Data Classification

Classifying your data by automatically tagging data based on data privacy requirements, data sensitivity and data security have now become a necessary and in some cases a mandatory requirement to assure data governance and customer goodwill.

Overview

All data quality (DQ) programs must classify their vast stores of existing and incoming data on a continuous basis or risk missing important data governance and management requirements. After data observability, data classification is the next obvious step in assuring data quality success.

With DQLabs your team is empowered with the latest advents in auto tagging and classification along with a versatile and transparent universal business glossary to link your classified data to your data catalog.

Data Classification Features

Leverage DQLabs Data Classification to improve understanding of the data quality with its multiple levels of classification at the attribute, dataset, and data source level based on data governance and compliance policies.

Auto Tagging and Classification

Let the DQLabs platform automatically tag and classify your data based on the most relevant and necessary data privacy, sensitivity and security needs. Assure compliance and provide support for governance and mandatory regulations such as GDPR, CCPA, Sox 2 and HIPAA to name a few.

Versatile Data Sharing

DQLabs performs multiple levels of classification at the attribute, dataset, and data source level to provide ease of sharing and permissioning. Despite multiple data formats and far-flung data silos DQLabs will perform data classification to support compliance and governance.

Improve Understanding of Your Data

With DQLabs you can easily and quickly discover, inventory, profile, tag, and create semantic relationships between data assets as a key component to understanding data quality. Our semantic search capability integrated with advance machine learning (ML) allows your team to quickly and easily discover and classify your data.

Data Analyst Ratings

DQLabs intuitive and universal dashboards offer the ability for technical and business users to share queries and datasets including publishing, sharing and promoting data regarding governance features such as dataset user ratings. Stay abreast of any and all changing governmental, legal and organizational requirements of your data.

Assign and Discover Data Levels

DQLabs allows you to assign multiple levels to your data based on your data governance policy, procedures and controls. Automatically discover personal data dispersed across the enterprise, including associated lineage, policies, and usage.

Data Classification

Use DQLabs classification capabilities to comply with compliance classification and assist with compliance audits. Use data classification to

Allow users to share queries and datasets in the review of governance requirements

Improve your teams understanding of data based on data classification rules and principals

Implement continuous data classification with the benefit of ML assisted data Search and Querying

Execute automatic semantic identification via full text search to provide data classification based on the business context

Implement an automated attribute search using data quality scores

Data Integration

Combine your data from all sources using AI powered built-in connectors

Data Discovery

Automatically discover and classify data based on data sensitivity and security

Data Quality

Use our Machine Learning enabled platform for all data quality tasks

Data Observability

Semantic discovery allows users to refine business context without any manual efforts

Data Remediation

Identify your optimal data pre-processing strategies and automate your data curation process

Business Insights

Generate a 360-degree view of customer records, products and devices using Master Data Management (MDM) models dipiscing

Best Practices

BLOGS

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 DQLabs.ai’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 DQLabs.ai’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.

DQLabs.ai 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.

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