method#Data#Analytics#System Architecture

Source System Analysis

A structured approach to analyzing source systems within an organization.

The source system analysis aims to understand the existing data sources and their architecture.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityAdvanced

Technical context

Integrations

DatabasesData WarehousesETL Tools

Principles & goals

Principles

Ensure data integrity.Transparent documentation of analysis processes.Pursue iterative improvements.

Value stream stage

Build

Organizational level

Enterprise

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Data loss during migration.
Insufficient analysis leads to errors.
Lack of stakeholder involvement.

Best practices

Regular reviews of data quality.
Documentation of all analysis steps.
Involving stakeholders in the process.

I/O & resources

Inputs

List of existing data sources.
Data management policies.
Stakeholder requirements.

Outputs

Documentation of source system analysis.
Optimization plan.
Traceable migration steps.

Resources

Description

The source system analysis aims to understand the existing data sources and their architecture. It assists in optimizing data integration and migration while identifying potential issues early on.

✔Benefits

Optimized data migrations.
Improved data quality.
Reduced integration costs.

✖Limitations

Dependency on the quality of source systems.
Requires technical expertise.
Time-consuming with large amounts of data.

Trade-offs

Metrics

Migration Duration
Time taken to migrate data from a source to the target environment.
Data Quality Score
Assessment of data quality based on defined metrics.
Integration Success
Number of successfully integrated data sources compared to the total.

Examples & implementations

Data Migration in a Financial Institution

A financial institution successfully upgraded a legacy system, applying source system analysis.

Implementation of an ETL Pipeline

A company implemented an ETL pipeline based on the results of the source system analysis.

Data Quality Optimization

The source system analysis enabled a company to significantly improve data quality.

Implementation steps

Conduct an inventory of source systems.

Assess data quality and architecture.

Create a detailed migration plan.

⚠️ Technical debt & bottlenecks

Technical debt

Outdated systems.
Lack of training.
Insufficient documentation of processes.

Known bottlenecks

Slow data sources.Insufficient data quality.Lack of integration tools.

Misuse examples

Starting without analyzing existing systems.
Not considering data quality.
Lack of resource planning.

Typical traps

Over-optimism about data quality.
Underestimating the effort required.
Ignoring stakeholder feedback.

Required skills

Data management skillsAnalytical thinkingTechnical knowledge of systems

Architectural drivers

Stakeholder requirements.Technological constraints.Data management policies.

Constraints

• Technological dependencies.
• Budget constraints.
• Resource availability.