Business Intelligence & Data Warehouse
Business intelligence (BI) and data warehousing (DWH) or the development of analytical platforms are not one-off projects. They are a continuous process that must be firmly anchored in the corporate culture and harmonize seamlessly with other business processes. Only those who adhere to this principle will be successful with BI/DWH.
BI consists of a variety of analysis tools and methods, including business analytics, advanced analytics, data mining and self-service BI. It includes all techniques for analysis and reporting in the company, with the aim of answering business management questions – from standard reports in controlling to pattern recognition from weblogs in the area of customer journeys.
Data warehousing forms the basis for these analyses and includes data storage, data preparation and data quality management. An extended approach is the data lake, which collects structured and unstructured data and provides the basis for explorative analysis processes. Today, this integration is often implemented in a cost-optimized data lakehouse that combines classic data warehouse and data lake functions.
We are proficient in both classic data warehouse architectures and modern data lakehouse architectures.
Classic DWH/BI architecture
The relationship between BI and DWH can be compared to an iceberg: What is visible to business users is only 10-20 percent of the total effort. The larger part – the source connections, harmonization, layered data processing and topics such as data quality, compliance and master data management – remains invisible beneath the surface.
The self-service BI approach breaks down this principle and gives experienced business users more flexibility in connecting and linking any data sources. These tools make it possible to gain new insights and transfer them to a data warehouse if necessary. Despite this flexibility, the company’s information base should be available in the data warehouse as a “single source of truth” with quality assurance.
Automated ETL processes across all layers of a DWH enable business users to access prepared and structured information that is periodically comparable, structurally harmonized and professionally checked. This ranges from standardized KPIs to rule-based data mining in the DWH and data lake.
A methodically, professionally and technically correct process ensures the most important prerequisite for the acceptance and success of the BI/DWH system: reliable data and information.
We look forward to discussing with you the advantages of this modern and innovative methodology for your project and working together to ensure your success.