Enterprise Data Quality Fabric
Simplify data management and deliver high quality data products to humans and machines.
Data Quality Fabric is the self-driving car of data management. We automate the tedious aspects of the journey, so that you focus on creating value from data.
Enjoy the Data ride!
These forward-thinking companies are already reaping tangible benefits
Up to 40% higher data accuracy
- Better data available for use right away
- More accurate reporting
Up to 60% faster data processing
- Lower hardware and processing costs
- Faster delivery of data
Forget lengthy “data projects”
Remove friction and automate the delivery of accurate and reliable data to those who need it, whenever they need it.
Data Quality Fabric
What exactly is a Data Quality Fabric?
A Data Quality Fabric is a new iteration of the data fabric architecture that embeds data quality services throughout the whole data lifecycle and ensures the delivery of high quality data.
Your enterprise data instantly available to humans and machines as high quality data products, with governance and compliance ensured automatically.
Data Quality Fabric
Simplify master data management
A leading Canadian retailer simplified the matching process, speeding up data processing by 40% and reducing compute costs by 25%.Learn more
Simplify regulatory reporting
A large UK banking group automated the production of regulatory reports for BCBS 239 and implemented continuous data quality monitoring.Learn more
How and most importantly why does it work?
1. Connect your data sources
Add all of the systems that store critical business data.
2. Capture and ingest metadata
Data Quality Fabric captures metadata from the connected sources by profiling its data.
You can also import and create business term definitions, data quality rules, DDLs, data models, and any other metadata.
3. AI infers additional metadata
Data Quality Fabric constantly monitors data for anomalies, data quality problems, and changes in data content.
But your data stewards are always in control to accept or reject metadata created by AI.
4. Use metadata to automate data governance processes
Set up metadata-driven data pipelines that work regardless of the actual data source, such as:
Data quality monitoring
5. Consume reliable, accurate data
No matter whether it’s a business user consuming data or an API, both will get valid, accurate data.
Why did we put data quality into the data fabric?
Because connecting data silos is not enough. To derive meaning from data and integrate data from these silos, you need to embed data quality processes throughout the data lifecycle. Here is how:
Discover the Ataccama
Ataccama ONE is a full stack data management platform.
See what else you can do.