Key concepts
Below you can find an overview of fundamental term and concepts in Dataedo, designed to help you understand its capabilities and core components.
Glossary entry
The glossary defines organizational terminology. It helps to maintain a consistent understanding of critical concepts across teams. Each entry includes a title, definition, and optional fields.
Catalog
A data catalog provides a central inventory of an organization’s data assets. It organizes and manages enterprise-wide data, including metadata like glossaries, classifications, data lineage, reports or badges. A data catalog is designed to help your teams locate, assess, and use data effectively for analytics.
Community
A Data Community in Dataedo is a collaborative space within your organization where team members can share insights, provide feedback, and collectively enhance the understanding of your data assets. It brings together features like comments, ratings, questions, to-dos, and warnings, enabling seamless communication and knowledge sharing.
Data community fosters an environment where your Dataedo users can clarify uncertainties, highlight important details, and improve the overall quality of your data documentation. For example, if you encounter unclear descriptions or missing details in the Portal, users with appropriate permissions can directly edit fields such as titles, descriptions, or custom values.
Connector
Connectors are built-in libraries that allow Dataedo to extract metadata from various data sources. They support native connections (for example, SQL Server, Oracle) and generic ones (for example, via ODBC).
Critical data
Critical data represents the most important information in your organization, such as financial data, customer records, or intellectual property. Dataedo helps you identify, document, and protect this data.
Custom fields
Admins can define up to 100 custom fields, such as checkboxes, lists, or text fields, to customize documentation and capture additional metadata attributes.
Data asset
A data asset represents any documented entity, such as tables, views, reports, stored procedures, or triggers. These are the core objects managed within your Dataedo repository.
Data classification
Dataedo helps you comply with data protection regulations by identifying and tagging fields that contain sensitive data. It supports multiple parallel classifications, each tailored to different purposes or regulations, including two built-in options: PII and GDPR.
The system automatically suggests classifications based on column names and the classifications of other fields in the repository. However, users make the final decision and can also manually curate classifications.
Classifications are stored in two user-defined fields:
- Data classification - indicates the level of sensitivity (for example, sensitive, special category).
- Data domain - defines the type of information (for example, email, name).
Administrators can also create custom classifications to suit their needs.
Data dictionary
Data dictionary defines data sets and their elements within a specific data source or model. It includes metadata such as data sets, fields, relationships, and definitions, providing clarity on the structure and meaning of data stored in a database.
The Dataedo Data Dictionary lets you and your team document your data schema, including tables, columns, and relationships. It provides tools to visualize your data model and share detailed documentation across your organization. It allows even non-technical users to explore and analyze data independently.
Data domain
Data domains are logical groupings of physical data assets designed to organize and present these assets in a way that reflects stewardship or ownership. They align with the principles of the data mesh model, emphasizing decentralized ownership and domain-oriented design. Data domains represent the Data Team's or IT perspective, showcasing how the organization's data and applications are structured and managed.
Data lineage
Data Lineage in Dataedo allows you to track the data flow through your ecosystem, from its origin to its destination. At its core, data lineage maps relationships and transformations between data sources and data objects. It allows users to visualize data dependencies, uncover issues, and ensure consistency while processing any changes in the system. Data lineage in Dataedo operates across different scopes–from individual columns and tables to entire systems, offering insights into the connections and transformations at every step of the data lifecycle.
You can browse through Data lineage in Dataedo Portal and use manual designer in the Desktop, where you can define processes and flows.
Data profiling
Data profiling analyzes your database content. It provides insights into data patterns, distributions, and anomalies. It enables reviewing sample data and metrics to assess data quality.
Data Quality rule
A data quality rule defines the criteria for evaluating your data's consistency, completeness, and accuracy. It helps you ensure it meets business requirements.
Description
Every object in Dataedo includes a description field, which allows data stewards, architects, and developers to document data models effectively.
Dataedo initially imports descriptions from external sources into the repository, but only when the description field in Dataedo is empty. Once you provide a description, it won't be overwritten by subsequent updates, as Dataedo serves as the master source for descriptions.
Objects like tables support rich-text descriptions, while object elements (columns, parameters) support plain-text descriptions.
Custom fields
Admins can define up to 100 custom fields, such as checkboxes, lists, or text fields, to customize documentation and capture additional metadata attributes.
ER diagram
With Dataedo, you can visualize your database's structure through detailed ER diagrams (Entity-Relationship diagrams). These diagrams break down complex data models into smaller, focused views, highlighting key entities, their primary columns, and the physical and logical relationships between them.
Knowledge base
The knowledge base includes business domains that reflect logical divisions within a company that help group data by business group or department. For example, they can represent areas like HR, Finance, Product, Development, or Customers. These domains provide a structured way to organize resources and manage data relevant to specific business areas.
Lookup (reference data)
Lookups, or reference data, provide fixed, standardized values (for example, country codes and status labels) that enrich datasets and maintain consistency.
Master data
Master data refers to the critical business information shared across an organization. It typically includes key entities like customers, products, suppliers, and locations, serving as a single source of truth for consistent and accurate data across systems.
Metadata scan
A metadata scan retrieves schema details from data sources, identifying attributes like table structures, relationships, and constraints.
Repository
A repository is a central database where all metadata is stored. It can be hosted on platforms like SQL Server or Azure SQL Database. The repository organizes and secures your metadata, and acts as the backbone of the Dataedo platform.
Schema changes
Schema Change Tracking in Dataedo helps monitor and document database schema changes, such as updates to tables and columns, with added context through comments.
It supports development teams in updating code and reports, maintenance teams in identifying schema-related issues, and data governance efforts in tracking data asset modifications.
SQL parsing
SQL parsing in Dataedo analyzes SQL scripts to extract metadata, which facilitates the documentation of complex queries or transformations.
Steward Hub suggestions
Dataedo provides suggestions to help data stewards document objects more effectively by reusing descriptions and classifications.
