Make better decisions using data you can trust, and build a data-driven business. Data quality is part of our DNA, so we can help you put it at the core of everything you do. We support traditional relational databases and emerging big data technologies such as Hadoop with enterprise-grade support and scalability. Whether your data is in-stream, in-database, in-memory or in-batch, we help you get it right.
Improve your data where it exists.
SAS Data Quality meets you where you are, addressing your data quality issues without requiring you to move your data. You’ll work faster and more efficiently – and, with role-based security, you won’t put sensitive data at risk.
Manage the entire data quality life cycle.
Data quality isn’t something you do just once; it’s a process. We help you at every stage, making it easy to profile and identify problems, preview data, and set up repeatable processes to maintain a high level of data quality.
Build on decades of data quality experience.
Only SAS delivers this much breadth and depth of data quality knowledge. We’ve experienced it all – and integrated that experience into our products. We know that data quality can mean taking things that look wrong and seeing if they’re actually right. How? With matching logic. Profiling. Deduplicating. And – above all else – innovating.
Promote collaboration by empowering every team.
With SAS Data Quality, IT is no longer spread too thin, because we give business users the power to update and tweak data themselves. Out-of-the-box capabilities don’t require extra coding. Enhanced SAS and third-party metadata management, visualization and reporting keeps everyone on the same page. And more great work gets done.
- Data cleansing. Correct nonstandard or duplicate records as well as unknown data types.
- Data profiling. Better understand your data by uncovering relationships across tables, databases and source applications.
- Entity resolution. Identify individuals across multiple data sources from incomplete relationships.
- Unified web-based console. Monitor data quality jobs, and view data issues and governance activities.
- Visualization and reporting. Create reports and share information about data management initiatives as well as monitor data health and status of remediation issues.
- Data integration. Embed data quality into extract, transform and load (ETL) and extract, load and transform (ELT) activities from multiple sources.
- Foundational master data management. Achieve a single view across multiple sources for one domain.
- Business glossary and lineage. Align business and IT, relate business and technical metadata, and visualize how changes affect other data assets.
- Data remediation. Route decisions about data issues to the right data steward or DBA for resolution.
- In-database technologies. Shorten the time needed for key data quality and analytical processes by carrying out data quality and scoring functions in the database.
Overall, we’ve found that by communicating with our members individually, understanding the cost structure and making sure we optimize all of our marketing efforts, we can raise the same amount of money for our conservation mission with much less expense.