Connecting to DBs in Owl Web

What is OwlDQ

OwlDQ is an intelligent data validation tool.

OwlDQ Detects Unintended Data Errors Without Human Bottlenecks.

  • Tired of wasting an afternoon unwinding ETL / ingestion jobs?

  • Know the dread of another ‘missing data’ fire drill?

  • Is it crazy to think your time can be better spent than wading through data issues?

Focus on Adding Business Value & Avoid Expensive, Complex Commitments

Systematically Eliminate Your Biggest Data Blind Spots.

How Can OwlDQ Help?

Boost productivity. 80% faster than manual coding. Reduce development costs. Get faster, easier access to data quality metrics. Show line of business users how to self-service.

  • Implementing Checks

    • Autodiscovery

    • Generates SQL validations, parameters & thresholds

    • Rule suggestions

  • Taking Inventory

    • Bulk Profiling & Metadata Collection

    • Data Mapping with Column Identification

    • Map Column Fingerprints, Cross-Table Matches & PII Checks

  • Consolidating Systems

    • No more closed-systems or confusing scripts

    • Macro & micro views for measuring effectiveness over time

    • Global management Across Sources / Platforms / Environments

  • Enabling More Users

    • Easy to use Rule Editor

    • Pre-Built Analytics and Charts

    • Extensible APIs, Open Architecture

What Savings Does OwlDQ Provide?

Save Hours of Effort with Auto-generated Data Validation Checks

  • Top 10 Bank

    Reduced 60% of their manual Data quality workload + $1.7M cost savings

  • Top 3 Healthcare Organization

    Completed a 6 Month Data Check migration requirement in six weeks

  • Top Insurance Organization

    Satisfied Regulatory Second Line Controls in a 4-week program

While Reducing System-Wide Pain Points

  • Overwhelmed with tickets

  • Business users find issues first

  • Touchy pipelines break with minor updates

  • Too busy responding to fire drills to implement new projects

What Sets OwlDQ Apart?

OwlDQ is The Only Tool Business & Technical Users Will Love

Every feature, visual, and component within Owl is intended to make the analysis and implementation of data checks easier.

Why Use Owl?

Because Humans Can’t Predict Every Which Way Data Can Go Wrong.

Billing Issue Example
Financial Data Example
API Example
IoT / Meter Example
Billing Issue Example

"An unexpected ETL update occurred during a migration that changed our up-to-date-payments indicator from TRUE/FALSE to 1/0. Needless to say, we were very surprised when invoices were not sent. The rework and reconciliation were super painful. An enormous amount of my time was wasted."

Financial Data Example

"One of our 200+ reference data feeds introduced a pipe (|) into a position field. The field was defined as VARCHAR so this was allowed. Our models went crazy and we thought we breached risk limits. We ended up selling out of positions (losing millions). Only to uncover the root cause much later that week."

API Example

"We pull data from many APIs. One platform accounts for 10% of enrichment activities (i.e. how we monetize our data). Our auth token accidentally had a daily quota imposed, yet job control said green light (successful connection). We still loaded some rows (1k), just not entire payloads. This was super nuanced. We literally lost ~10% revenue that month."

IoT / Meter Example

"When we introduced new meters, they were hooked up and sending valid readings. They were valid values within valid ranges. Turned out their default setting was rounding the actual values and we were losing precision. Devastating, considering the amount of precision required with blood values."

What's Next?

Schedule a Conversation to Start

OwlDQ offers a low-hanging fruit opportunity to extend your data quality toolset.

The fact that data quality is a consistent pain point suggests it's important to many business-critical functions and legacy products not getting the job done.