Objectives:

The concept of Data Warehouse dates back to the 1980s when technology enabled organisations to integrate data from a variety of sources into a common data model. The integrated data could provide information on operating processes and create new possibilities for exploiting data to make decisions and create organisational value. Furthermore, Data Warehouses were seen as a means to reduce the proliferatons of decision support systems (DSS), most of which were based on the same basic business data. The concept of “enterprise warehouse” offered a way to reduce data redundancy, improve information consistency and enable a company to use its data to make better decisions.

Activities carried out by our Team:

Understand the requirements

The development of a Data Warehouse is different form the development of an operational system. Operational systems depend on specific and precise requirements. Data Warehouses collect data that will be used in different ways. Furthermore, usage will evolve over time as users analyse and explore the data.

Defining and maintaining the DW/BI architecture

The DW/BI architecture should describe where data comes from, where, when, why and how it goes into a warehouse. The ‘how’ includes the hardware and software details and the organisational framework to bring all activities together. The technical requirements should include performance, availability and time requirements.

Developing Data Warehouse and Data Marts

Typically, DW/BI projects have three simultaneous development paths:

• Data: The data needed to support the analysis that the company wants to do. This track includes identifying the best sources for the data and designing rules on how the data is corrected, transformed, integrated, stored and made available for use by applications. This phase also includes deciding how to handle data that does not meet expectations.
• Technology: The back-end systems and processes that support the storage and movement of data. Integration with the existing enterprise is crucial, as the warehouse is not an island unto itself. Enterprise architectures, particularly technology and application specialities, usually manage this path.
• Business Intelligence tools: The suite of applications needed by data consumers to obtain meaningful information from distributed data products.

Populating the Data Warehouse

The most important part of the work in any DW/BI project is the preparation and processing of data. Design decisions and principles for the detail of the data contained in the DW are a key priority for the DW/BI architecture. The publication of clear rules on which data will only be available through operational reports (as in the case of non-DW data) is critical to the success of DW /BI efforts.

Implementing the Business Intelligence Portfolio

Implementing the BI Portfolio means identifying the right tools for the right user communities within or across business units. Finding similarities by aligning common business processes, performance analysis, management styles and requirements.

Keeping data products

An implemented Warehouse and its BI tools for the client is to be considered a data product. Improvements (extensions, expansions or modifications) of an existing DW platform should be implemented incrementally. Keeping the purpose of an expansion can be a challenge in a dynamic work environment. Establishing priorities with business partner and focusing the work on mandatory enhancements.