Data quality isn’t a nice-to-have when it comes to running your business. It’s a must. And since SAS puts data quality at the core of everything, you know you’re getting the best technology on the market. Our solution supports traditional and emerging Hadoop, Impala or big data initiatives, with limitless scalability. It works on your terms. And it helps business and IT work more closely together.
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 metadata management and visualization 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.
- 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 data network and lineage components. Align business and IT via the built-in business glossary, secure metadata management and visualization capabilities.
- Data remediation. Route decisions about data issues to the right data steward or DBA for resolution.