Data Governance

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


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.

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

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

Semantic Discovery for Data Quality Management


Semantic Discovery for Data Quality Management

Fundamentals of Semantic Discovery

A data-driven business gains no value from its data which lacks a clear meaning and context. Such businesses will, therefore, constantly try to make sense of their massive data. How? Some businesses have teams of business analysts, data specialists, and other personnel employed to manually review, analyze, and classify all available data. In large businesses, it becomes difficult to do this manually, hence the need for automation. They are using ML algorithms to automate the process of analyzing and classifying massive datasets through self-learning capabilities. This automation has been proven to save up to 90% of project time.

What is Semantic Discovery?

Semantic Discovery is the approach to profiling data based on its semantic categories. Semantic Discovery supports the possibilities of exploring semantic categories of data in question and querying complex semantic relationships in datasets to create tabular analyses which have indicators and patterns which may be pre-defined.

Why Semantic Discovery?

Semantic Discovery is a process that helps businesses to automatically derive business meaning from data to enable understanding and automating business processes. With no clear meaning and context of data, the data may of be minimal value to a business, especially the data-driven businesses.

With Semantic Discovery, you can;

  • Explore semantic categories and query complex semantic relationships in the data to be analyzed.
  • Scan through data, analyze the characteristics and values of the data,
  • Create table analyses preconfigured with indicators and patterns that best suit your data.
  • Compare your data against other fields with an aim to propose semantic meaning and relationships with other available datasets.
  • Semantic Discovery for a data-driven company enables further automation.
  • Automatically generate data quality rules for a given dataset.
  • Provide the basis for protecting personal data to enable self-service data ingestion.

How does DQLabs Semantic discovery work?

Data quality measurement without meaning, semantics, or understanding of the business context of the data does not help get better business practices. With DQLabs’ Data Sense™ capabilities, a business can automatically enrich semantics for any type of data, whether it has metadata information or not. As a result, a business can automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management and auto-discover rules and sensitivity classification in alignment with the business landscape.

With semantic identification and extensive integration into data catalog or data governance systems, you can derive end-to-end views of your data assets for the purposes of governance, privacy, compliance, and data quality. This will allow the data stewards to search and discover metadata as well as understanding the data quality associated with each attribute.

What are’s Semantics Discovery Features?

DQLabs’ Semantics Discovery will be of great help for any type of data source. It has built-in support and integration for simplified metadata management and automates the process of discovering, inventorying, profiling, and tagging data. Some of the features include;

Auto Discover Semantics

To help you discover and extract semantics from various enterprise data warehouses, operational databases, enterprise applications, cloud data stores, and nonrelational data stores with the help of just a few clicks using out-of-the-box connectors.

Automatic Sensitivity Classification

This will help you configure at ease your own sensitivity levels per your data governance programs and automatically identify the sensitivity footprint and classification at each attribute level.

Identify True Data Type

This is a feature that will help to ignore formatting, locale, and culture and identify the true data type at the attribute level to find relevant data quality rules.

Auto Discover Relevant Rules

With enriched semantics and business context for each attribute, let the platform discover all relevant data quality measurement rules for you.

Auto Detect Necessary Remediations

Measurement without remediation does not help a business to improve data quality. With enriched metadata and semantics, now you can enjoy remediation libraries that can perform smart curation at every attribute or dataset level.

Search and Discover with Relevance

Perform semantic searches across datasets and find the most relevant datasets by various metrics such as data quality score, drift level, sensitivity classification, etc., all within one platform.

Auto-tagging and Classification

Includes classification per the most up-to-date data privacy and security compliance regulations — such as the EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

How will your company benefit from’s Semantics Discovery?

Leveraging DQLabs’ Semantics Discovery will help you to scan through your data, analyze the characteristics as well as values, compare the data against other fields, and eventually propose semantic meaning and relationships with other datasets. Semantic Discovery capabilities can be purposed for any company’s needs and data and will work in different languages and parameters resulting in metadata that will allow further automation.

Interested in a platform demo? Signup now.

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