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
The data producers, consumers, and leaders deserve an ecosystem that delivers the data that is relevant to them – one size fits all approaches and solutions no longer cut it in this modern data landscape. Data minds and Business minds see data in different ways even when working on the same data for the same business outcomes. You need a platform that promotes Decentralized Data ownership culture to improve data relevance and data collaboration.
With the growth in data, there is an explosion of modern architectural thinking (Data as Product, Multi-Cloud) that has led us to Cloud Datawarehouse, Lakehouse to Data Fabric, and Data Mesh adoptions. With this growth and expansion of technologies, both the Data Producers and Consumers of data are shifting away from the traditional ideologies around Centralized Data Ownership towards new principles around “Decentralized Data Ownership”. Further, requires a tight collaboration across different persona to meet business needs and the data quality needs.
This requires not just looking at metadata but going beyond metadata and looking under the data to derive insights from top to bottom and tying directly to business outcomes. We at DQLabs believe that a comprehensive modern data quality check should go across these three levels – Data Reliability, Business Fit Measures, and KPI metrics.
In our second installment of the Defining Data Relevance webinar series, Raj Joseph, Founder and CEO of DQLabs, and Sanjeev Mohan, industry expert and Principal at SanjMo, unpack the complexities of the Modern Data Stack and Modern Data Quality Platforms and the ever-growing need for Data Relevance.