Glossary

Anomaly

A record or data structure that failed one or more quality checks during a Scan. An anomaly is evidence of a potential issue within the configured check and Scan scope, not proof that the data is incorrect.

Accuracy

The data represents the real-world values they are expected to model.

Sync Operation

Detects new, changed, or removed containers and fields in a datastore and updates the inventory used by later operations such as Profile and Scan.

Comparison

An evaluation to determine if the structure and content of the source and target Datastores match.

Comparison Runs

An action to perform a comparison.

Completeness

Required fields are fully populated.

Coverage

The breadth and frequency of active quality checks evaluated for data. Within Quality Score, higher Coverage reflects more applicable checks being asserted, but it does not establish that every business requirement is represented.

Conformity

Alignment of the content to the required standards, schemas, and formats.

Connectors

Components that can be easily connected to and used to integrate with other applications and databases. Common uses include sending and receiving data.

Consistency

The value is the same across all datastores within the organization.

Container (of a Datastore)

The uniquely named abstractions within a Datastore that hold data adhering to a known schema. The Containers within a RDBMS are tables, the containers in a filesystem are well formatted files, etc.

Data-at-rest

Data that is stored in a database, warehouse, file system, data lake, or other datastore.

Data Drift

Changes in a data set’s properties or characteristics over time.

Data-in-flight

Data that is on the move, transporting from one location to another, such as through a message queue, API, or other pipeline.

Data Lake

​​A centralized repository that allows you to store all your structured and unstructured data at any scale.

Data Quality

The degree to which data is fit for its intended use. Teams evaluate quality through dimensions such as accuracy, completeness, conformity, consistency, coverage, precision, timeliness, and volumetrics.

Data Quality Check

A rule and its settings that express an expectation for data. Qualytics evaluates active checks during a Scan and records failures as anomalies for review.

Datastore

Where data is persisted in a database, file system, or other connected retrieval systems. You can check more in Datastore Overview.

Data Warehouse

A system that aggregates data from different sources into a single, central, consistent datastore to support data analysis, data mining, artificial intelligence (AI), and machine learning.

DFS Datastore

A file-based datastore that connects Qualytics to files on a distributed file system (DFS), such as cloud object storage like Amazon S3, Azure Blob Storage, or Google Cloud Storage. The Containers of a DFS datastore are files in formats such as CSV, XLSX, JSON, Avro, and Parquet.

Distinctness (of a Field)

The fraction of distinct values (appear at least once) to total values that appear in a Field.

Enrichment Datastore

Additional properties that are added to a data set to enhance its meaning. Qualytics enrichment includes whether a record is anomalous, what caused it to be an anomaly, what characteristics it was expected to have, and flags that allow other systems to act upon the data.

Excluded Field

A field that has been manually removed from quality monitoring by a user. Its quality checks are archived (except Expected Schema, which is updated), and dependent computed fields are also excluded. Excluded fields can be restored to active status.

Favorite

Users can mark instances of an abstraction (Field, Container, Datastore, Check, Anomaly, etc.) as a personalized favorite to ensure it ranks higher in default ordering and is prioritized in other personalized views & workflows.

Field Status

A property assigned to every field in Qualytics that determines how the platform interacts with it. The four statuses are Active, Masked, Missing, and Excluded. Field status controls whether a field is included in profiling, scanning, and quality check evaluations. Learn more in Field Status Overview.

Incremental Identifier

A Field that can be used to group the records in the Table Container into distinct ordered Qualytics Partitions in support of incremental operations upon those partitions:

• a whole number - then all records with the same partition_id value are considered part of the same partition.
• a float or timestamp - then all records between two defined values are considered part of the same partition (the defining values will be set by incremental scan/profile business logic). Since Qualytics Partitions are required to support Incremental Operations, an Incremental Identifier is required for a Table Container to support incremental Operations.

Incremental Scan Operation

A Scan Operation where only new records (inserted since the last Scan Operation) are analyzed. The underlying Container must support determining which records are new for incremental scanning to be a valid option for it.

