These days organizations of all sizes rely on data (and increasing amounts and types of it) to make informed decisions, and more of their employees, across roles and departments, need data to execute on their jobs. But are they working with data at a quality level acceptable for the intended purpose?
Often not. Incorrect and inconsistent data is a pervasive problem in small, mid-size and large organizations. It can be caused in many ways, such as user entry error, mismatching, system-related corruption, data consolidation from sources that use different standards and more. Whatever the cause, the results can negatively impact productivity and performance ranging from wasted resources, lost revenue, damaged credibility, low customer satisfaction and non-compliance issues. (We’ve seen some pretty ugly outcomes triggered by data quality issues, including cases where BI systems and processes have to be completely overhauled in order to get back on track.)
Choosing a data quality solution that enables you to continuously improve the information value of your data and make it more suitable for its intended use as well as empower all sorts of users is key. There are many data quality products in the marketplace, but most are expensive and require intensive labor and technical expertise to administer. In comparison, SQL Server Data Quality Services (DQS) is a cost-effective knowledge-driven solution that enables professionals with or without DB skills to perform a variety of data quality operations, such as cleansing, matching and profiling, and can be set up and used in practically minutes. At the core of DQS is a knowledge-base that allows your data experts (typically data stewards) to store and build knowledge about your data, incorporate business rules and then test it for correctness and validity. Once you’ve built the knowledge base, you can continue to improve it and apply its value in multiple data-quality improvement processes. Another cool feature in DQS is the ability to leverage the cloud-based services of reference data providers to help verify the quality of your data. To help you and other data-minded folks learn more about DQS, here’s good DQS FAQ and a technical article Data Quality Services Performance Best Practices Guide. Thanks to everyone at the recent SQL Saturday in Minneapolis for the great conversations and questions asked when we presented on DQS. Keep them coming!