2021.10
Collibra DIC Integration
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What is OwlDQ
OwlDQ is an intelligent data validation tool.

Relief for Backlogged Resources. No more bottlenecks for business users. Confidence your data is validated.

8 Ways to Add-Value Using OwlDQ

    1.
    Crowdsourcing
    “People that have never written SQL are now helping with data quality”
    2.
    Rule Coverage
    “Did in 20 days what took 2 years with our legacy tool”
    3.
    Audit & Identify Gaps
    “Audited our existing checks and could not imagine the gaps we uncovered.”
    4.
    Automate Repeatable Processes
    “Owl cut 60% of our manual workloads”
    5.
    Technology Limitations
    “We now scan files and Kafka, avoiding downstream issues”
    6.
    Getting standard
    “No more piecemeal reports. Files, Warehouse, Lake. All metrics in one, transparent place.”
    7.
    Building Reports, Visuals, Workflows
    “This takes the place of 3 tools”
    8.
    Prioritized Efforts
    “Easy to see top priorities for improvement”
    Another expensive project missing deadlines?
    Tired of wasting an afternoon unwinding ETL / ingestion jobs?
    Know the dread of another fire drill?
    Is it crazy to think your time can be better spent than wading through data issues?

OwlDQ Detects Unintended Data Errors Without Human Bottlenecks.

Systematically Eliminate Your Biggest Data Blind Spots.

Focus on Adding Business Value & Avoid Expensive, Complex Commitments

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
    Saved 2000 hours during a cloud migration requirement
    Top Insurance Organization
    Satisfied Regulatory Second Line Controls in a 4-weeks (what took 2 years using their previous tool)

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

How Can OwlDQ Help?

Click a button and smile - knowing baseline validation checks are applied - instead of spending hours manually digging through data & stitching together scripts

    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
      Self-Service, Easy to use Rule Editor
      Pre-Built Analytics and Charts
      Extensible APIs, Open Architecture

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

What makes OwlDQ unique?

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
"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."
"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."
"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."
"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."

Schedule a Conversation

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.

Last modified 8mo ago