JDBC Datastore

A datastore that connects Qualytics to a relational database, data warehouse, or analytical database through a standard JDBC driver (for example, PostgreSQL, Snowflake, or SQL Server). The Containers of a JDBC datastore are tables.

AI Effort

A Profile operation setting that controls how broadly Qualytics AI explores observed patterns and check types. The labeled scale has six levels: Off, Low, Medium, High, Extra High, and Max. Each higher level builds on the previous one. In the API, this is the ai_effort field.

AI Managed Check

A data quality check generated, validated, and maintained by Qualytics AI from observed data behavior. Most originate during Profile operations; Volumetric and Freshness checks can use Observability measurement history. AI Managed checks display a purple AI badge with a four-point star icon. The applicable oversight setting controls whether new checks become Active or remain Draft for review. Editing evaluation logic converts the check to Authored so the team's decision is preserved.

AI Badge

A purple pill containing a four-point star icon and the text "AI" that visually identifies AI Managed checks (tooltip: "Authored by AI and is continuously tuned to your observed data") and anomalies whose every failed check is AI Managed (tooltip: "Identified by AI managed checks").

Qualytics AI

Qualytics AI uses observed data behavior to generate, validate, and maintain AI Managed checks. These checks provide starting coverage from data patterns. Data stewards control oversight, review important checks, and add Authored checks for business expectations that observed data cannot establish.

Masked Field

A field that remains fully operational (profiled, scanned, and quality-checked) but whose actual values are hidden across the platform by default. Users with Editor permission can reveal masked values, and every access is recorded in the masking audit log. Learn more in Field Masking.

Merge Fields

An operation that combines two fields on the same container, typically when a column is renamed in the source data. The source field keeps its history (quality checks, anomalies, profiles) and adopts the target field's name. The target field is removed. Learn more in Merge Fields.

Metadata

Data about other data, including descriptions and additional information.

Missing Field

A field that was previously active but is no longer found in the source data during a profile operation. Missing fields are automatically restored to Active when they reappear. They cannot be manually restored by a user. Learn more in Field Status Lifecycle.

Native Datastore

A datastore that connects Qualytics to a database or data warehouse through the platform's own integration instead of a JDBC driver. Currently available for Databricks.

Object Storage

A type of data storage used for handling large amounts of unstructured data managed as objects.

Operation

The asynchronous (often long-running) tasks that operate on Datastores are collectively referred to as "Operations". Examples include Sync, Profile, Hash, and Scan.

Partition Identifier

A field used to divide a container's records into smaller groups for operations. When possible, use a datetime Incremental Identifier so the same field can support both partitioning and incremental processing.

Pipeline

A workflow that processes and moves data between systems.

Precision

The level of detail or resolution present in data compared with what its intended use requires.

Profile Operation

An operation that generates metadata describing the characteristics of your actual data values.

Profiling

The process of collecting statistics on the characteristics of a dataset involving examining, analyzing, and reviewing the data.

Proprietary Algorithms

A procedure utilizing a combination of processes, tools, or systems of interrelated connections that are the property of a business or individual in order to solve a problem.

Quality Score

A measure of data quality calculated at the Field, Container, and Datastore level. Quality Scores are recorded as time-series enabling you to track movement over time. You can read more in Quality Scoring.

Qualytics App

aka "App" this is the user interface for our Product delivered as a web application.

Qualytics Deployment

A managed or self-hosted Qualytics environment used by one customer organization.

Qualytics Implementation

A customer’s Deployment plus any associated integrations.

Qualytics Partition

The smallest grouping of records that can be incrementally processed. For DFS datastores, each file is a Qualytics Partition. For JDBC datastores, partitions are defined by each table’s incremental identifier values.

Record (of a Container)

A distinct set of values for all Fields defined for a Container (e.g. a row of a table).

Schema

The organization of data in a datastore. This could be the columns of a table, the header of a CSV file, the fields in a JSON file, or other structural constraints.

