Data Observability

Monitoring your data without semantics or any business context results in a high level of false positive alerts, hampering rather than helping your organization. DQLabs’ process of semantic discovery allows business and technical users to focus on refining business context across all your data without any handwritten rules or manual efforts.

Overview

With the explosion of unstructured data into the enterprise, as well as traditional structured data, it has become a full-time task in itself to observe your data inventory. Adding to this arduous task is data that is located in various silos with a constant changing nature or meaning based on customer and business needs. No longer does the organization have the money, resources or time to manually discover and review this explosion of data.

To mitigate this concern, DQLabs allows users to easily connect to their data sources, quickly measuring and then monitoring your data for you. This capability will detect irregularities in your data loads including changes in data volume along with changes in data characteristics to identify outliers. You can also utilize inherent actionable alerts and notifications which you can integrate with any productivity and collaboration tools.

Data Observability Features

DQLabs shifts you into auto-pilot mode with its smart DQ monitoring capabilities. Impart adaptive rules with auto thresholds, benchmarking, and actionable alerts to manage and monitor your data environment which automatically adjusts and adapts based on data trends.

Duplicate Monitoring

Duplicate data affects your overall data strategy and erodes the reputation of your data quality and reporting. DQLabs is powered with advanced ML models which help you to automatically identify, monitor, and remediate duplicates.

Out-of-the-box Adaptive Threshold

The DQLabs platform eliminates the need for manual rules and fine tuning by providing you with out-of-the-box adaptive auto thresholds. Use this functionality and drift rules configuration capabilities to benchmark and monitor any attributes across your organization.

Drift Monitoring Across 14 Types of Detection

Get continuous DQ drift monitoring across all of your data by defining 14 types of smart anomaly detections with actionable alerts and notifications that can be integrated with any of your productivity and collaboration tools such as Outlook, Teams, Slack, and more.

Create your Own Behavioral Analysis

DQLabs data quality monitoring capabilities allows you to create your own behavioral analysis which uses time-series comparisons for multiple attributes, forecasting, analysis, and visualization.

Schema Level Monitoring

DQLabs helps you to monitor any changes made to an attribute or a specific dataset. It also monitors and alerts you to any alterations made to a collection of logical structures or schema objects in your data.

Source to Target Comparison

Benefit from the integrated ability to compare in real-time the source of your data to its target and get a complete picture of your data’s transformation journey over time.

Data Observability

Utilize DQLabs built in data observability to easily connect to your data sources with inherent actionable alerts and notifications which you can integrate with any productivity and collaboration tools. Detect irregularities in your data loads including changes in data volume along with changes in data characteristics to identify outliers with DQLabs smart monitoring capabilities. With DQLabs you can:

  • Monitor for duplicate data using advanced ML models
  • Execute monitoring for changes to attributes, data sets, logical structures and schema objects.
  • Define 14 types of smart anomaly detections with actionable alerts and notifications.
  • Create your own time-series comparisons for multiple attributes, forecasting, analysis, and visualization.

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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