Improve Data Quality with Smart Curation

Measurement without remediation does not help to improve data quality. Using DQLabs you can not only identify the bad records as measured by the DQScore™ but our platform also allows you to remediate and fix any kind of data quality issues within your organization. DQLabs remediates your data quality issues by cleaning, enriching, and merging good records using ML-based smart curation libraries, modules and it identifies the optimal data preprocessing strategies to make this process faster while it also automates data curation with controls on data quality thresholds.

Smart curation along with visual learning allows you not only to convert bad data into good data but also utilizes machine learning when you interact or intervene with the platform to adapt to your organization’s specific needs. As you interact with the platform over time it learns from you how best to deal with any type of dataset or domains which in turn helps you to reduce operational costs and drives trustworthy outcomes.

Check out our Data Quality Smart Curation Features

Leverage DQLabs and Remediate Data Quality issues by cleaning, enriching and merging good records using ML based Smart Curation and Self Learning.

Automatic Smart Curation

Reduce your manual efforts spent cleaning data. Automate the curation process with DQLabs’ smart curation module which intelligently cleans the data using efficient machine learning algorithms.

Multiple Levels of Remediation

DQLabs allows you to clean and enrich data precisely the way you want to by providing three levels of machine learning algorithms: Basic, Reference, and Advanced.

Continuous Semantic Learning

DQLabs comes with the inherent ability to identify the patterns and trends in your data and continuously learn semantic relationships to constantly improve the remediation process.

Human Guided Reinforcement Learning

The DQLabs platform continuously learns from every interaction. It adapts and evolves based on the feedback you provide.

Enrichment Using API

You can easily connect any authoritative external or internal data enrichment tools with DQLabs using APIs to augment your data enrichment efforts by cross comparing and updating data like addresses, phone numbers, email adresses, missing values and more.

Merge, Consolidate and Create Semantic Models

Merging and consolidating data from huge data sets is a tedious process for data professionals. DQLabs makes it easier for you to join various relevant datasets, consolidate the data, and create semantic models to gain a single holistic view.