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Anomalies Overview

An anomaly records one or more quality check failures found by a Scan. It points to a record, a data structure, or a set of rows that did not meet what a check expects, and it keeps the evidence your team needs to decide what happened. This hub gathers everything about reviewing, triaging, and resolving anomalies.

The Deep Dive pages explain the concepts, from what an anomaly is and how a Scan creates it to its status, Impact, and fingerprint. The How-tos walk through each task step by step, grouped by what it changes. The API page covers the endpoints, and the FAQ answers the common questions.

Permissions

The Reporter team permission on the datastore is enough to see its anomalies. Changing an anomaly's status, tags, assignees, or description, and archiving or deleting it, requires the Author team permission. See Anomaly Permissions for the full matrix.

Deep Dive

Understand what an anomaly is, how a Scan creates it, and what each part of an anomaly tells you.

  • Getting Started

    A first triage: find an anomaly, read it, assign it, and set its status.

    Getting Started

  • Introduction

    What an anomaly is, why it matters, and a short tour of every topic in this Deep Dive.

    Introduction

  • How It Works

    How a Scan creates anomalies, AI Managed and Authored checks, the lifecycle, duplicates, and notifications.

    How It Works

  • Record Anomalies

    Anomalies that flag one row: when they are created, how rows are grouped, and examples.

    Record Anomalies

  • Shape Anomalies

    Anomalies about many rows or the whole dataset: what creates them, what they count, and examples.

    Shape Anomalies

  • AI Managed and Authored

    How anomalies from AI Managed and Authored checks differ, the AI badge, and how triage tunes AI Managed checks.

    AI Managed and Authored

  • Impact

    How the Low to Critical impact level is decided from the failed checks, and when it updates.

    Impact

  • Status

    The lifecycle of an anomaly, from Active and Acknowledged to the archived statuses.

    Status

  • Assignees

    How the Assignees field works, who can be assigned, and when assignees are notified.

    Assignees

  • Fingerprints

    How Qualytics recognizes a newly detected anomaly as a duplicate of one it already found.

    Fingerprints

  • Anomaly Views

    The four screens that show anomalies: how to reach each one, what it shows, and what you can do there.

    Anomaly Views

  • Description

    The business-friendly explanation of the data quality issue behind an anomaly.

    Description

  • Source Records

    The rows behind an anomaly, how to read them, and how masked values are protected.

    Source Records

  • Tickets

    Linked Jira and ServiceNow tickets: sync modes, what reaches the ticket, and status sync.

    Tickets

  • Timeline

    The record of every change to an anomaly, and how comments and mentions work.

    Timeline

  • Examples

    Real scenarios that follow an anomaly from the Scan that finds it to the decision that closes it.

    Examples

  • Best Practices

    Guidelines for keeping the anomaly list short, trusted, and acted on.

    Best Practices

  • Permissions

    The team permission and user role behind every anomaly action.

    Permissions

How-tos

Triage and manage anomalies step by step.

Reference

  • API

    Read, update, archive, and delete anomalies, and work with their comments, source records, and tickets programmatically.

    API

  • FAQ

    Common questions about types, Impact, status, duplicates, tags, tickets, and assignees.

    FAQ