OwlDQ

OwlDQ provides predictive data quality software that connects to over 40 databases and file systems, enabling real-time data scanning and monitoring. The platform automatically generates adaptive rules and enforces data quality standards to prevent the propagation of inaccurate data, thereby reducing operational costs and improving decision-making accuracy.

Traction 10/10

Glenelg North, United States · HQ

Founded 20171K

Artificial Intelligence
Data & Analytics
Enterprise Software
Software Only

Updated 5 months ago

Funding

Funding rounds are not available yet.

Founders

2 founders

Stijn Christiaens

Co-Founder

Felix Van de Maele

Co-Founder

Product

Problem

Organizations struggle with data quality issues stemming from human errors, siloed tools, and inadequate data strategies, leading to higher operational costs and inaccurate reporting. Detecting and resolving these issues across diverse databases and file systems is challenging, resulting in untrustworthy data and flawed business decisions.

Solution

OwlDQ provides a data quality platform that connects to various data sources, enabling real-time data scanning and monitoring to ensure data accuracy and consistency. The platform uses AI-generated adaptive rules to automatically identify and address data anomalies, preventing the propagation of inaccurate data downstream. By classifying sensitive data and detecting unexpected schema changes, OwlDQ helps organizations maintain data validity and prevent potential issues on critical assets. The platform's auditing and monitoring capabilities offer insights into data pipeline health, revealing blind spots in data operations and enabling proactive data quality management.

Target Audience

OwlDQ is designed for data engineers, data stewards, and IT professionals who need to ensure data quality across their organization's data landscape.

Features

Connects to over 40 databases and file systems, supporting both pushdown and pull-up processing
AI-generated AdaptiveRules automatically adapt to changing data conditions
Automated data classification using industry-specific rules to identify broken records
Generative AI for writing complex data quality rules with natural language prompts
Data pipeline health monitoring by business unit and data source
Schema change detection to ensure data validity
Reusable rule templates for accelerating custom rule creation