Data Governance

Enable improved business outcomes by using modern data and analytics control to better understand data.

Overview

In today’s world, the pace of business and the diversity of data exceeds the capabilities to meet existing governance requirements. Navigating through many different data types, users, sources, and uses of data assets across an organization has made data governance more challenging and complex, and now requires diverse implementation approaches. Data governance is further complicated by the lack of a standardized approach or framework to clarify governance objectives.

To align and succeed with data and analytics governance programs, use DQLabs to automate data and analytics governance implementation by deploying augmented data management.

Data Governance Features

Leverage DQLabs to deploy our out-of-the box data governance model with established governance processes, stewardship roles, and informational health metrics to improve your data quality.

Better understanding of data

Leverage the DQLabs data catalog optimized with Machine Learning (ML) to simplify and automate the process of discovering, inventorying, profiling, tagging, and creating semantic relationships between data assets to better understand data governance.

Better understanding of data - DQLabs
Trace data back to its source using Data Lineage - DQLabs

Trace data back to its source using Data Lineage

Achieve data-driven business outcomes and thereby build trust in the data by automating the process of data lineage. Automatically observe the data lifecycle by tracing key data from its data source to its final data consumption.

Automated Data Traceability

Use DQLabs inherent ability to monitor and report on data assets life history over a period of time. Other reporting aspects of data management include data quality, trend, sensitivity, curation impact and more.

Minimize risk with right access - DQLabs

Minimize risk with right access

The use of DQLabs Centralized Data Security and Permissioning of data assets across data sources, datasets, and even the attribute level makes data governance simple and easy.

Proactive identification of sensitive data

Automatic identification of personally identifiable information (PII) using semantic type identification across various information assets avoids exposing sensitive data.

Data Governance

Employ DQLabs data and analytics governance to navigate through different data types, sources, users, and data assets to ensure the data is accurate, consistent, secure, and aligns with your company’s overall data governance objectives.

  • Align and succeed with meeting your data and analytics governance programs
  • Improve data quality using a centralized and scalable data governance framework
  • Take advantage of DQLabs clear and concise permissioning and downward application propagation
  • Employ the easy to use Out-of-the box data catalog and data lineage features to understand the data from its source to consumption
  • Benefit for a standardized approach across all functions, sources, and users to reach 100% data compliance for all regulations and initiatives

Best Practices

See what DQLabs can do

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