"Data Warehouse is a physical and a structured data that enables an integrated view of the underlying data sources." Its value comes from the associated processes of administration, data management and evaluation of data - the Data Warehousing
The approach to separate operational and dispositive data goes back to 1980 years. The aim was to save consistent and aggregated data for decision support in analysis, monitoring and control tasks in an independent database (Data Warehouse). The need to make (selected) decisions in shorten time intervals, leads to get (parts) of data from operational sources (near real-time) into the warehouse. Furthermore, decision models are increasingly being used to trigger the control tasks by defined actions automatically. These overcome of manual decision making and tripping is called Active Data Warehouse. The benefit of an active data warehouse depends on the art to adapt the architecture and processes based on the requirements of the company:
- Continuous improvement of data quality and resolution of errors in the sources
- Transparency and actuality of the data flows (from all sources) in the Data Warehouse
- Clear and transparent responsibility for data and interfaces in the Data Warehouse
- Clear and unambiguous separation of productive and non-productive data
- Continuous adjustments to the data warehouse to new requirements in terms of analysis, monitoring and control tasks
- Continuous review of decision models (actuality and validity) in the Data Warehosue
- Continuous capacity adjustments and management of "hot spots" in the data
- Adequate solutions for compliance with data protection requirements
- Data Warehouse as an integral part of an IT strategy
- Suitable software / appliances towards requirements and service expectations of the / data warehouse provider
- Usage of known and established administrative procedures of the data warehouse (structuring provides ITIL - IT Infrastructure Library)
- Non-database based storage formats, such as Hadoop ecosystem becomes more important; the Data Warehouse was not designed for unstructured or semi-structured data.
Our Offerings:
We support companies in the introduction, adaptation and implementation of their Data Warehouse.
Procedure examples:
- Requirements analysis, software selection with solution design, build infrastructure, create data model (s), data management (ELT / ETL), reporting, and access control, going live
- Analysis of existing data warehouse solutions in terms of suitability for new requirements such as data update frequency, volume of data, data quality, transparency of data flows, integration of analytic functions, performance, dependencies other infrastructure components (e.g. Reporting, Data Management) and costs, preparing position papers for the expansion or transformation of a Data Warehouse
- Analysis of Data Warehouse requirements and categorizations of service requests based on "make or buy", collection of cost savings and developing a sourcing strategy
- Transformation of a Data Warehouse platform
- Transition to a Data Warehouse platform
How can we help you? Depending on the situation in the form of consultancy, interim management, executive coaching and training.
Project examples:
• Consulting: Introduction of a new Data Warehouse platform and introduction of a central data model and central Data Warehouse processes
• Interim management: Project management for transforming a data warehouse platform, including data management
• Interim management: Project management for the extensions of an existing platform to value-based inventory information for reporting, valuation and analysis
• Interim management: Project management for the construction of new data warehouse solutions
• Interim Management: Software selection for a Data Warehouse storage with Proof of Concept