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

See what DQLabs can do

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