Web Analytics · Analytics Engineering

Data Enrichment & Transformation

Every derived table can be traced back to the raw export that still sits underneath it.

Raw GA4 exports often contain duplicate campaign labels, inconsistent channel names, and records that fail to join. We create a governed transformation layer that resolves those issues without modifying the raw exports beneath it.

Activation-ready tables with documented logic, measurable join quality, and the original raw data available for review.

Plan data transformationAll Analytics Engineering & Data Activation tasks
A Zeo specialist moving raw data crates through an enrichment funnel into labeled jars

Some of the 500+ brands we've worked with

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  • Pegasus Airlines
  • TransferGo
  • Canbebe
  • AVVA
  • Country Floors
  • Quick Sigorta

How we run it

Profile the exports, specify the rules, test edge cases, and monitor every release.

We add a maintainable transformation layer while keeping the raw record available underneath. Four steps convert raw warehouse data into documented tables with visible quality controls.

How we hold ourselves to it

  • Preserve raw exports as the evidence layer — Transformations read from a separate raw layer without changing it, allowing anyone to compare normalized outputs with the original records.
  • Standardize repeated business labels — Campaign, channel, and product names follow consistent rules while retaining what each source originally recorded.
  • Measure every failed join — We calculate match rates and expose unmatched records rather than dropping them or assigning guessed values.
  • Date and version every rule — Each historical rule change receives a version and effective date so it does not silently rewrite previous periods.

  1. Profile the raw data

    We inspect actual values, nulls, distributions, and inconsistencies before defining transformation logic.

    Data profile

    AI assist
    Flags null spikes and label variants automatically.
    Human gate
    Analyst confirms which anomalies matter.
    Owners
    Analytics Engineer, BI Analyst
    Illustrated figure examining a search result row through a large magnifier

  2. Specify the transformation rules

    We document normalization, join, and deduplication logic, including test cases for known edge conditions.

    Transformation specification

    AI assist
    Drafts SQL join and normalization logic.
    Human gate
    Your data owner approves the join keys.
    Owners
    Analytics Engineer, Data Owner
    Illustrated figure sketching plans at a drafting table

  3. Build and test

    We implement the approved rules and test them against known-good examples and boundary cases.

    Test results

    AI assist
    Runs the rules against boundary-case fixtures.
    Human gate
    Engineer reviews failed test cases before merge.
    Owners
    Analytics Engineer
    Illustrated figure feeding a data strip through a machine

  4. Release and monitor

    We deploy the transformations and add checks for schema or data drift that could break downstream tables.

    Monitoring setup

    AI assist
    Watches for schema drift after each run.
    Human gate
    On-call analyst approves the drift alert threshold.
    Owners
    Analytics Engineer, Warehouse Admin
    Illustrated figure watching a monitor full of tracked rows

New structure sits on top. The raw data stays underneath, untouched.

Automation profiles the export, flags null spikes and label variants, drafts the join and normalization SQL, runs it against boundary-case fixtures, and watches for schema drift after each run. The calls that shape the data stay human: which anomaly matters, which join key is legitimate, and what a failed test means before anything merges.

What you get

Every transformed table comes with a data dictionary.

You receive usable tables and maintainable documentation rather than an opaque query.

  • Technical specification

    Transformation specification

    Versioned documentation of the exact normalization, join, and deduplication rules.

    Accepted when

    The spec is versioned, and the version that produced the current tables is identifiable.

    Cadence: Versioned at every rule change

  • Working document

    Activation-ready tables

    Clean, joined tables prepared for dashboards, models, or downstream activation.

    Accepted when

    Row counts and category distributions reconcile against the raw source within a stated tolerance.

    Cadence: Rebuilt on the pipeline schedule

  • Reference document

    Data dictionary

    Definitions and source lineage for each derived field so future analysts do not need to reverse-engineer the logic.

    Accepted when

    Every derived field names its source columns and the rule that produced it.

    Cadence: Updated with each derived field

We call it done when: join match rates are measured and documented, unmatched records have a stated disposition, and the raw layer is still queryable.

Fit and readiness

Your exports landed, but the labels still disagree.

Landed exports that still don't agree on labels are usually the first sign this needs to happen.

A good fit when

  • Your GA4 or other exports have landed in the warehouse, but campaign, channel, or product labels are inconsistent across sources.
  • You need joined, activation-ready tables for a dashboard, model, or downstream tool, well beyond raw event exports on their own.
  • Existing transformation logic is scattered across spreadsheets or undocumented queries that nobody fully trusts.

Better handled as other work when

  • You need the GA4 export connection built before any warehouse transformation rules are written or tested. That is GA4 Integrations & Data Activation.
  • You need server-side collection routing rather than warehouse-level transformation. That is Server-Side GTM & Cloud Setup.

If one of these is closer to your situation, start here instead: All Analytics Engineering & Data Activation tasks

We call it done when: the raw data is profiled and the data owner has approved the join keys, so the rules are written against what the export actually contains.

People who build your measurement system

Zeo designs measurement systems that connect a business decision to governed collection and reporting you can check. The people shown here work on the part of that system this page covers.

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Tools we use

Tools behind this work

  • BigQuery

    holds the untouched raw export the page's whole approach depends on leaving alone

  • dbt

    where the transformation rules get built as tested, version-controlled models, not one-off scripts

Next step

Turn raw exports into tables people can actually build on

Show us the raw data and what the resulting tables need to support. We will design transformations that clean and structure it without obscuring the original record.
Plan data transformation

Before we start

Questions teams ask before booking

No. The transformation layer reads from raw exports without modifying them, so the original evidence remains available if a result needs investigation.