How Business Intelligence Tools Are Used with Data Warehouses

Introduction

Business intelligence refers to software, hardware, and practices applied in collecting data, processing it, and analyzing it in order to assist managers in informed decision-making (Kimball & Ross, 2013). A Data Warehouse is a composite storage area for data taken from several areas in the company.

How Business Intelligence Tools Are Used with Data Warehouses

Data warehousing is therefore a core module in business intelligence as it allows information to be stored in a common place, enabling fast access and is often analyzed and used for making informed decisions by managers and other corporate end users (Inmon, 2005). By integrating various data sources into a singular repository, a data warehouse supports complex queries and analytics, providing a robust foundation for business intelligence activities.

Best Practices for Designing a Data Warehouse for Business Intelligence

Sponsorship

Make sure you have a high-level business sponsor not only during the building but also one that remains present even after the data warehouse has been built and is presented ready for use. The business sponsor ought to be communicating with other business partners. Data owners and handlers are obliged to establish full ownership pertaining to the data therein.

Room for Growth

Data is continually going to be added; therefore, when designing it, one should consider leaving enough room for incremental addition of data. Additionally, you should also remember to retain the team of developers that assisted in building it for this purpose (Vaisman & Zimányi, 2014).

Expertise

Depending on the manner in which you want to design your required system, you should select experts experienced in that area to advise you on the best designing methods in addition to validating your resources and choice of technologies.

Minimized Scope

During the early steps in building, ensure you reduce the subject areas to be worked on to lower the investment in the initial build and assist in acquiring experience in a smaller area to be expanded later (Golfarelli & Rizzi, 2009).

Loading the Data Warehouse

Tools such as Utility Extract/Transformation/Load (ETL) software tools among others can be used to verify, for example, that the quality of data is maintained. The frequency with which to load data into the data warehouse is another element that should be considered.

Education and Support

One needs to avail the necessary information describing the new system, including the techniques and modeling tools together with its structure and method of how to access information from the data warehouse. This is to be used by corporate users of the system who may not be familiar with how it operates.

Future Modification

One ought to ensure the modification process of the data warehouse is controlled and documented in detail. This ensures that any changes made are traceable and can be evaluated for their impact on the overall system.

Steps for Implementing a Data Warehouse

Carrying Out a Feasibility Study

The costs and benefits of implementing a data warehouse are defined as well as the elements that contribute to the successful implementation of the system. One puts into consideration the advantages and disadvantages of implementing the required system. The results of the feasibility study are presented to the managers in the company to decide on the sponsors. Tasks of each of the developer team members involved in the implementation are also defined.

Business Line Analysis

This involves getting to familiarize with the organization for which the system is built, its operations, and business requirements. The data warehouse scope is defined at this juncture.

Design of the Data Warehouse Architecture

The physical and logical foundation of the system to be built, which includes the support architectures, data, and technical structures necessary for implementation, as well as the applications, are designed.

Selection of the Technological Solution

The best tools for use in implementing the data warehouse are chosen. The technical and business requirements for the implementation need to be put into consideration.

Project Iterations Planning

User and technical requirements for the new system are redefined, the restrictions brought about by the present systems are also noted and documented.

Data Warehouse Modelling

The database schema is achieved, metadata is defined, and the data source list is updated to include all the information necessary for the building and setting up of the new system.

Testing and Implementation

The developers test the system and later involve the users to use the system in an attempt to detect and eliminate any errors in the system.

Roll-out and Deployment

The system is then presented to the managers and other corporate users for use.

Data Warehouse Maintenance Techniques and Strategies

A data warehouse management system is used in the maintenance of the data warehouse. Maintenance techniques and strategies include the following:

View Synchronization

In this technique, any affected view definition is rewritten in the data warehouse whenever there is a schema change, thus making the current view definition undefined.

Incremental View Maintenance

This technique maintains the data warehouse extent every time a data update occurs within a source.

View Adaptation

This technique enables the adaptation of the view extent incrementally after the view definition has been changed either directly by the data warehouse designer or indirectly by the view synchronization system.

References

• Golfarelli, M., & Rizzi, S. (2009). Data Warehouse Design: Modern Principles and Methodologies. McGraw-Hill.
• Inmon, W. H. (2005). Building the Data Warehouse. Wiley.
• Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
• Vaisman, A., & Zimányi, E. (2014). Data Warehouse Systems: Design and Implementation. Springer.