Here's how you can effectively build a data model for a business intelligence solution.

In vielen Projekten stelle ich fest das gleiche Kennzahlen in verschiedenen Abteilungen unterschiedlich definiert oder berechnet werden. Dies führt häufig zu Fehlern im gesamten Business Intelligence Prozess. Ziel sollte es sein alle Abteilungsübergreifenden Kennzahlen zentral in einem KPI-Katalog zu definieren und zu dokumentieren. Die hier erarbeiteten Kennzahlen sollen regelmäßig auf Grundlage neuer Kenntnisse neu bewertet werden.

Building a BI Model: Needs First Before the building begins, understand the blueprint: Stakeholder Engagement: Uncover reporting needs, KPIs, and decision-making requirements. Asking the Right Questions: Ensure the model addresses real business problems. Analysis in Mind: Consider the types of analyses and data granularity needed. Business Goal Alignment: The closer the model aligns, the greater the value it delivers. Think of your data model as a house. Understanding business needs is like laying the foundation. By gathering clear requirements from stakeholders, you ensure the model is built to support their specific needs and contributes directly to achieving business goals.

BI Model: Feeding the Beast with Data The right data fuels powerful insights. Identify your sources: Internal Systems: CRM, ERP, and other internal applications. External Powerhouses: Market research, social media analytics, and more. Data Quality Matters: Assess reliability, volume, and update frequency. Think of your data model as a car. Data sources are the fuel that keeps it running. By identifying and evaluating different sources, you ensure you're using high-quality data, which translates to accurate insights that drive better decision-making. A comprehensive understanding of your data landscape will help you design a data model that efficiently integrates and utilizes all this information.

BI Model: Building the Blueprint (Schema Design) Now that you know the needs and data sources, design the blueprint: Data Organization Framework: The schema defines how data is structured within the database. Star Schema Shines: Considered a popular choice for BI due to its: Simplicity for users Fast query performance Fact & Dimension Tables: Connects central fact tables with detailed dimension tables. Schema Selection: Guided by the types of queries and desired performance. By choosing the right schema design (like the star schema) and considering the types of analysis you'll perform, you ensure your data model is efficient, user-friendly, and delivers insights quickly.

Bei der Entwicklung von Reporting werden der Phase der Entwicklung eines Design-Schemas häufig zu wenig Zeit eingeräumt. Das daraus resultierende Ergebnis ist maßgeblich für die Performance und das Nutzererlebnis der zurückzuführen. Daher rate hier hier ein breites Wissen im Datenmanagement aufzubauen und zu verstehen wie Daten bestmöglich zusammen agieren können.

BI Model: The Power of Relationships Relationships connect your data model's elements, ensuring data integrity and accurate queries. - Cardinality: Defines how unique data is across tables (one-to-one, one-to-many, etc.). - Referential Integrity: Keeps data consistent by preventing "orphaned" records. Proper relationships are the foundation for efficient data retrieval and a scalable BI solution. Think of them as bridges between data islands, enabling seamless information flow for powerful insights.

BI Model: Keeping it Speedy (Performance Optimization) A fast BI system is a happy user! Optimize your model for speedy data retrieval: Indexed Columns: Like road signs, they direct queries to the right data quickly. Partitioning Power: Manage large tables efficiently by dividing them into subsets. Materialized Views: Pre-calculated query results for faster access to frequently used data. BI Tool's Cache: Utilize caching mechanisms to store frequently accessed data. Incremental Processing: Process only new or changed data for faster updates. Remember, optimization is an ongoing journey. As your data volume grows or query patterns evolve, you may need to fine-tune your model to maintain peak performance.

Häufig wird die Leistungsoptimierung als nachgelagerter Prozess angesehen. Leider findet dieser viel zu selten statt. Ich versuche schon von ETL Prozess bis zum fertigen Report mich extrem auf eine Leistungsoptimierte Entwicklung zu konzentrieren. In der Regel dauert die Entwicklung eines kompletten Datenmodells und der jeweiligen Reports so nicht wesentlich länger.