Smart Data Curation

With DQLabs you can Identify your optimal data pre-processing strategies and automate your data curation process of assembling, managing and presenting your data to assure control of your data quality thresholds.


With the explosion of data from, an endless variety of data integrations and sources, more and more companies are struggling with data curation aka data wrangling which is the process of transforming and mapping data from one form to another.

Let DQLabs reduce your operational costs and create trustworthy outcomes using smart curated datasets assisted by our innovations in AI/ML based algorithms and models.

Data Curation Features

DQLabs AI/ML based smart curation modules identify the optimal data preprocessing strategies by automating data curation while providing controls on your data quality thresholds. The data curation process is further enhanced with reinforcement learning which predicts the type of repair needed to resolve an inconsistency and applies that repair to improve quality.

Generate more accurate data

Using an optimal mix of unsupervised and supervised machine learning (ML) including advanced algorithms, all unknown patterns in your data are identified so you can cleanse and provide more highly accurate data.

Multiple levels of data cleansing

Benefit from DQLabs ability to deduplicate, clean and enrich your data using three select levels of curation – basic, reference and advanced algorithms. All of these are automatically configured based on DQLab’s patented DataSense™ module.

Built-In Continuous Learning

As market environments shift, so do business strategies including the underlying data in your business operations. As the data evolves and changes into new forms and different lifecycles, DQLabs learning platform continuously evolves to automatically remove and create new rules to improve the process of data cleansing.

Visual Learning with Detailed Curation Reports

DQLabs visual learning environment provides the capability for business and technical users to uncover the root cause of quality issues via detailed and automated reporting of results. Advanced leading-edge algorithms automatically discover within minutes patterns, insights, fraud, missing values and correlations across all data silos.

Human Guided Curation for Reinforcement Learning

As business analysts, data stewards and data analyst interact with DQLabs, the platforms integrated automated intelligence (AI) learns the user behavior from those interactions to guide and reinforce automated smart actions as well as determine actions that need further refinement. This process of scaling the human element with ML algorithms helps you cleanse vast amounts of data more effectively and smarter.

Transform by Configuration

Rather than authoring or creating heavy extraction, transform and load (ETL) workflows for cleansing the data, DQLabs provides an easy and intuitive way of configuring transform tasks to improve the consistency, validity, and reliability of the data.

Standardize in multiple ways

DQLabs automatically standardizes your data using various different algorithm sets utilizing distance / similarity / phonetic based clustering along with pattern detection, functions, and reference libraries.

Data Curation

DQLabs curation module utilizing leading edge ML identifies the optimal data preprocessing strategies and automates data curation with controls on data quality thresholds. This is further enhanced with the help of reinforcement learning which predicts the types of updates needed to resolve inconsistencies. Use DQLabs data curation module to

  • Generate more accurate data
  • Initiate multiple levels of data cleansing
  • Apply built-in continuous learning
  • Employ visual learning with detailed curation reports
  • Benefit from human guided curation for reinforcement learning
  • Transform by configuration
  • Standardize in multiple ways

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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