Solving data quality at scale for Exinity, a multi-brand fintech operator

When you operate multiple brands across regulated markets, data quality is not just an analytics concern. It is a compliance issue, a marketing efficiency issue, and ultimately a trust issue.

Exinity operates more than five fintech and gaming brands and serves more than two million customers across regulated markets. After 18 months with a traditional CDP, they found themselves spending more time working around their tooling than working with it: solving data problems downstream, maintaining parallel instrumentation, and waiting on vendor fixes for issues that blocked day-to-day operations.

They didn’t need another marketing interface. They needed architectural control.

The breaking point: Downstream fixes and vendor bottlenecks

Exinity moved to server-side tracking to improve site performance and meet privacy requirements. But the migration exposed structural limitations in their existing CDP. Critical attribution context, including UTMs, device attributes, and session data, was lost in the transition, forcing Exinity to maintain parallel client-side SDKs and doubling their instrumentation burden.

That was painful, but the deeper issue was architectural. Routine needs like bot filtering, event enrichment, and jurisdiction-specific PII handling required support tickets and vendor coordination. Governance logic lived in a managed black box, and adapting it to Exinity’s pace of change was slow.

The breaking point came when Google decommissioned Universal Analytics. Exinity’s GA4 connector in their legacy CDP broke and stayed broken for a full year. As Edvard Kristiansen, VP Insights at Exinity, put it: They could not wait for the vendor to fix it, so they built a reverse ETL workaround to route data into GA4 themselves.

That confirmed what the engineering team had been sensing for months: Exinity wasn't preventing bad data upstream. They were repairing it downstream, and increasingly, routing around their CDP instead of relying on it.

Why architectural control mattered more than features

Exinity chose RudderStack for a simple reason: governance enforced at ingestion.

RudderStack takes a different architectural approach than a traditional CDP. Instead of storing data in a proprietary system, it delivers governed customer data directly into the customer’s data cloud, keeping the warehouse as the system of record. For Exinity, that meant BigQuery remained the single source of truth, with no proprietary copy to reconcile, audit, or worry about.

The capability that changed the equation was RudderStackTransformations: programmable JavaScript functions that run on every event before it reaches downstream tools. Instead of filing tickets to adjust data quality rules, Exinity’s engineers write, review, and deploy logic directly.

For a multi-brand operator working across jurisdictions, compliance rules change, data quality issues emerge, and business requirements shift constantly. Transformations let Exinity adapt at the pace of their business, not at the mercy of a vendor’s roadmap.

Governance enforced before delivery, not repaired after

Across Exinity’s use cases, the pattern is the same: Enforce rules before data fans out, not after it lands in downstream tools. If disallowed or malformed data reaches a destination, the damage is already done, whether that means a compliance breach, inflated metrics, or a broken report.

Exinity’s Transformations enforce governance at three levels:

1. Compliance and data security. Exinity hashes PII before events are delivered downstream, helping meet GDPR and related requirements. In privacy-sensitive jurisdictions, they strip IP addresses automatically, without engineers maintaining separate rules for each market. These controls are enforced before delivery, not applied after the fact.

2. Data quality and integrity. Bot traffic is filtered before it reaches analytics and activation tools, preventing inflated metrics and wasted spend. Allowlist and denylist rules ensure only validated events trigger campaigns and only real customers receive messaging. Attribute normalization keeps schemas consistent across teams, so downstream logic does not break when naming conventions differ.

3. Enrichment for activation. Events are enriched in flight using external APIs so marketing can target verified customers with premium offers while steering unverified users toward identity completion. Predictive value models run in the pipeline, letting teams adjust bids for high-value prospects and prioritize retention campaigns for at-risk customers.

BigQuery as Exinity’s single source of truth

With Exinity’s previous CDP, customer data lived in two systems: the CDP’s proprietary database and Exinity’s warehouse. That duplication created reconciliation headaches and complicated compliance audits.

RudderStack eliminated the reconciliation problem. BigQuery became Exinity’s single source of truth, with no proprietary copy to audit separately. For a multi-jurisdiction fintech operator, this simplification has real business impact: one system to govern, one set of definitions teams can rely on.

Operational impact across teams and brands

The shift was immediate. Exinity now maintains consistent data governance across more than five brands and multiple regulatory frameworks, and the benefits extend to every team that depends on customer data.

Marketing teams build segments and trigger campaigns using validated data from the RudderStack pipeline. Product teams route customer feedback into Slack and Teams, enriched with sentiment analysis and customer context. Compliance teams enforce jurisdiction-specific PII hashing and data handling for GDPR. Analytics teams work with clean, consistent data across tools, without reconciling conflicting sources.

Instead of working around their CDP, Exinity’s data team defines the rules of the pipeline at the source, and downstream teams trust the data that reaches them.

From reactive cleanup to proactive prevention

If you operate across brands, jurisdictions, or regulated markets and find yourself continuously repairing data downstream or routing around vendor limitations, the pattern is familiar. Exinity broke it by shifting governance upstream: enforcing data quality, compliance, and schema consistency before events reach downstream tools, with BigQuery as the single source of truth.

That is the shift from reactive cleanup to proactive prevention, and it is what makes customer context trustworthy enough for marketing, product, analytics, and compliance teams to depend on.