Data quality measurement without semantics or understanding of business context of the data does not help drive better business practices. With DQLabs’ Data Sense™ capabilities, you can automatically enrich semantics for any type of data with or without metadata information.

Now you can not only automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management but also auto discover rules as well assensitivity classification in alignment with your organizational landscape.

The traditional manual process of creating business glossaries, mapping assets, identifying business rules, and continuously going through the process of upkeeping has failed.

With DQLabs out of the box capabilities, you can perform all forms of data quality tasks automated efficiently to measure, score and identify bad data using auto-discovered rules, patterns, insights, fraud, missing values, and correlations across data source, data set, attribute, department/function or organizational domain level.

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

Monitor with benchmarking, trend forecasting, and actionable alerts to cover a wide variety of DataOps and Data Observability use cases such as data pipeline monitoring, source to target checks, schema or data level deviations or abnormalities.

Measurement without remediation does not help to improve data quality. Using DQLabs you can not only identify the bad records as measured by the DQScore™ but our platform also allows you to remediate and fix any kind of data quality issues within your organization.

DQLabs remediates your data quality issues by cleaning, enriching, and merging good records using ML-based smart curation libraries, modules and it identifies the optimal data preprocessing strategies to make this process faster while it also automates data curation with controls on data quality thresholds.

Using DQLabs both business and technical stakeholders can analyze and make sense of bad data to their own ends. All stakeholders can better understand their own data and achieve their functional goals faster than ever before.

The platform is built to empower users to derive usable insights and to recommend the next best actions both strategically and tactically. With the platform, you can generate all the metrics you need and measure across any of the data dimensions used in your organization.