DQLabs Features

Allows administrators to assign role-based user access for specific data sets

Hides and masks sensitive data automatically based on user permissions

Provides historical insights into original data sources and transformations made to data sets

Offers simple search capabilities to discover specific data sets

Allows users and administrators to easily clean data to maintain quality and integrity

Converts data formats of source data into the format required for the reporting system without mistakes

Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Allows users to comment on data sets to help future users better interact and interpret the data

Permits profiling of data sets for increased organization, both by users and machine learning

Creates a business glossary for faster understanding by the average business user

Indexes metadata descriptions for easier searching and enhanced insights

Automates recommendations for users based on machine learning functionality

Offers natural language querying functionality for non-technical users

Cleans data to improve quality via automation

Correctly identify inaccurate, incomplete, or duplicated data from a data source.

Utilize deletion, modification, appending, merging, or other methods to correct bad data.

Standardize data formatting for uniformity and easier data usage.

Clean data as it enters the data source to prevent mixing bad data with cleaned data.

Finds duplicates using the fuzzy logic technology or an advance search feature.

Generate real-time depth analytics utilizing event metrics, logging and metadata.

Use custom or pre-built tests for buisness rules. to ensure data quality.

Involves automation capabilities to identify and track issues, failed operations by looking at historical trends.

Complete visibility of the data pipeline, and immediately notifies the data team if any issues. Ensures cross stack visbility.

Provide follow-up information after data cleanings through a visual dashboard or reports.

Automatically run data identification, correction, and normalization on data sources.

Schedule automated audits to identify data anomalies over time based on set business rules.

Gives a view of the entire data quality management ecosystem.

Allows user role-based access and actions to authorization for specific tasks.

Captures and manages data hierarchy and definitions.

Helps integrate external and internal reference data such as industry codes, entity identifiers, country codes etc used to categorize information across systems.

Helps understand the origination and route of data along with the transformations happening across various domains.

Provides context and information for data assets stored across domains and ensures adherence to established policies and processes.

Identify the different type of anomalies and receive alerts.

The data observability environment can be viewed from a single dashboard.

Provides immediate alerts for any anomalies or expected events.

Establishes lineage for the data pipeline - from data warehouse to the data user.

Support integrations with various business applications which support different data processes. Also, integrate with apps to provide alerts.

Centralizes the management of multiple domains of master data which includes product, customer, vendor, asset, location and pricing.

Matches master data records and merges them into a single accurate record across source systems.

Shows relationships between records and entries, defined in a repository model.

Provides a simple customizable design environment to develop custom UIs using simple drag and drop actions.

Encompasses of policies, principles and qualities to promote access to accurate and certified master data.

Helps mask or hide critical information to prevent unauthorized access.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting