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SAS a Leader in Gartner Magic Quadrant for Data Quality Solutions 2021

Organizations today have a critical need for trusted, reliable and high-quality data amidst rapid growth in distributed data landscapes and the scale, growth and diversity of data within enterprises.

SAS has transformed its data quality products by bringing them into the powerful, cloud-native SAS Viya platform.

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SAS data quality solutions facilitate confidence in data accuracy and completeness, empowering better decisions.

SAS is recognized as a Leader in the 2021 Gartner Magic Quadrant for Data Quality Solutions* based on its completeness of vision and ability to execute. The analytics and AI innovator has transformed its data quality products by bringing them into the powerful, cloud-native SAS® Viya® platform. This enables tighter integration of data quality functions with SAS analytics, data integration, data preparation and data governance.

According to the Gartner report: “Data quality has traditionally been mandated to fulfill compliance and governance requirements and to reduce operational risks and costs. Increasingly, data quality also becomes a necessity when amplifying analytics for better insights and for making trusted, data-driven decisions. Data quality is a competitive advantage that data and analytics leaders must continuously engage with in order to achieve those goals.”

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“Creating a data-driven culture is essential for an organization’s success and there is a growing use of and reliance on analytic and AI models,” said Tapan Patel, Senior Product Marketing Manager for Data Management at SAS. “SAS data quality solutions give customers confidence in data accuracy and completeness, facilitating better decisions and driving enterprise value.” 

Unique features integrated into SAS data quality products include:

  • The SAS Quality Knowledge Base, a collection of functions that can be used to perform out-of-the-box or customized data quality operations. It provides quick access to functions (e.g., parse, standardize, pattern analysis, fuzzy matching) to help organizations clean data and infuse trust and reliability in it.
  • SAS Event Stream Processing, which allows organizations to take data quality all the way to the edge and apply data quality functions (e.g., filter, parse, categorize, aggregate, standardize, cleanse) in real time to streaming data from IoT sources, sensors, transactions, devices and more.

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