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.

Generate more accurate data
Multiple levels of data cleansing

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

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

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

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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.


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

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