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.

The Problem: Data Complexity

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

Our Solution: Streamlined and Automated

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.

• Automated Data Reconciliation Between Source and Target Built using Diverse Set of technologies: Our Solution was able to reconcile data between source databases which includes SAP HANA, SQL Server, Oracle, Postgres, JSON, CSV file and target analytics Snowflake platform 
• Centralized Data Quality Rule Catalog: Configured Central Data Quality Rule Catalog that could be applied all data sources
• Automated Alerts: Set up automatic alerts for data anomalies, catching issues early and preventing them from impacting operations.
• Enhanced Data Visibility: Created intuitive dashboards and a central alerting system to monitor and continuously improve data quality.
  
  

  

  The Results: Clear and Impactful Wins

  The changes brought about by 4DAlert were transformative, yielding impressive improvements across various aspects of data management:

   

  

   

  1. 90% Reduction in Downtime: Data reconciliation issues were slashed by 90%, resulting in a smoother and more reliable operational environment. This reduction in downtime meant fewer disruptions in data availability, which in turn led to more consistent and timely decision-making. The improvement in operational stability ensured that data-driven insights were both accurate and readily accessible.
  2. 70% Decrease in Validation Effort: Daily data validation efforts saw a remarkable 70% reduction. The time and resources saved from this decrease allowed the team to focus on higher-priority tasks and strategic initiatives. This boost in efficiency not only improved workflow but also enhanced the ability to respond quickly to emerging business needs and market changes.
  3. Enhanced Data Accuracy and Trust: With automation handling reconciliation and quality checks, data accuracy improved significantly. This increase in reliability helped rebuild trust in the data, making it a more dependable foundation for decision-making. Users could rely on the integrity of the data, reducing the risk of errors and misinformed decisions.

  “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

   

  Technologies in Play