—but rarely to zero
That naturally leads to a tiered strategy:
Hot – recent, high‑value data for detection and “right now” dashboards
Warm – data you might need for investigations and troubleshooting
Cold – compliance and rarely accessed data, stored cheaply in open formats
Decomposition lets you choose which parts of an event go to which tier, instead of dragging every field along for an expensive ride.
3. Plan for “unknown value” data
Some telemetry isn’t obviously useful today but becomes priceless during a major incident. For that “unknown value” class:
Keep it in low‑cost, open storage - no heavy up‑front indexing, no hard lock‑in
Use schema-on-need and search-in-place approaches so you can shape and enrich it when you discover you need it
Data decomposition makes this affordable: keep the raw bits cheaply, invest only in the slices that prove valuable later.
Control your data, don’t let it control you
At its core, data decomposition is about control.
When you understand your data at a granular level, what it is, why it exists, who needs it, and how sensitive it is, you can:
Protect what matters most with appropriate rigor
Spend where it counts, instead of paying premium prices to store noise
Keep your options open, avoiding vendor and architecture lock‑in that turns data into a liability, not an asset
Cribl exists to give you that control. Data decomposition, implemented through flexible pipelines, open formats, and schema-on-need, is how you stop keeping “all the things” and start deliberately keeping the right things, in the right place, for the right reasons.
Interested in how to apply this?
Check out our knowledge article on a practical path to data decomposition.
