Analytical Data Modeling
Analytical data modeling is the process of designing data structures to support business processes and enable informed decision-making.
Classification
- ComplexityMedium
- Impact areaBusiness
- Decision typeDesign
- Organizational maturityAdvanced
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Insufficient data leads to incorrect analyses
- Data integrity is often compromised
- Complexity can lead to misunderstandings
- Conduct regular data reviews
- Implement feedback loops
- Continuously maintain documentation
I/O & resources
- Access to relevant data sources
- User feedback
- Technical know-how
- Comprehensive analysis reports
- Actionable recommendations
- Strategic decision foundations
Description
This approach focuses on developing data models optimized for analyzing business data. These models help organizations make long-term strategic decisions and enhance operational efficiency.
✔Benefits
- Improved data-driven decisions
- Efficiency gains through analyses
- Better brand loyalty
✖Limitations
- Data dependence can lead to delays
- Complexity can complicate implementation
- High documentation required
Trade-offs
Metrics
- Data Analysis Speed
The speed at which data can be analyzed.
- User Satisfaction Rate
The rate of user satisfaction with the provided analyses.
- Cost-Benefit Ratio
The ratio between the costs and the benefits obtained from the data analysis.
Examples & implementations
Data Model for an E-Commerce Platform
A comprehensive data model covering all aspects of the e-commerce business, from customer behavior to sales analyses.
Financial Reporting in the Financial Services Sector
An example of an analytical model used for creating financial reports in banks.
Analytics Framework for a Marketing Team
A structured framework for analyzing marketing initiatives that identifies optimization potentials.
Implementation steps
Identify core processes
Develop simple models
Make iterative improvements
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated databases
- Lack of automation
- Insufficient infrastructure for data analyses
Known bottlenecks
Misuse examples
- Excessive reliance on Excel data
- Neglecting data quality
- Lacking security audits
Typical traps
- Failing to update data regularly
- Insufficient communication between teams
- Neglecting data protection policies
Required skills
Architectural drivers
Constraints
- • Data classification adjustments
- • Regulatory compliance
- • Technological dependencies