🔔 Stay tuned. Data quality is a new capability in dScribe, and we're still expanding it. This article covers the fundamentals — full documentation is coming soon.
You can now define data quality rules directly in dScribe. Rules are modeled on the Open Data Contract Standard (ODCS), so the quality expectations you capture in the catalog use an open, portable format rather than a dScribe-only definition.
What you can do today
Data quality rules let you express what "good" looks like for your data and attach those expectations right where the data is documented:
Define rules without code — build rules through a guided form; no SQL or scripting required (though SQL and custom options are there if you want them).
Apply rules at two levels — on a dataset as a whole, or on an individual dataset element (a column).
Stay tool-agnostic — rules follow the ODCS, and can be executed by open-source data quality engines you already use: Soda, Great Expectations, and Monte Carlo.
Adding a quality rule
Open a dataset or one of its columns and choose Add rule. The Add quality rule dialog walks you through a few choices.
Severity
Set how a failing rule should be treated: Info, Warning, or Error. This lets you separate purely informational checks from the ones that should raise a real flag.
Rule type
Pick how the rule is defined:
Library — a pre-built metric. Choose a Metric (for example, Invalid Values), a Dimension (for example, Accuracy), and a Threshold (for example, must be greater than (>) 0). This is the no-code path.
SQL — a custom query, for checks you'd rather express yourself.
Text — a documented-only rule: a quality expectation you want recorded in the catalog without an automated check behind it.
Custom — sync and execute in an external engine. Select the Engine (Soda, Great Expectations, or Monte Carlo) and a Dimension.
Description
Add a short description explaining what the rule checks and why it matters, so the next person to open the rule understands the intent at a glance.
Where rules show up
Once saved, a rule appears under Quality rules on the dataset or column it belongs to, with its metric, dimension, and threshold summarized in plain language — for example, Invalid values · accuracy · must be greater than (>) 0 (Amount). Each rule also shows a run state, such as Not run, until an execution has taken place.
Where to go next
→ More on data quality is on the way. We'll expand this article as the capability grows — reach out through the in-app chat if there's something specific you'd like to do.
Have a question or can't find what you're looking for? Use the chat icon inside the catalog to reach the dScribe support team.