SAS® Data Quality

Brilliance isn’t built on bad data.

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Improve your data quality today. Tackle big tasks tomorrow.

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Our experience. Your key to success

Technology shaped by thousands of customers.

At SAS, we’re not new to the data game. In fact, we’re thinking a few steps ahead. Building in matching, standardization, de-duplication, address parsing – it’s all part of SAS Data Quality. In multiple languages and for locations all over the world. You get a solution that’s built on experience and customized just for you.

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A new approach


Learn more. Know more.

Some people see data quality in terms of black and white – it’s either right, or it’s not. But at SAS, we see the full spectrum. We know that data quality isn’t just right or wrong. It’s taking things that look wrong and seeing if they’re actually right. How? With matching logic. Profiling. De-duplicating. And above all else – innovating.

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Data quality that fits you


Better data. When you need it.

Improving today’s data prepares you for tomorrow’s challenges. So it’s important to get your data quality right, as quickly as possible. SAS Data Quality meets you where you are, regardless of your operating system or infrastructure. You can improve data using batch, real-time or in-database processes. It’s data quality, done your way.

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Create a quality culture


Consistent results without chaos.

Even though data cleansing happens behind the scenes, it shouldn’t be a mystery. You need your processes to be clear and logical. Something that’s intuitive for both business and IT. SAS Data Quality makes it easy to manage your entire data quality life cycle – from first data analysis through to correction and monitoring.

Features

Data cleansing

  • Correct nonstandard or duplicate records as well as unknown data types.
  • Establish data hierarchies and reference data definitions.
  • Customize preset business rules to suit your individual needs.

Data profiling

  • Validate data against standard measures and customized business rules.
  • Uncover relationships across tables, databases and source applications.
  • Verify that the data in your tables matches the appropriate description.

Entity resolution

  • Identify individuals across multiple data sources from incomplete relationships.
  • Manage entity resolution routines through advanced fuzzy-matching technology.
  • Create multirecord clusters, confidence scores and scatter plots to determine potential clusters.
  • Recognize when slight variations suggest a connection between records.

Data management console

  • Monitor data quality jobs and view data issues and governance activities.
  • Access all data management activity from a single, common control point.
  • Secure role-based access and actions to authorization for specific data quality tasks.

Data integration

  • Embed data quality into Extract, Transform and Load (ETL) and Extract, Load and Transform (ELT) activities from multiple sources using both traditional batch processing and in-database methods.
  • Transfer data to new or different locations while improving the accuracy and consistency of data through data migration.
  • Match information within or across data sources, standardizing formatting differences.

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