The purpose of implementing a business intelligence project can either be stand-alone to solve a specific and acute information need in the enterprise, or to develop a comprehensive and coherent solution for management information, that:

1.supports the use of the corporate governance model, as this is described the organization' governance policy

2.forms an important part of the quantitative basis for economy and resource management at various levels within the organization, including the insurance of broader and more continuous access to relevant information (elimination of bottlenecks in the manual report production)

3.contributes to quality management, monitoring of performance indicators as well as following up on the goals set

4.creates a better basis for coordination of work tasks with the organization’ external partners, including making relevant information available to business partners in an efficient and secure manner

5.inspires to new opportunities for the combination of information and management of the organization

6.ensures cost-effective production of BI.

Interfaces to IT and other business projects etc.

The technical phases through the implementation of a business intelligence project often include:

1.collection of data from the relevant source systems

2.modeling and transforming data

3.analysis and processing of data , and

4.presentation of data in reporting solutions.

In organizations with high data complexity, these activities are, of course, considerably easier to implement when there is a comprehensive data model and IT-architecture framework present in the organization.

These technical activities will also have to be coordinated with any other strategic IT or organizational projects undertaken in the organization. For example:

Information structuring

The organization' information infrastructure and Information Management forms the basis for coherent and consistent business intelligence.

An overall structure for the description of the organization' information and data in a unified data model provides the optimal basis for business intelligence in organizations with high data complexity.

These descriptions are metadata – data about data – that amongst other things describes how and when the key figures are calculated from what sources data comes as well as who owns the data.

Included in an optimal information structure are also

structured documentation of processes, workflow and -rules. Such evidence is an important part of the source for business intelligence.

Information Management (IM)

Effective management of the organization’ knowledge is of strategic importance in many lines of business. Knowledge management must provide the basis for updated and qualified knowledge being made available in structured form when and where it is needed.

Knowledge management often means that the organization have established formal policies and guidelines for knowledge management, data structuring, data access and data ownership amongst the organization’ organizational units.

Information management and governance thus covers both qualitative and quantitative information, where in particular the latter has heavy influence for business intelligence-projects.

ERP

The organization’ ERP solution(s) (Enterprise Resource Planning system) often contain detailed knowledge about the organization' resources and processes – mainly translated into economic measurements.

Corporate ERP solutions, however, is very different in nature from those that consist of 1 "all inclusive" ERP system, to being composed of one or more financial applications and one or more production systems.

ERM

Corporate Electronic Record Management (called ESDH in Danish and including Document and Case Management) solutions should support most of the organization’ business processes and communication with its business partners – including clients and customers.

Source data that is generated in the organization’ ERM system(s), may, with the source data from the organization’ other production systems be utilized in the business intelligence production.

With the correct definition of metadata and with documented and supported workflows it will, amongst other things, be possible to not only to count the production but also to break down the production processes and production flows as well as to identify the cost factors such as spent time and skills, etc. contributing to the activities.

Often — but not always – the organization’ ERM solutions are supported by a more or less standardized data warehouse-structure.

The business intelligence challenge is therefore often to consolidate and maintain these data warehouses.

Production- and administrative systems

Often — but not always – the organization’ production and administrative systems are supported by a more or less standardized data warehouse-structure.

The business intelligence challenge is therefore often to consolidate and maintain also these data warehouses as well.