Why DQLabs

Typical data governance strategy involves stewards manually managing data via a process that requires time and effort and cannot keep up with the growing demands in modern data management. Outlined below are the steps organizations go through before they achieve a baseline level of measurement. This approach is cumbersome, it does not scale and more importantly “it doesn’t work!”

Why DQLabs

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.

DQLabs with its AI/ML powered solution helps organizations radically streamline the process of creating actionable data quality initiatives by providing out of the box capabilities around:

  • Automatic semantic classification with no metadata, or with metadata and further enhances the business context of the data using any existing catalog systems in your ecosystem as well.
  • Autodiscover rules using discovered semantic classification for DQ scoring.
  • Measure and Monitor Data Quality automatically using subjective and objective dimensions, adaptive thresholds, outlier, and anomaly detection, as well as data observability principles.
  • Search, discover with relevance using the above calculated metrics for managing assets in your data governance or catalog platforms.
  • Remediate Data Quality Issues by smart curation, issue workflow management, and integration with other 3rd party tools for collaboration.
  • Measure business outcomes and receive valuable insights for recommendations using out of the box dashboards.
  • No manual upkeep or maintenance using our self-learning and self-service platform.

See what DQLabs can do

CASE STUDIES

Smart Cities MDM Initiatives

The City is one of the top-ranked metropolitan areas in the United States. The City’s regional economy is versatile and spread across various verticals, with a robust emphasis on life sciences, agribusiness education and research, logistics, manufacturing, aerospace, and professional services.

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Asset Management Challenges - Case Study Brief

CASE STUDIES

Asset Management Challenges

One of our customers is a leading US-based Asset Management service provider dealing with businesses like brokerage firms, banks, trust companies, insurance providers, and credit unions which involves different data sources like Investment Accounting, Trading, Compliance and Billing systems, etc.

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Price & Operational Analytics - Case Study Brief

CASE STUDIES

Price & Operational Analytics

A major automotive software provider is in the business of consolidating data from different dealerships using Dealer system providers. A Dealer Management System is a software suite that provides the tools auto dealers need to more effectively run their business.

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Best Practices

Semantic Discovery for Data Quality Management

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.

Interested in a platform demo? Signup now.

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