As more business users, analysts, and data scientists access data from new and varied sources, achieving the level of data quality necessary for confident, data-driven decisions has become even harder. Big data lakes, enterprise data hubs, and cloud data storage present new challenges that require organizations to rethink data quality rules and processes designed for use with traditional systems such as data warehouses.
Organizations dependent on big data for a wide range of business decisions need data quality management that can improve the data so it is fit for each desired purpose. Without data quality management, the massive quantities of data organizations ingest will not provide the anticipated benefits—and can even do harm if used to drive faulty business decisions.
This TDWI Checklist Report offers six strategies for improving big data quality. In discussing these strategies, we will look at how managing data quality in big data environments differs from traditional systems.
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