The functionalities of Visual Analytics and Self-Service BI make it possible to prepare even complex data in an understandable and appealing way through visualizations, thus enabling users to gain knowledge quickly. However, visualization is usually only the “tip of the iceberg” of the underlying data warehousing.

For value-adding analyses and evaluations, data from a wide variety of sources often has to be linked and combined in order to obtain a complete picture with which the drivers of business success can be specifically identified. Possible data sources include:

• Enterprise Information Systems (EIS)
• Enterprise Resource Planning Systems (ERP)
• Customer Relationship Management Systems (CRM)
• Marketing Software Systems

but standard files such as Excel, CSV or PDF can also be linked.

Two central aspects are defined within the framework of Data Warehousing:

• Data Modeling: Definition of data structures according to the analysis requirements derived from business processes (e.g. according to which dimensions and filter options should data be analyzed later)
• Data Integration: Efficient implementation of ETL process to efficiently connect the various data sources:
  • Extraction (E): Extraction and import of data from the various source systems
  • Transformation (T): Transformation of the source data into the desired format (according to the data model) including the necessary cleansing, assignments, combinations and calculations
  • Load (L): Storage of data in the target database in the format of the defined data model

Experience often shows that users can only create individual analyses and reports with a lot of manual effort. The necessary data has to be compiled from different systems and by different contact persons in a time-consuming process and then reformatted, cleansed and consolidated in Excel. Only after this effort can the actual analysis and evaluation of the data begin. This manual process is repeated on a monthly, weekly or even daily basis. In addition to the general dissatisfaction of employees with this manual and error-prone process, the associated resource expenditure is extremely inefficient. This effort often leaves employees too little time to gain knowledge and interpret results before the submission deadline or until the process is restarted.

Against the above background, the objective of data warehousing is to map the process from data connection to processing to the provision of the finished data set as efficiently as possible through automation.

In this way, free capacities for the interpretation of results and data-based decision making are created, which typically materialize in significant performance increases.

In order to successfully implement Data Warehousing in the long term, a joint analysis of the initial situation is an absolute prerequisite. Depending on the status quo in terms of skills, data basis and technology, we then define both the target image and the starting point for an independent data analysis. According to your requirements, we guide and accompany you at the desired speed on your way to becoming a data-driven company.

We are happy to start with an initial workshop in which we discuss a specific application situation from your company together and devise a possible solution approach as a Minimum Viable Product (MVP) or prototype. Based on the jointly-developed objectives and the associated requirements, our team of experts can then work out the MVP in an agile development process. The MVP can then be individually extended step-by-step with additional data and functionalities. We act proactively with integrity and precision at all times. The technologies we use can be flexibly expanded and offer sufficient scalability in the long term.

Feel free to contact us and our team of experts will help you find the right solution for you to generate real added value from your data.