Augmented Data Quality

DQLabs.ai is an augmented data quality platform to manage your entire data quality life cycle. With ML and self-learning capabilities, organizations can measure, monitor, remediate and improve data quality across any type of data – all in one agile, innovative self-service platform.

With a Data Quality First approach and higher degree of automation, both business and technical stakeholders can improve ROI and enrich customer experiences by discovering trustable data and business insights in minutes.

We have an out of the box platform that provides immediate data quality metrics for Data Quality Stewardship, Active Metadata Management, Catalog, Governance and DataOps monitoring and alerts.

Modules

DQLabs platform module is categorized to span across the entire life cycle of Data Quality. Furthermore, the platform has both API and a rich UI portal with self-learning capabilities that can integrate and work well with any existing products in your landscape.With a prime focus towards Data Quality, DQLabs modules are categorized into

Smart Connectors

Connect to unlimited data sources in any form, shape, location

Semantic Discovery

Auto discover rules by semantic identification and classification

Measure Data Quality

Evaluation of all attributes across subjective and objective data quality dimensions

Monitor Drift and Behavior Analysis

Continuous DQ monitoring using different types of anomaly detections across length, pattern, null and blank

Remediate and Improve Data Quality

Remediate Data Quality Issues by cleaning, enriching and merging good records using ML based Smart Curation and Self Learning

Business Dashboards and Insights

Outcome focused measurement using KPI, metrics and performance measures

With these modules, you can now configure the platform however you want depending upon your data maturity cycle irrespective of whether you are just starting out or already working with an ecosystem full of data governance, catalog, and modern data management products.

What our customers say

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What our customers say

" DQLabs handed us the real automation that we were looking for, along with connecting, discovering, profiling, cleansing and more. Now we trust the quality of the data we use to project our business outcomes."

" The impact that we are making with DQLabs is profound. Inventory of data assets is maintained through the semantic discovery and organization of distributed datasets."

" Finally, with the help of DQLabs we were able to enrich geospatial data to aid anyone interested in affordable housing with more accurate location information about the funded projects."

" DQLabs was chosen by the value proven through Proof-Of-Concept engagement and it certainly made an impact over the rivals in the area of DQ. Now the entire Data Quality life cycle is automated!"

" DQLabs has played a tremendous role in collecting and sensing valuable information with out-of-the box self-learning capabilities. A data quality-focused approach of reporting and analytics helps boost in forecasting efficiency and operations."

" In order to gain customer insights, first we needed a platform with superior data integration capabilities to create single customer views using customer data from a wider variety of internal systems."

" Transparency and accountability are high priorities for the City of Spokane. DQLabs and Azure Databricks enable us to deliver a consistent source of cleansed data to address concerns for high-risk populations and to improve public safety and community planning."

" We evaluated multiple products for automated data quality and governance and finally integrated DQLabs with our platform which empowered our customers with self-service capabilities."

Best Practices

BLOGS

Semantic Discovery for Data Quality Management

Fundamentals of Semantic Discovery

A data-driven business gains no value from its data which lacks a clear meaning and context. Such businesses will, therefore, constantly try to make sense of their massive data. How? Some businesses have teams of business analysts, data specialists, and other personnel employed to manually review, analyze, and classify all available data. In large businesses, it becomes difficult to do this manually, hence the need for automation. They are using ML algorithms to automate the process of analyzing and classifying massive datasets through self-learning capabilities. This automation has been proven to save up to 90% of project time.

What is Semantic Discovery?

Semantic Discovery is the approach to profiling data based on its semantic categories. Semantic Discovery supports the possibilities of exploring semantic categories of data in question and querying complex semantic relationships in datasets to create tabular analyses which have indicators and patterns which may be pre-defined.

Why Semantic Discovery?

Semantic Discovery is a process that helps businesses to automatically derive business meaning from data to enable understanding and automating business processes. With no clear meaning and context of data, the data may of be minimal value to a business, especially the data-driven businesses.

With Semantic Discovery, you can;

Explore semantic categories and query complex semantic relationships in the data to be analyzed.
Scan through data, analyze the characteristics and values of the data,
Create table analyses preconfigured with indicators and patterns that best suit your data.
Compare your data against other fields with an aim to propose semantic meaning and relationships with other available datasets.
Semantic Discovery for a data-driven company enables further automation.
Automatically generate data quality rules for a given dataset.
Provide the basis for protecting personal data to enable self-service data ingestion.

How does DQLabs Semantic discovery work?

Data quality measurement without meaning, semantics, or understanding of the business context of the data does not help get better business practices. With DQLabs’ Data Sense™ capabilities, a business can automatically enrich semantics for any type of data, whether it has metadata information or not. As a result, a business can automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management and auto-discover rules and sensitivity classification in alignment with the business landscape.

With semantic identification and extensive integration into data catalog or data governance systems, you can derive end-to-end views of your data assets for the purposes of governance, privacy, compliance, and data quality. This will allow the data stewards to search and discover metadata as well as understanding the data quality associated with each attribute.

What are DQLabs.ai’s Semantics Discovery Features?

DQLabs’ Semantics Discovery will be of great help for any type of data source. It has built-in support and integration for simplified metadata management and automates the process of discovering, inventorying, profiling, and tagging data. Some of the features include;

Auto Discover Semantics

To help you discover and extract semantics from various enterprise data warehouses, operational databases, enterprise applications, cloud data stores, and nonrelational data stores with the help of just a few clicks using out-of-the-box connectors.

Automatic Sensitivity Classification

This will help you configure at ease your own sensitivity levels per your data governance programs and automatically identify the sensitivity footprint and classification at each attribute level.

Identify True Data Type

This is a feature that will help to ignore formatting, locale, and culture and identify the true data type at the attribute level to find relevant data quality rules.

Auto Discover Relevant Rules

With enriched semantics and business context for each attribute, let the platform discover all relevant data quality measurement rules for you.

Auto Detect Necessary Remediations

Measurement without remediation does not help a business to improve data quality. With enriched metadata and semantics, now you can enjoy remediation libraries that can perform smart curation at every attribute or dataset level.

Search and Discover with Relevance

Perform semantic searches across datasets and find the most relevant datasets by various metrics such as data quality score, drift level, sensitivity classification, etc., all within one platform.

Auto-tagging and Classification

Includes classification per the most up-to-date data privacy and security compliance regulations — such as the EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

How will your company benefit from DQLabs.ai’s Semantics Discovery?

Leveraging DQLabs’ Semantics Discovery will help you to scan through your data, analyze the characteristics as well as values, compare the data against other fields, and eventually propose semantic meaning and relationships with other datasets.

DQLabs.ai Semantic Discovery capabilities can be purposed for any company’s needs and data and will work in different languages and parameters resulting in metadata that will allow further automation.

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