A Practical Path to Data Decomposition

Data decomposition is the process of understanding and organizing your data so you can make intentional decisions about what to keep, where it should live, and how it should be used.

It’s building a working blueprint of your data: what’s coming in, how much of it there is, who owns it, who relies on it, and what requirements apply. With that blueprint, you can ensure the right data is available to the right teams, at the right time, without overpaying to store everything everywhere.

This guide walks through a practical path to applying data decomposition.

Step 1: Pick a Small, High-Impact Set of Sources

Great starting points:

• Authentication / identity logs
• Firewall / proxy logs
• Cloud audit logs
• EDR / endpoint telemetry

Document which teams use them and for what. This gives you a concrete scope and real stakeholders.

Step 2: Break Records Into Entities and Fields

For each source:

• Identify entities (user, device, app, account)
• List attributes (IP, host, role, region, event type, action)
• Flag fields that are clearly sensitive (PII, secrets, financial or health data)

You’re turning “logs” into something your governance and security teams can reason about.

Step 3: Classify Sensitivity and Purpose Per Field

Attach simple tags to each field:

• Sensitivity – public, internal, confidential, regulated
• Purpose – security, operations, compliance, analytics, debugging, unused

These tags become your control plane. They drive:

• Masking or encryption
• Drop/keep decisions
• Routing to different tools and storage tiers

Step 4: Map Fields to Storage and Retention

Use those tags to decide:

• Which fields must live in hot analytics platforms and for how long
• Which should be summarized up front (aggregations, counts, histograms) with raw events going to object storage
• Which are compliance-only and can bypass expensive platforms entirely

This is where you start to see real savings and real risk reduction.

Step 5: Implement Policies in Your Pipelines (The Cribl Part)

Now you need a pipeline that can actually enforce all of this in motion. Whether you’re using Cribl Stream, Cribl Edge, or a homegrown stack, your pipeline should:

• Parse and normalize events into structured records
• Apply field-level policies: drop, mask, hash, enrich, or route based on sensitivity and value
• Land data in open formats (like Parquet or JSON in object storage) so you retain flexibility and avoid hard lock‑in

At Cribl, we call this schema-on-need:

• Some data is rigidly structured for performance
• Some remains raw
• Some is accelerated only when read, not when written

Data decomposition is what makes schema-on-need operationally viable. With Cribl, this looks like:

• Stream to route, reduce, and replay data across hot analytics tools, data lakes, and archives
• Edge to filter, enrich, and normalize data at the source, before you ever pay network or ingestion tax
• Lake to keep raw data in open formats so you’re always one decision away from a new tool, not one migration project away from sanity

Step 6: Iterate Based on Real Usage

Finally, close the loop:

• Review which fields actually get used in investigations, dashboards, and reports
• Look at search and access patterns to refine what should be hot vs. warm vs. cold
• Adjust classifications and policies as your environment and regulations evolve

Data decomposition isn’t something you finish, it’s something you refine.

As your data, teams, and requirements evolve, so should your blueprint. Revisit your classifications, usage patterns, and policies regularly to ensure you’re still keeping the right data in the right place.

Start small, iterate often, and let real usage guide your next decisions.