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TDWI Checklist Report | Optimizing Data Quality for Big Data

December 20, 2018

For the most part, data quality is data quality, whether data is big or small, old or new, traditional or modern, on premises or on cloud. This means that data professionals who are under pressure to get business value from new data assets can leverage existing skills, teams, and tools when ensuring quality for big data. Even so, “business as usual” is not enough.

Although data professionals must continue to protect the quality of traditional enterprise data, they must also adjust, optimize, and extend data quality and other data management best practices to fit the business and technical requirements of big data. The good news is that organizations can apply current data quality and other data management competencies to big data, albeit with adjustments and optimizations.

This TDWI Checklist report drills down into the adjustments and optimizations in data quality practices required for big data. The report will help user organizations understand technology and business requirements for big data and other new data assets, plus data quality’s role in attaining maximum business value from such assets.

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