Agile MDM

Generate a 360-degree view of customer records, products and devices using our AI/ML based semantic Master Data Management (MDM) models which provide an extensible framework.

Overview

Traditional MDM platforms are slower to implement, difficult to adapt, and they take large amounts of effort and resources only to realize the data has shifted due to new business strategies. DQLabs provides faster ready to play semantic models to easily ingest data from multiple sources, abstracting the art of creating golden view records by using a combination of steps which includes consideration of the data quality profile, cataloguing, and curation while still maintaining data governance.

Leverage immediate ROI by using the ready made multi-domain master models or employ your own models which do not require any advanced skills to create.

Agile MDM Features

DQLabs AI/ML based agile MDM models provides business and technical users with a ready-made entity definition including creation of high-quality golden records for customers, products, vendors, assets, locations, devices, and other business entities

Out of the box models

DQLabs out-of-the-box plug and play models are provided for various verticals and configured for various data subject areas including main party models such as customers, citizen, patient, supplier, vendor, employee and associate data models. Other data models include transaction data models, product data models, financial or quantitative data models, asset data models and location or facility data models to name a few.

Out of the box models - DQLabs
Create your own model with available components - DQLabs

Create your own model with available components

Use DQLabs capabilities to create your own models while leveraging our proven functionality and algorithms to address specific requirements pertaining to important data quality issues (e.g., standardization of names, addresses, contact details and hierarchies, and merging of duplicate party records). This not only includes the built-in capabilities for decomposition of data into its component parts but also for applying industry/local standards, business rules or a knowledge base to modify data to specified formats, values and layouts.

Leverage Data Management using API, Search or Extracts

Optimize your team’s ability to ingest semantic model views back into transactional systems via DQLabs pre-built APIs, RESTful web services or Portal search. Capabilities include offline extracts that enable businesses to make quick wins and improve source data quality by blending and merging master data directly into transactional applications.

Build confidence and trust in your reports - DQLabs

Build confidence and trust in your reports

The semantic models or the golden record are built using multiple levels of processing and algorithms which includes scoring of data sources. Take advantage of its quality profile on each attribute with weightage distribution and proven algorithms which span across match, merge, consolidation and linkage enabling organizations to build a 100% trusted semantic model.

Trust-based Governance included

DQLabs has incorporated a built-in trust-based approach that is tailored to your needs rather than a a one-size-fits-all, top-down approach. Our platform adapts to the situation and the level of central governance required. Not only does the platform provide traceability and auditability but it also provides the controls for data and analytics governance plus risk assessment including control and compliance as related to data quality, security, privacy and retention.

Machine Learning powered Semantic Models - DQLabs

Machine Learning powered Semantic Models

DQLabs uses AI/ML algorithms to automate the configuration and optimization of master data processing to reduce provisioning and implementation cycle times and reducing the need for highly skilled resources to provide immediate business value.

Agile MDM

Employ DQLabs simple to use entity definitions to create semantic models for customers, products, devices, and other business entities. Feed master records into semantic models and then update and extract the models for processing and export for further ingestion and to reference data in other applications. The semantic model layer is supported with pre-built APIs and RESTful web services for viewing and controlling master data directly in other applications

  • Save time with out-of-the box models
  • Leverage operations efficiency using API, Search, or Extracts
  • Build Confidence and crate a trusted view for your customer reports
  • Gain economies of scale with the included Governance module
  • Take advantage of the Machine Learning powered semantic models

Best Practices

Trellance Annual Conference 2022 - DQLabs Events

EVENTS

Trellance Annual Conference 2022

DQLabs is proudly sponsoring this year’s Trellance Annual Conference, which will be held at the luxurious JW Marriott along the sparkling waters of Tampa Bay on May 17th–20th.

Our Senior Vice President of Revenue, Lance Keel, will be speaking at the conference on the topic “Leading Data Quality Practices and Why it’s Critical“. Come join the session and meet our team at the conference to learn more about what’s new at DQLabs!

Attending this conference can help you learn how DQLabs augmented data quality platform can manage your entire data quality life cycle with Artificial Intelligence and Machine Learning to combine processes and technologies to improve, monitor, and prepare “ready-to-use” data.

For more information, book a meeting with our experts.

View More Arrow image