concept#Data#Analytics#Business Intelligence#Data Analysis
Online Analytical Processing (OLAP)
OLAP (Online Analytical Processing) is a technology that allows for fast complex queries on large datasets.
OLAP enables businesses to analyze and visualize data efficiently.
Maturity
Established
Cognitive loadMedium
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityAdvanced
Technical context
Integrations
DatabasesBusiness Intelligence ToolsETL Tools
Principles & goals
Data must be consistent and up-to-date.Analyses should be automated.Data security is of utmost priority.
Value stream stage
Build
Organizational level
Enterprise
Use cases & scenarios
Use cases
Scenarios
Compromises
Risks
- Lack of data quality can lead to incorrect results.
- High costs for infrastructure.
- Lack of user acceptance.
Best practices
- Perform regular data reviews.
- Ensure documentation of processes.
- Gather and integrate user feedback.
I/O & resources
Inputs
- Raw data from various sources
- Explicit requirements for analyses
- Data quality standards
Outputs
- Analytical Reports
- Visualized Data
- Decision Support
Description
OLAP enables businesses to analyze and visualize data efficiently. It supports decision-making through fast aggregation and multidimensional data analysis.
✔Benefits
- Enhanced analytical capabilities.
- Faster decision-making.
- Multidimensional data views.
✖Limitations
- High resource requirements.
- Complexity in implementation.
- Requires specialized knowledge.
Trade-offs
Metrics
- Average Query Time
The average time taken to execute a query.
- User Acceptance Rate
The percentage of users who regularly use the system.
- Data Processing Time
The time taken to process the data.
Examples & implementations
OLAP in a Financial Application
A financial service provider uses OLAP to analyze market trends.
Customer Analysis with OLAP
A retailer uses OLAP for customer segmentation.
Sales Forecasting with OLAP
A company uses OLAP for accurate sales forecasting.
Implementation steps
1
Define the analysis strategy.
2
Build the infrastructure.
3
Integrate and prepare the data.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated technologies.
- Lack of documentation.
- Insufficient maintenance systems.
Known bottlenecks
Resource BottleneckData Integration IssuesLack of User Acceptance
Misuse examples
- Ignoring data quality standards.
- Missing integration with downstream systems.
- Excessive queries that impact performance.
Typical traps
- Neglecting data preparation.
- Not considering user needs.
- Overcomplicating data analyses.
Required skills
Database ManagementAnalytical SkillsData Visualization Knowledge
Architectural drivers
Real-time data processingIntegration capability with other systemsUser-friendly interfaces
Constraints
- • Limitations on data sources
- • Regulatory requirements
- • Technological limitations