Data Difference: What Is It And Why Do You Need It?

Recognizing data differences is key to rectifying errors, enhancing data quality, and supporting effective data governance, especially when working with large datasets and diverse systems.

Data difference denotes the identification of inconsistencies between two datasets.

Hashem Raslan

March 8, 2024

Data difference refers to the discrepancies or variations found when comparing two sets of data. Think of issues like missing records, variations in record values, and schema changes. These inconsistencies, often identified too late, can lead to increased costs and tedious remediation efforts.

To bridge this gap, Telmai is introducing its innovative new Data Difference feature, designed to automatically track inconsistencies in data movement, identify issues, and provide detailed insights into the differences in a machine-readable format. It can handle data of any scale, from megabytes to terabytes, and boasts over 250 integrations, facilitating data comparison across diverse systems and file formats.

Imagine an e-commerce company that manages vast amounts of data across multiple systems and regularly transfers customer information and order details across various systems like databases and cloud data warehouses. Any inconsistency in data movement—like an updated customer address not correctly reflected in the order processing system— can lead to significant issues, such as orders shipped to incorrect addresses. In such scenarios, the ability to accurately track and rectify data discrepancies becomes critical to maintain operational efficiency and ensure customer satisfaction.

How Does Data Difference Work?

First, users configure two data sources for comparison and define their relationship. These data sources can vary in type and source; such as one can be a CSV file, and the other is DeltaLake source. Telmai will then scan these datasets, identify discrepancies, and report them. The report will include information on missing or new records, record value variations, and schema changes. The differences are then compiled into a downloadable file for review.

Check out the practical demonstration with a dataset from Kaggle that illustrates the feature’s effectiveness. The tool accurately identified the changes by modifying a duplicate dataset – deleting and altering records – and then running Telmai’s Data Difference scan. Although the current version requires defining an ID attribute and prioritizes certain features, future enhancements are expected to expand its capabilities.

Elevate Your Data Management from Reactive Correction to Proactive Quality

Ensuring accuracy and reliability in your data isn’t just a routine task—it’s about excellence in execution. Telmai doesn’t merely correct data anomalies; it proactively spots and isolates them early. This means the data coursing through your systems is dependable and consistently so.

Are you prepared to not just control but master your data quality? Try Telmai today and discover how it can transform and streamline your data management process.

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