Master Data Management

Traditional MDM are slower to implement, not easier to adapt, takes longer cycles of effort and resources before leverage just to realize the data has shifted due to new business strategies. DQLabs provides a faster, ready to play Master data management solution using is Master module by the ability of easily ingesting from multiple sources of data, abstracting the art of creating golden view records using a combination of steps that includes consideration of data quality profile, cataloguing, curation and still maintaining the governance.

Leverage immediate ROI by a plug and play multi-domain master models out of the box for your environment or create your own Model without requiring advanced skills.

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MDM Features

DQLabs AI/ML based MDM provides an extensible framework to create Master Data Management Models for customers, products, devices, and other business entities to automatically generate a 360 view or golden view of records.

MDM Features

Out of the box models

Out of the box plug and play models for various verticals and packaged capability for various data subject areas including but not limited to main party models such as customers, citizen, patient, supplier, vendor, employee, associate data models, Transaction data models, Product data models, Financial or Quantitative data models, Asset data models, Location or Facility data models etc.,

MDM Features

Create your own model by configuring

Ability to create your own model but still leverage our proven functionality and algorithms to address specific requirements of main party 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 knowledge base to modify data to specified formats, values and layouts.

MDM Features

Leverage Operationally using API, Search or Extracts

Ability to ingest MDM model views back into transactional systems via Pre-built APIs, RESTful web services or Portal search or offline extracts that enables businesses to make quick wins and improve source data quality by blending and merging master data directly into transactional applications

MDM Features

Build Confidence and Trusted view of Customers Report

The MDM or the golden record is built using multiple levels of processing and algorithms that includes scoring on data sources along with its quality profile on each attributes, with weightage distribution, proven algorithms that spans across match, merge, consolidation and linking enabling organizations to build a 100% trusted MDM model.

MDM Features

Trust-based Governance included

DQLabs has inbuilt Trust-based approach that is no longer a one-size-fits-all, top-down approach, but that adapts to the situation and the level of central governance required. Not only the platform has traceability, auditability but also provide the controls for data and analytics governance, plus risk assessment, control and compliance as related to data quality, security, privacy and retention.

MDM Features

Machine Learning powered MDM

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

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Other Modules

All complex tasks around connection, data profiling, curation, master data management and reporting is all done in few clicks.

Data Catalog

DQLabs modernizes the meta data, catalog and taxonomy management with its out-of-the-box connectors and using its patent pending AI driven DataSense™ technology.

Data Quality

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.

Data Curation

ML based smart curation modules to automatically curate the data based on the metadata definition and using Reinforcement Learning at all three levels of curation – Basic, Reference and Advanced.

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