Schema Differences

Differences in the organization of information between two datastores that are supposed to hold the same content.

Source

The origin of data in a pipeline, migration, or other ELT/ETL process. It’s where data gets extracted.

Tag

Users can assign Tags to Datastores, Profiles (Files, Tables, Containers), Checks and Anomalies. Add a Description and Assign a Weight. The weight value directly correlates with the level of importance, where a higher weight indicates higher significance.

Target

The destination of data in a pipeline, migration, or other ELT/ETL process. It’s where data gets loaded.

Third-party data

Data acquired from a source outside of your company which may not be controlled by the same data quality processes. You may not have the same level of confidence in the data and it may not be as trustworthy as internally vetted datasets.

Timeliness

It can be calculated as the time between when information should be available and when it is actually available, focused on if data is available when it’s expected.

Volumetrics

Data has the same size and shape across similar cycles. It includes statistics about the size of a data set including calculations or predictions on the rate of change over time.

AgentQ

The AI assistant built into the Qualytics platform. AgentQ uses the AI provider configured for the deployment and the Model Context Protocol (MCP) to help users explore datastores, build transformations, create quality checks, and investigate anomalies through natural language. Its results and changes remain subject to user permissions and human review. Learn more in AgentQ Overview.

Model Context Protocol (MCP)

An open standard that defines how AI assistants connect to tools and resources exposed by external systems. Qualytics implements an MCP server that makes authorized data quality context and actions available as tools. Learn more in MCP.

MCP Server

An application that exposes a set of tools and resources through the Model Context Protocol. The Qualytics MCP server handles authentication, executes tool logic, and returns structured results to connected AI clients.

MCP Client

An AI assistant or agent (like Claude Desktop, ChatGPT, Cursor, or AgentQ) that connects to one or more MCP servers and can call their tools during a conversation.

MCP Tool

A discrete capability exposed by an MCP server that an AI assistant can call. Each tool has a name, description, input schema, and structured output. Examples include list_datastores, create_quality_check, and anomaly_describe.

Agentic Endpoints

Qualytics API endpoints that use an agent to service the request, bringing conversational AI capabilities directly into custom applications and automation workflows. Because they delegate to an agent, they are non-deterministic. They provide the same capabilities as MCP through standard REST calls. Learn more in Agentic Endpoints.

LLM (Large Language Model)

An AI model that can understand and generate natural language. AgentQ uses the provider configured for your deployment, either the Qualytics-managed provider or a supported external provider, to interpret requests and decide which available tools to call.

Topic Guardrail

An AgentQ control that checks whether a request relates to data quality or the Qualytics platform and declines requests outside that scope. Relevant requests can occasionally be misclassified and may need to be rephrased.

Context Injection

When AgentQ is opened from a page with relevant data (a datastore, container, field, quality check, or anomaly), the asset's identity is automatically embedded in the conversation context. This allows the user to ask questions without specifying which asset they mean.

Co-Authorship Tracking

Platform changes made through AgentQ are stamped with a co-author record in the audit trail alongside the user's identity, providing a record of AI-assisted actions.

Guided Workflow

A multi-step tool that executes a structured process for complex tasks. AgentQ includes guided workflows for analyzing trends, investigating anomalies, interpreting quality scores, generating quality checks, and creating computed assets.

Weight (Importance / Severity)

The numeric weight value directly correlates with the level of importance, where a higher weight indicates higher significance. In the UI, this value is surfaced under different labels depending on the entity:

• Importance: On checks (cards, rows, totals, sort options, observability, templates, histograms).
• Severity: On anomalies (cards, rows, highlight panel, detail modal, sort options, flow trigger filters).

Field weight, tag weight, and quality-score dimension weights (Completeness, Coverage, Conformity, Consistency, Precision, Timeliness, Volumetrics, Accuracy) continue to use the term Weight with the existing icon. In the API, the underlying field is weight for both checks and anomalies, and weight_threshold for the flow trigger anomaly filter.