Modern Data Catalog

One of the key aspects of DQLabs optimal data quality platform is the automated e-process of discovering, inventorying, profiling and tagging using our ML-augmented data catalog for simplified metadata management.

Overview

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.

Data Catalog Features

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.

Connect to all data sources and data types

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

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.

Understand Data Quality Impact

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.

Auto tagging and Classification

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.

Patent Pending DataSense™

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.

Data Catalog

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

Connect to the most popular data sources and types

Benefit from ML assisted data Search and Querying

Understand Data Quality Impact

Execute Auto tagging and Classification

Implement Smart Cataloguing

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 Remediation

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

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

Business Insights

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

Best Practices

EVENTS

DQLabs in Action: Data Collaboration in the Modern Data Stack

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.

Agenda

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