Augmented Data Quality

Use DQLabs to scan various types of data sources and data sets in real time. Generate your data quality score with the ability to track, manage and improve data quality over time.

Overview

As organizations use more data in distributed environments and face more complex business requirements, data and analytics leaders are challenged to employ effective data quality programs and tools to maintain high data quality. DQLabs augmented Machine Learning enabled platform performs all of the required data quality tasks with the use of efficient and effective automation that is  transparent to business and technical users.

Data Quality Features

Leverage DQLabs AI/ML augmented data quality platform to scan all types of data sources and data sets in real time. Manage your data quality with scoring that tracks, manages and improves data quality over time.

Assure consensus in understanding data quality

Data quality can mean different things to various departments, their business user and their technical users. The DQLabs platform has made it possible for organizations to reach a consensus by providing the ability to compare scores form one source to another irrespective of the type or size of the organization.

Assure consensus in understanding data quality - DQLabs
Improve data quality on any types and-or forms of data anywhere - DQLabs

Improve data quality on any types and/or forms of data anywhere

Track, Manage and Improve your data quality over time using DQLabs Autonomous Data Quality profiling that can scan any type of data in real time and provide you with an immediate quality impact analysis.

Highly interactive visual learning environment

The DQLabs visual learning environment allows business and technical users to interact with data and understand the root cause of any quality issues. The platform comes with prebuilt algorithms that are fine tuned to automatically discover patterns, insights, fraud, missing values and correlations across attributes, datasets and data sources within a fraction of minutes to improve data quality.

User driven interactive profile execution - DQLabs

User driven interactive profile execution

Discover, analyze and understand in minutes critical patterns and Insights by performing on demand or scheduled profiling as part of the integral UI. Recognize immediate ROI without requiring any advanced skills.

Out of the box catalog with quality scorecard

All integral metadata information and cataloguing of sources includes a data quality score (DQScore™), a simple intuitive way for data analyst, modelers, and business analyst to understand the impact of data usage via reports and analytics.

Trend Analysis - DQLabs

Trend Analysis

As businesses shift in strategy and data changes, DQLabs automatically discovers and monitors these changes employing autoconfigurations to improve data quality – all autonomously without any human intervention.

Data Quality

DQLabs offers an autonomous data quality platform that scans various types of data sources and data sets in real time and provides scoring with the ability to track, manage and improve data quality over time. It also discovers patterns, insights, fraud, missing values and correlations across attributes using all connected datasets and data sources within a fraction of minutes.

  • Obtain a consensus regarding data quality understanding requirements
  • Improve data quality on any types and/or forms of data
  • Reduce start up and training time with the highly interactive visual learning environment
  • Employ user driven / interactive profile execution
  • Reduce time with DQLabs Out-of-the box catalog with quality scorecard
  • Understand your data lifecycle with trend analysis

Best Practices

The rise of the Modern Data Stack and the Modern Data Quality Platform - DQLabs Webinar

EVENTS

The rise of the Modern Data Stack and the Modern Data Quality Platform

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. 

Abstract:

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.

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