Building a Resilient Marketing Intelligence Data Pipeline
We transformed fragmented marketing data into an automated, structured and dependable source of insight.
Our Customer’s Challenge
A global organisation relied on marketing intelligence distributed across multiple web analytics, CRM and digital intelligence platforms. Data was held in different accounts, properties and report formats, making it difficult to consolidate into a consistent view of digital performance.
Existing automation was vulnerable to variations in the source data. Reports could contain inconsistent row structures, multiple records within a single output, reserved characters and duplicate entries. These exceptions caused database loads to fail, created gaps in the reporting dataset and required ongoing manual intervention.
The organisation needed a more robust integration architecture that could reliably extract, transform and centralise its marketing data while remaining flexible enough to accommodate new sources and evolving reporting requirements.
Our solution
We designed and implemented a cloud-based MarTech data pipeline that collected information from the organisation’s marketing platforms and loaded it into a central, structured database.
AWS Textract was used to convert semi-structured report content into machine-readable data. Bespoke transformation logic then interpreted the extracted content, separated reports containing multiple records and mapped each value into a defined database schema.
Zapier orchestrated repeatable workflows across the different analytics accounts and properties. We strengthened the integrations through data sanitisation, reserved-character handling, duplicate detection and idempotent database operations, preventing individual data anomalies from disrupting the wider process.
We also introduced alert-led monitoring and a structured maintenance approach, enabling integration failures to be identified, investigated and resolved before they affected downstream reporting.
The Results
- Data reliability: Standardised and validated incoming data, minimising failures caused by inconsistent formats, special characters and duplicated records.
- Automation: Replaced repetitive data consolidation activities with repeatable workflows spanning multiple marketing platforms, accounts and properties.
- Reporting readiness: Created a central, structured dataset that could be queried, compared and consumed by downstream analytics and reporting tools.
- Operational resilience: Introduced graceful exception handling so isolated data issues no longer prevented the wider pipeline from completing.
- Data integrity: Implemented duplicate management and idempotent database operations to protect the accuracy and consistency of the reporting dataset.
- Scalability: Established a reusable integration pattern capable of supporting additional properties, data sources and future reporting requirements.
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