With the explosion of data, and a variety of data integrations and sources, more and more companies are struggling with data cleansing, data curation or data wrangling– primarily the process of transforming and mapping data from one form to another.
DQLabs reduce operational costs and drive trustworthy outcomes using smart curated datasets using innovations in the area of AI/ML based algorithms and models.
DQLabs AI/ML based smart curation modules 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 and predicts the type of repair needed to resolve an inconsistency and applies to improve quality.
Using a careful blend of both unsupervised and supervised and ML algorithms, all unknown patterns in data are identified to cleanse and provide more highly accurate data
Ability to deduplicate, clean and enrich data using three levels of curation – basic, reference and advanced algorithms. All of these are automatically configured based on the DataSense™ module of DQLabs
As market environments shift, so does business strategies and therefore the underlying data in the business operations. As the data evolves and takes new forms and different lifecycles, DQLabs learning platform continuously evolves and builds and removes rules automatically to improve the process of data cleansing.
Visual Learning environment that allows business and technical users to interact with data and understand the root cause of quality issues. The platform comes with algorithms that are fine tuned to automatically discover patterns, insights, fraud, missing values and correlations across attributes, datasets and data sources within fraction of minutes to improve data quality.
As business analyst or data stewards or data analyst interacts with DQLabs, the platform learns the user behavior from those interactions to guide and reinforce smart actions vs actions that further needs refinement. This combination of scaling the human element with ML algorithms helps us to cleanse vast amounts of data more effectively and smarter.
Rather than authoring or creating heavy ETL / ELT 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 standardizes automatically your data using multiple different algorithms sets but not only limited to Distance / Similarity / Phonetic based clustering, pattern detection, functions, or reference libraries.
All complex tasks around connection, data profiling, curation, governance and reporting is all done in few clicks.
Easy entity definition and creation of high-quality golden records for customers, products, vendors, assets, location, devices, and other business entities.
Ability to discover patterns, insights, fraud, missing values and correlations across attributes using all connected datasets and data sources within fraction of minutes using AI from users’ behaviors and automatic business rules configurations.
Easy entity definition and creation of high-quality golden records for customers, products, vendors, assets, location, devices, and other business entities.
CURIOUS ABOUT DATA CURATION