concept#Data#Platform#Data Management#Metadata

Data Catalog

A data catalog is a central resource for managing data inventories.

A data catalog enables organizations to manage and make their data accessible efficiently.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityAdvanced

Technical context

Integrations

DatabasesBI ToolsAnalytical Platforms

Principles & goals

Principles

Maximize data qualityEnsure accessibilityPromote collaboration

Value stream stage

Build

Organizational level

Enterprise, Domain

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Security vulnerability with insufficient protection
Data quality issues
User acceptance issues

Best practices

Regularly check data quality
Train users
Document processes

I/O & resources

Inputs

Access Request
Data Source Description
Metadata Updates

Outputs

Access Rights
Data Source Reports
Updated Query Results

Resources

Description

A data catalog enables organizations to manage and make their data accessible efficiently. It supports the documentation of data sources, management of metadata, and optimization of data preparation.

✔Benefits

Increased efficiency in data management
Better decision support through access to relevant data
Optimized data analyses

✖Limitations

Requires ongoing maintenance
Dependent on technology
Requires training

Trade-offs

Metrics

User Satisfaction
Measuring user satisfaction when accessing the catalog.
Data Completeness
Checking the completeness of data listed in the catalog.
Analysis Speed
Measuring how quickly analyses can be conducted using the catalog.

Examples & implementations

Data Catalog at Company X

Company X has developed a data-driven catalog that facilitates access to multiple data sources.

Optimizing BI Processes

A comprehensive data catalog has led to increased efficiency in business intelligence activities.

Successful Data Analysis at Company Y

Company Y uses the data catalog to conduct high-quality analyses.

Implementation steps

Analyze data sources

Create a catalog structure

Implement data access

⚠️ Technical debt & bottlenecks

Technical debt

Outdated technologies
Insufficient system integration
Lack of adaptability

Known bottlenecks

Data QualityAccess IssuesTechnology Limitations

Misuse examples

Incomplete data source description
Poor user interface
Ignoring data protection regulations

Typical traps

Wrong prioritization of tasks
Lack of testing before implementation
Ignoring data silos

Required skills

Data Analysis SkillsMetadata ManagementTechnical Knowledge

Architectural drivers

Compliance with data protection regulationsIntegration of existing systemsScalability of the data infrastructure

Constraints

• Compliance with standards
• Technological prerequisites
• Budget constraints