Just about anyone can generate data from any device, any location resulting in an ever increased amount of data quality issues. With so many data entry points, companies are further challenged with meta data management, curation and governance issues. Without having a platform to manage these processes, organizations are left with bad predictions, poor outcome even after investing millions of dollars in sophisticated analytics, business intelligence and visualization technologies.

DQLabs was created with the vision to provide a simple way for organizations to handle issues around data quality, governance, curation, master data management effectively. With the use of AI and Machine Learning – the sophistication of the technology is blended carefully with the art of simplicity. All complex tasks around connection, data profiling, curation, master data management and reporting is all done in few clicks.

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An Unified Suite of Modules for all Data Management Needs.


AI/ML Driven MetaData Management

Connect to any offline, online and real time sources with few clicks using out-of-the-box connectors. With AI driven DataSense™ module, DQLabs modernizes the meta data, catalog and taxonomy management with an integrated AI/ML and automated rules and learning capabilities. Not only it centralizes all metadata information, its relevant business terms, rules, data structures, data schemas, relationships, and metadata from sources but also enables search and query capabilities of metadata to facilitate discovery of data assets.

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Improve Data Quality

An Autonomous Data Quality profiling framework 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 has the ability to discover patterns, insights , fraud, missing values and correlations across attributes using all connected datasets and datasources within fraction of minutes.

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Intuitive Data Curation

Machine Learning based smart curation module that 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 also predict the type of repair needed to resolve an inconsistency.

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Define MDM Model

Create Master Data Management for customers, products, devices, and other business entities and feed master records into MDM, update, and extract MDM for processing and export for further ingestion and reference data in other applications. Further the MDM layer is supported with Pre-built APIs, RESTful web services for viewing and controlling master data directly in other applications.

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Use Cases

A Unified Suite For All Vertical Needs.


Smart Cities Initiatives

As a part of the smart city initiative — City Administrators were looking for an end-to-end, comprehensive, enterprise scale, Master Data Management solution which can provide information that promotes government transparency, accountability and provide citizens with information that encourages and invites public participation and feedbackThe solution should ingest large volume of data from various sources in real time and build a MDM Citizens model so as to ingest reference data back into originating systems plus enhance meaningful transparency reports.  

Discover how the major city overcame data quality, curation and data management  challenges and successfully implemented an MDM solution using AI powered DQLabs platform.  

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Asset Management Challenges

The day to day challenge of a leading U.S based Asset Management Provider is that they must run specific data quality routines and data preparation on the top of their enterprise systems and ingest lots of custodial data from different businesses like bank, brokerage firms, trust companies, insurance providers which involves different data sources like the Investment Accounting System, Trading Systems, Compliance and Billing systems etc. 

Discover how this major asset management provider implemented AI powered DQLabs data quality platform to scale their data quality and business rule gaps across the enterprise without any human intervention. 

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Price & Operational Analytics

A major automotive software provider is in the business of consolidating data from different dealerships using Dealer system providers. These data coming from various sources had lots of data challenges, duplication, typos and data issues that involved huge amount of time investment on the data preparation as preparing data is foundational to any analytics initiative. The proposed solution was demanded to adapt to evolving data types, various use cases around automotive and a modern streaming, real time modern data preparation and data quality, curation, integration platform and practices along with smart profiling using AI/ML and NLP technologies.

Discover how this automotive vendor overcame the challenges of automating data stewardship and DataOps tasks using AI powered DQLabs platform.  

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360-Degree Customer Intelligence

One of the largest retailers in US faced challenges in managing data quality, data integration and  data management while creating a single view of the customer using a data management platform that  can connect with various data sources and types. The problems with various data source ingestion, quality and cleansing and required transformation dictated a seamless, easy to manage platform that can   strengthen marketing, analytics, reporting capabilities across the enterprise and provide better customer engagement.

Discover how the major retailer implemented a highly available and scalable Customer Data Platform using AI powered DQLabs platform.

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