concept#Data#Analytics#Data Modeling

Logical Data Model

A logical data model describes the structure and relationships of data within a specific domain.

A logical data model is crucial for understanding data and its relationships within an organization.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeDesign
Organizational maturityIntermediate

Technical context

Integrations

CRM SystemsERP SystemsAnalytics Tools

Principles & goals

Principles

Clarity about data structures.Normalization of data.Define relationships between entities.

Value stream stage

Build

Organizational level

Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Data loss during migration.
Faulty data integration.
Outdated data models.

Best practices

Regular review of the model.
Involvement of stakeholders.
Use of standards.

I/O & resources

Inputs

Existing data sources.
Requirements for the data model.
Stakeholder feedback.

Outputs

Created data model.
Consolidated records.
Analysis report.

Resources

Description

A logical data model is crucial for understanding data and its relationships within an organization. It enables a clear representation of entities, attributes, and their interactions, enhancing data management and usage.

✔Benefits

Improved data integration.
Increased data quality.
Optimized reporting.

✖Limitations

May not cover all use cases.
Dependent on correct data sources.
Requires continuous maintenance.

Trade-offs

Metrics

Data Integration Rate
The percentage of successfully integrated data.
Time to Generate Reports
The average time taken to generate a report.
Data Quality Score
A measure of the quality of the data used.

Examples & implementations

Banking Data Model

A logical data model for a banking system describing accounts and transactions.

E-Commerce Data Model

A data model for an e-commerce company for managing products and orders.

Healthcare Data Model

A data model for a healthcare system managing patient information.

Implementation steps

Analysis of requirements.

Design of the data model.

Implementation in a test environment.

⚠️ Technical debt & bottlenecks

Technical debt

Outdated model structures.
Insufficient documentation of changes.
Lack of employee training.

Known bottlenecks

Integration complexity.Resistance to change.Insufficient data resources.

Misuse examples

Use of unverified data.
Neglecting quality standards.
Missing attachments for data sources.

Typical traps

Not considering data requests.
Overlooking stakeholder feedback.
Too quick implementation without testing.

Required skills

Data analysis skills.Knowledge of data modeling.Experience with SQL.

Architectural drivers

Technological changes.Changes in business requirements.Data quality standards.

Constraints

• Regulatory requirements.
• Technical limitations.
• Resource capacities.