The Data Warehouse Lifecycle Model

What Exactly is A Data Warehouse?

It is a computer database that collects and stores information regarding a particular organization or group and is part of the Business Intelligence software. The main aim behind using a data warehouse is to manage the information available and also to analyze the existing data. There are various types of data warehouses but they all contain some common features - all of them are built for a particular subject and allow changes to be made in the stored information. The data stored in BI data warehouse software cannot be deleted but can be monitored and analyzed.

The Data Warehouse Lifecycle Model Today

BI Design:-

The best data warehouses work simultaneously with the available data of the organization as well as well as consider incomplete requirements of the analytical communities. Thus this makes data warehouse and design not a difficult process but it is somewhat complicated and is more like construction and not IT. Some of the main milestones here include user interviews, catalogues of the source, keeping details of the decision marking process and design tasks. Before beginning the design of a data warehouse, it is very important that the goals of the BI data warehouse are clearly noted down and made known to all everyone involved. Since the data warehouse is meant to serve its users it needs to be made to suit different types of users - analysts and the decision makers. A successful business intelligence data warehouse must provide accurate and consistent data, be able to give quick and right answers to various questions raised. All the data included must be detailed and summarized. Care should be taken so as to not deviate into many unique subjects but a single subject should be set as your objective.

BI Prototype:-

The primary objective of a prototype is to limit and in particular cases re - frame the user requirements by giving them detailed BI analysis reports of what they have asked for - thus fulfilling their demands. After two or more meetings between the organization and the users the gap between the actual needs and the needs as mentioned by the user's closes down and then the prototype will have to work to maintaining diplomatic relations between the two thereby getting the end users help in some cases where it is needed. The end users and stockholders will be more helpful if they know what's going on behind the curtains.

BI Deployment:-

The deployment of business intelligence solution involves a minimum of two deployments. A prototype has to be set in such a BI environment where it can be tested for its performance and at the same time undergo a series of stress tests. Documentation is the most important resource for a successful BI data warehouse and therefore clear and accurate documentation should be done of all the data available.

BI Operation:-

The daily maintenance of the data warehouse and its various services has to be maintained by a group of BI experts - this ensures that the data warehouse remains updated. DSS analysts can access the warehouse and can manage the ongoing process and also keep a tab on them. It is also very important to note that the warehouse has to be kept up to date with the latest data at all times for the business intelligence data warehouse to be a success.

BI Enhancement:-

Enhancement basically means enhancing all the available BI sources to give an over all better look to the complete BI tool. Some of the fields that can be enhanced are - Technology, operations, management and designs that need to change and adapt to the changing dynamics of business intelligence requirements. Moreover all the corners where some improvement can take place should be improved on a regular basis.