Data Quality in the Age of Big Data

With the rise and evolution of big data and the importance of valid/qualified data, data quality best practices and tools are more relevant than ever. But looking at the past, some adjustments and optimizations to older guidelines are needed, in order to reach success.

While the need for data quality does not change, the adaptation of existing techniques is key for excellence. The scale of big data compared to traditional data sources is immense, which results in more in-depth knowledge of the data process.

Key steps in ensuring high data quality in big data include, but are not limited to:

• Standardization
• Deduplication
• Matching and tool automation
• …

Certainly the ‘matching and tool automation’ is essential for scaling the efforts, as some business users still want to explore the data and remediate it on their own.

Within Modis Life Sciences, we offer data quality services on company data, both providing user-friendly reports as well as remediating the data quality at the source through process optimization and implementing QC checks within the data entry process.

– Yolan Brems

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