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.
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.
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.
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.
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.
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.
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.
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.
DQLabs automatically standardizes your data using various different algorithm sets utilizing distance / similarity / phonetic based clustering along with pattern detection, functions, and reference libraries.
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 dataInitiate multiple levels of data cleansingApply built-in continuous learningEmploy visual learning with detailed curation reportsBenefit from human guided curation for reinforcement learningTransform by configurationStandardize in multiple ways
Data Integration 
Data Discovery 
Data Quality 
Data Observability 
Data Remediation 
Business Insights 
