Reconcile data between source and analytics database after every data load
Measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose
Ability to understand, diagnose, and manage data health across multiple IT tools throughout the data lifecycle
An organized inventory of data assets in the organization
Practice in which incremental code changes are made frequently and reliably
Compare two database definitions and apply the differences from the source to the target
Collaborative data management practice
Flowchart that illustrates how “entities” such as people, objects or concepts relate to each other within a system.
Pfizer, a global leader in pharmaceuticals with annual revenues exceeding USD 100 billion, faced major challenges with their data systems. Their data flowed through various ERPs and source systems before reaching their Snowflake analytics platform, creating a tangled mess of inconsistencies and quality issues. Problems often only surfaced when business users noticed them—too late to address without causing delays and inaccuracies.
Pfizer’s data integration process involved numerous systems—Oracle, SQL Server, HANA, and JSON files—all feeding into Snowflake. This complex path frequently led to data inconsistencies and data quality issues. Manual reconciliation and quality checks couldn’t keep up, making it hard to spot problems before they affected business operations.
“We were always catching up on issues with data Accuracy and reconciliation were only flagged when users noticed them, which was frustrating and slow. It hurt our decision-making and caused significant delays.”
Senior Data Analyst, Pfizer
To tackle these issues, we deployed 4DAlert’s Automated Data Reconciliation and Data Quality module within an Azure Kubernetes cluster. This solution transformed Pfizer’s data management by centralizing data reconciliation, data quality checks, and data observability.
Enhanced Data Visibility: Created intuitive dashboards and a central alerting system to monitor and continuously improve data quality.
The changes brought about by 4DAlert were transformative, yielding impressive improvements across various aspects of data management:
“With 4DAlert, we’ve automated our data processes and significantly reduced manual work. Early detection of data quality issues means quicker, more accurate insights. It’s been a game-changer for Pfizer.”
Director, Data Analytics, Pfizer
Transforming Pharmaceutical Data Processes with 4DAlert
4DAlert’s Data Reconciliation and Quality Module helps pharmaceutical companies optimize complex data management. It improves data governance and regulatory compliance while driving innovation and growth. By centralizing data quality checks and automating reconciliation, 4DAlert maximizes data efficiency, giving you clear insights and supporting better decisions in a tightly regulated industry.
Curious about how 4DAlert can revolutionize your data management? Contact us today for a personalized demo and see the difference for yourself.
Pfizer
Pharmaceutical
New York, US
4DAlert Data Reconciliation and Data Quality
1) Analytics Platform
- Snowflake
2) Data lake -
AWS S3
Trino
3) Orchestration -
Airflow
4) Source Systems -
Json files,
CSV files,
SQL Server,
Oracle,
HANA,
3rd Party APIs
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