Why Data Mapping Is the Foundation of Observability

Understanding Your Data

Every failed SIEM deployment and every underperforming observability platform has one thing in common: the organisation did not understand its data before it started building. They selected a platform, deployed agents, connected data sources, and started ingesting. Six months later, they had runaway costs, unreliable detections, and no clear picture of what they were actually collecting. 

Data mapping — the disciplined process of discovering, inventorying, classifying, and documenting your telemetry data — is the unglamorous foundation that determines whether everything built on top of it works or fails. 

Data Classification: Three-Tier Model

Typically 10-20% of sources account for 60-80% of ingestion cost

What Data Mapping Actually Means 

Discovery identifies every source of telemetry in your environment — not just the obvious ones. Most organisations find 30-50% more data sources than expected, significant overlap, and critical services with no coverage at all. 

Flow mapping documents how data moves from source to destination. It reveals bottlenecks, single points of failure, unnecessary complexity, and duplication. 

Classification assigns a value and purpose to each data stream. Not all data is equally valuable, and treating it as such is the primary driver of cost overruns. 

Cost modelling quantifies the financial impact of each data stream. Typically, 10-20% of sources account for 60-80% of ingestion volume and cost. 

 

The Three-Tier Model 

Tier 1 (High Value): Real-time ingestion into SIEM/observability platform at full fidelity. Security events, critical application logs, authentication/access logs. 

Tier 2 (Moderate Value): Aggregated or sampled before ingestion, or sent to warm storage. Infrastructure metrics, verbose application logs, network flow data. 

Tier 3 (Low Value/Compliance): Routed to cheap object storage for retention, not ingested into real-time platforms. Health checks, debug logs, routine audit trails. 

Data Mapping: Four-Step Process

How to Run a Data Mapping Exercise 

Step 1: Inventory Your Sources 

Start with your SIEM and observability platforms — what is configured? Then expand to network infrastructure, cloud environments, and your application portfolio. Build a master inventory with consistent attributes. 

Step 2: Map the Flows 

For each source, trace the complete data flow from generation to final storage. Document every hop. Visualise the flows — a diagram immediately reveals architectural issues invisible in a flat inventory. 

Step 3: Classify by Value 

Assess each data stream across detection value, investigation value, and compliance value. Assign to Tier 1, 2, or 3. 

Step 4: Model the Costs 

Calculate the current cost of each data stream and the optimised cost based on your classification. The difference is your business case for implementing a telemetry pipeline. 

A data map created once and filed away is a snapshot that decays immediately. The data map should be a living document, maintained as part of your operational model. 

 

 

Next Steps 

Ready to take action? Apto Solutions offers a range of entry-point engagements designed to give you clarity before commitment: 

• Free Assessment: A no-obligation conversation with one of our platform specialists to understand your current state and identify quick wins. 

• SIEM Health Check: A structured review of your existing SIEM deployment covering architecture, detection coverage, data quality, and operational efficiency. 

• Observability Maturity Assessment: A framework-driven evaluation of your monitoring and observability capabilities against industry best practice. 

• Data Mapping and Discovery: An analysis of your telemetry data flows, identifying redundancy, gaps, and optimisation opportunities. 

Book your free assessment 

Assessed. Architected. Operated.