A leading global manufacturing company with over USD 15 billion in revenue and operations across more than 60 countries faced significant data management challenges. The company, having grown through numerous mergers and acquisitions, inherited over 100 ERP systems and various source systems, all feeding into a centralized Azure data lake and Snowflake analytics platform often led to issues such as data outliers, incorrect file formats, late arrivals, structural inconsistencies, incomplete datasets, and erroneous reference data. These issues caused frequent data loading errors, delays in data availability, and compromised data quality and insights.

Critical Data Issues: The Challenges The Organization Faced

Multifaceted Integration Landscape: Managing and integrating data from more than 100 disparate ERP and source system acquired through mergers and acquisitions resulted in a complicated and fragmented data infrastructure.

Frequent Data Quality concerns: Daily arriving data files contained quality concerns such as erroneous formats, outliers, delayed arrivals, structural errors, and incomplete datasets, impeding effective data loading and usage.

Manual Reconciliation Slowdowns: Manual data reconciliation methods were time-consuming and error-prone, resulting in delays in data availability and an increased risk of mistakes.

Inconsistent Data Frameworks: Variability in file structures and formats across source systems posed issues for data integration and consistency, complicating the data management process.

Delayed Issue discovery: The lack of real-time monitoring and early alert mechanisms resulted in late detection of data abnormalities, prolonging data issues and interfering with the execution of decisions.

Our data came in various formats from different sources, and aligning these structures was a huge challenge. It was tough to maintain consistency and accuracy.

 Steps Taken: 4DAlert Solution Implementation Journey

Implemented 4DAlert: Deployed 4DAlert across the source data feeds, Azure data lake, Snowflake analytics platform, and Airflow data orchestration system to tackle data quality, reconciliation, and observability challenges.

Configured Automated Scanning: Set up 4DAlert to automatically scan all incoming data feeds on a regular basis, ensuring continuous monitoring and early detection of data issues.

Monitored Airflow Logs: Integrated 4DAlert with Airflow to monitor logs and track data orchestration processes, allowing for real-time anomaly detection and disruption management

Reconciled Data in Snowflake: Implemented regular data reconciliation processes within Snowflake using 4DAlert, enhancing data accuracy and consistency of the data being analyzed.

Delivered Training and Support: Provided comprehensive training for stakeholders, developed customized alert dashboards and reports, and offered Level 2 support to ensure a smooth transition and effective use of the new system.

Outstanding Outcomes from 4DAlert’s Data Reconciliation, Data Quality, and Data Observability Module

80% Reduction in Data Quality Issues: 4DAlert identified and resolved data quality issues at the source, significantly reducing issues before data was loaded into Snowflake.

100% Automation of Reconciliation: Achieved full automation of data reconciliation processes, eliminating manual efforts and improving process efficiency.

Faster Data Processing Times: Streamlined data processing workflows and reduced delays, leading to quicker access to data and more timely insights.

Improved Data Availability: Enhanced the reliability of data feeds and data availability, allowing for more accurate and up-to-date information.

Enhanced Decision-Making: The increased data integrity and faster processing enabled better, more informed strategic decisions and operational planning.

“Since bringing in 4DAlert, things have really turned around. The automation has made managing data so much smoother, and the quality issues are much less frequent. It’s definitely improved our data operations”

Technologies in Play

Improve Data-Driven Decision Making with 4DAlert

For our global manufacturing client, 4DAlert transformed how they handle their data by improving data quality, automating data reconciliation, and providing real-time data insights.

With 4DAlert, they achieved remarkable results: a dramatic reduction in data quality issues, 100% automation of reconciliation processes, and faster, more reliable data workflows. It’s not just about fixing problems—it’s about transforming the way your data supports your business.

Curious about the impact 4DAlert can have on your business? Contact us today for a personalized demo and see how we can help optimize your data management.