Why Owl

What's The Problem?

Detecting data issues is nuanced, manual and time consuming. The traditional solution is to write bespoke code or use a rules engine to validate specific columns in a dataset. If missing data is a concern a common remedy is to write a nullcheck. Another common example is a row count check; a piece of logic that checks if the number of rows in a dataset is greater than a specified number. Of course, DQ and business rules can get much more complicated. Scale becomes a huge issue, because it is nearly impossible to write all the rules that a business truly needs to be confident in their data. Often times, the math is f(x) = columns * dbTables. 100 columns on average and 500 tables in a single warehouse equals 50,000 rules if you only wrote 1 rule per column. The reality is you need many rules per column, and your business has more than 500 tables and files. But there are even bigger problems with this strategy. Rules are a reactive approach to solving the problem; they are manually written and they don’t adapt (they are static). With a rules only approach you can measure your franchise risk by the number of rules you can write. This requires coders, domain experts and a tool to write and then manage the rules.

Why a Unified DQ Solution?

Aren't their other DQ companies and solutions on the market? Yes, absolutely. The challenge is the vast ways IT groups consume and process data. You need to find a product that can plugable and scaleable across Files, DB Tables, Data Frames and Kafka Topics etc... You need to find a product that offers a consistent feature set and covers all 9 dimensions of DQ. For most companies DQ is an after thought, an add-on, and only covers a single dimension such as rules or data drift. Owl offers a full DQ suite to cover the unique challenges of each dataset. Complete coverage and consistency drives trust. A single scoring and reporting framework with 9 pluggable features that can be activated in a tailorable DQ pipeline. Owl is horizontally scaleable, it can scan data from any location with infinity scale. Data quality needs to be abstracted from data ingestion for management to have a single normalized view of data health.

How Can Predictive DQ Help?

Owl intentionally solves the problem using a machine learning first, rules second based approach. Owl automatically puts all columns under quality control. This includes nullchecks, emptychecks, statistical profiles, sketches. Owl creates snapshots and baselines in order to benchmark past data and discover drift. Owl automatically creates a ML labeling system for users to collaborate and down-train items with a click of a button. The reason for this approach is obviously to maximize coverage while reducing the dependency of manual rule building. The greater technical benefit is that all of Owl's generated checks and rules are adaptive. Owl is constantly learning from new data and will make predictions in many cases for: typos, formatting issues, outliers and relationships. This is a paradigm shift from, risk being a measure of how many rules one can dream up and write, to simply click the Owl [RUN] button.

Do One Thing Extremely Well

Owl believes that data quality is such an important part of the data lifecycle that it requires a company which is solely committed to revolutionizing the way enterprises manage DQ. This is why Owl has a prescriptive approach to DQ (ML first, Rules second). The Owl software is purpose built for predicting and detecting DQ issues. Much like how Jira is used as the standard for software project management even though it is absolutely possible to manage project line items in an excel sheet. Businesses that manage a lot of data require Score Cards, Alerts, Reports, List Views, Collaboration, Down Training, Cataloging, Scheduling and much more.

Governance Checklist

Audit Passing

Pass stringent data governance audits. Export AI into SQL rules. Export profiles to excel. Generate reports and show user lineage.

Use AI

Be predictive vs reactive. Learn form data to become incrementally smarter and track down issues as soon as they arise.


Replay data tests. Re-create data scenarios and test what-if outcomes. Become agile in your approach to interactively making data quality better.

Our Team


Our elite team is dedicated to one thing, making the data that flows through your organization perfect. Owl regularly works with fortune 500 companies to put a defensible data quality program in place. Many managers and data stewards lose sleep over the unknowns lurking in their data. Let our team show you how to monitor and answer the unknowns for good.

Owl's services team is comprised of passionate data scientists who have spent their careers dealing with challenging data problems. Additional Owl services include an Owl Data Steward who can assist remote or on-site. Let Owl manage your data quality program with our managed service offering for guaranteed success.