In today’s world, diversity and the pace of business demand for data and analytics far exceeds the ability to meet through existing governance capabilities. Navigating through many different types, sources, users, and uses of data assets across an organization is becoming more and more challenging and complex requiring diverse implementation approaches and styles for data and analytics governance. Further its complicated with the lack of a standardized approach to governance blocked its governance objectives.
To align data and analytics governance programs and succeed with data and analytics programs, use DQLabs to automate data and analytics governance implementation by deploying augmented data management.
Leverage DQLabs to deploy out-of-the box standard data governance model with established governance processes, stewardship roles, and information health metrics to improve data quality.
Leverage an ML-augmented data catalog to simplify and automate the process of discovering, inventorying, profiling, tagging, and creating semantic relationships between data assets and understanding data quality.
Build trust in data-driven business outcomes by automating the process of data lineage to traced from the data source to data consumption.
Ability to report and monitor data assets over a period of time along with various aspects of data management such as quality, trend, sensitivity, curation impact, etc.,
Easy and Simple but Centralized Data Security and Permissioning of information assets across data sources, datasets, and even to the attribute level.
Automatic identification of personally identifiable information using semantic type identification across various information assets to avoid exposing sensitive data.
All complex tasks around connection, data profiling, curation, governance and reporting is all done in few clicks.
Operationalize your regulatory compliance and privacy programs through AI/ML enabled Data Compliance platform to report, remediate gaps and be 100% compliance.
Unlimited connectors for extracting data from any operational systems, data sources for provisioning integrated dataset for optimized and repeatable analytics.
Leverage data quality powered catalog to automate the process of discovering, inventorying, profiling and tagging by DQ score.
CURIOUS ABOUT DATA GOVERNANCE