Mapping & Reconciliation

One data model for every platform and every decision.

We align fields, metrics, and naming conventions so data from different platforms works together reliably.

Built for your use case

We design a data schema that fits your reporting and analysis needs one-to-one.
Platform knowledge included

We understand what each platform reports and how its fields and metrics should be interpreted.
Ready to scale

We match and enrich data so reliable, repeatable data processes can grow with your setup.

Multiple API calls

Every required reporting view, brought together

Performance, frequency, and detailed breakdowns often come from separate API calls. We manage those calls and combine their results for your use case.

Complete coverage: we retrieve the reporting views needed for performance, reach, frequency, and relevant breakdowns.
Compatible outputs: we align the different response structures before they enter your data model.
Managed operation: we maintain the calls and their dependencies as platform APIs change.

You define the questions. We handle the API complexity required to answer them.

Fields and metrics

The right definitions, kept current

We select the best available fields and metrics for your use cases and keep the underlying platform data up to date.

Platform-aware selection: we choose fields based on what each source actually provides and how it calculates results.
Clear definitions: platform-specific concepts are translated into a consistent schema your team can use.
Ongoing maintenance: we update the setup when platforms rename, replace, or introduce reporting fields.

Your model stays useful without your team having to track every platform schema change.

Data reconciliation

Different names become one reliable structure

We reconcile inconsistent campaign, creative, audience, and other naming conventions across platforms.

Shared naming: different source values are mapped to one agreed business definition.
Data enrichment: useful attributes can be derived from names, identifiers, or reference data.
Repeatable matching: reconciliation rules become part of the managed process instead of a recurring spreadsheet task.

The result is data that can be compared and aggregated across platforms with confidence.

Formulas and KPIs

Metrics shaped around the way you measure success

We combine values and fields to create calculated metrics that reflect your reporting and decision-making needs.

Cross-source calculations: values from different platforms can contribute to one shared KPI.
Your business logic: formulas follow the definitions and conditions agreed for your use case.
Consistent application: the same logic is applied automatically whenever the data is refreshed.

Your KPIs live in the data process, not in formulas that need to be rebuilt for every report.

Let's discuss your data scheme.

Show us the platforms, fields, and KPIs you need to bring together. We will review the structure with you.

Discuss your data scheme