Due to rapid growth and diversity of data sources, deployment models and data types, as well as business and technical users, organizations continue to struggle with manually identifying and inventorying valuable distributed and heterogeneous data assets.
To resolve this issue, the DQLabs augmented data catalog provides a full scan of existing data assets to derive end-to-end views of your data for both governance and compliance. This not only allows the data stewards to discover metadata but also provides an understanding of the data quality associated with each attribute.
For simplified management of metadata, leverage DQLabs ML-augmented data catalog to automate the process of discovering, inventorying, profiling and tagging your data across the enterprise.
Quickly discover and easily extract data catalog information from various enterprise data warehouses, operational databases, enterprise applications, cloud data stores, nonrelational data stores, and many more with just a few clicks using DQLabs out-of-the-box connectors. We not only connect to the data sources via REST-based APIs, XMLs and PDFs but also automate profiling, clustering, indexing and creating of semantic relationships.
ML-assisted search and querying enables you to perform semantic search across all metadata to find the most relevant datasets to browse and filter the derived datasets as needed.
DQLabs provides data quality scoring accompanied with deep dive analysis on any attribute. Perform or assess the impact of usage in data preparation, data reporting or analytics to ensure a higher quality of reports, dashboards and ML models.
This feature includes classification based on the most current data privacy and security compliance regulations — such as the EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) and includes sensitivity levels.
With the DQLabs AI driven DataSense™ module, DQLabs modernizes your meta data, catalog and taxonomy management with integrated AI/ML functions including automated rules and learning capabilities. Various data cataloging tasks such as metadata discovery, ingestion, translation, and enrichment are automated via DQLabs modern machine learning algorithms.
With DQ Lab’s data catalog, you can connect to any offline, online and real time source with just a few clicks using our out-of-the-box connectors. With the AI driven DataSense™ module, DQLabs modernizes meta data, catalog and taxonomy management with integrated AI/ML functions using automated rules and learning capabilities. DataSense™ centralizes all metadata information and includes all relevant business terms, rules, data structures, data schemas, relationships, and metadata from all sources. It also enables search and query capabilities of metadata to facilitate discovery of data assets. Use the patented DataSense™ to
Demand for data drives collaboration. For many data leaders, the mandate is clear: use data to deliver business value. And, with new use cases and data-intensive analytic methods, demand for data has exploded. Innovative data leaders have begun to break down silos within their organizations, and realize that just having a modern data stack is not enough.
Join us on this webinar to learn how the DQLabs platform is the Modern Data Quality Platform eliminates critical data silos by centralizing Data Observability, Data Quality, and Data Discovery into a single, agile AI-driven platform.
12:00 pm: Welcome & Introductions
12:05: pm: Industry Insights: Data Collaboration in the Modern Data Stack
12:15 pm: DQLabs in Action: Top-Down Data Health with the DQLabs Platform
12:30 pm: Questions & Answers
12:45 pm: Close