In the process of implementing new business intelligence tools and methods, there are several phases to consider. This article will provide an overview of the phases involved in the Business Intelligence Life Cycle. BI is the process of collecting, analyzing, and presenting data from a variety of sources. Data sources include text documents, images, sounds, formatted tables, and even web searches. Data analysis, also called knowledge discovery, involves identifying and synthesizing useful knowledge from these collections.
There are many steps involved in the process of creating and maintaining BI systems. This includes incorporating the needs of business users during the design process. Next, data models, database objects, and data integration mappings are developed. Front-end semantic layers are also developed. BI helps organizations to make better decisions and respond quickly to changing market conditions. By using data-driven information to improve business processes, organizations can better understand customer behavior, evaluate competitors, and identify and address customer issues.
Throughout the Business Intelligence Life Cycle, key performance indicators and other relevant data are gathered and analyzed to answer business questions. These information can explain the current performance of marketing campaigns, complaints, and purchasing patterns. Relevant data can be in multiple forms, including customer feedback. Another significant aspect of the BI Life Cycle is data warehousing, which involves the collection, management, and storage of relevant data in database management systems.
Next, predictive reporting is essential. With predictive modeling, companies can predict future events, compare the impact of various decisions, and even predict what may happen in the future. This is a complex process, and requires a significant amount of work and concentration on the part of the executive team. Ultimately, predictive analysis is essential to the business’s success. All executive knowledge workers must have access to data, including the IT staff. This process is outlined below.
Prescriptive modeling is the most advanced stage of the Business Intelligence Life Cycle. It involves understanding the complex relationships and interactions between data and processes. It also involves re-forecasting and a feedback loop. The first three stages of the Business Intelligence Life Cycle include descriptive statistics. The purpose of these reports is to answer a simple question, “Why did that happen?”. The analytical stage focuses on the cause and effect of the events that are occurring. It also involves monitoring business activities.
The next stage involves the development of BI applications. These applications use the data that has been extracted from the DW/BI solution. The BI team should work closely with the business to identify BI application candidates. BI applications include navigation interfaces and parameter driven BI applications. During the development stage, the business and DW/BI team complete tasks related to configuring business metadata, tool infrastructure, and constructing and validating analytic and operational BI applications.
During this stage, KPIs are generated and presented. These are often presented visually through common distribution channels for rapid review by senior management. The BI solution must tell the whole story. Statistical analysis, model building, and predictive analytics are also common during this phase. This process is often referred to as the Business Intelligence Life Cycle. These four stages are essential to implementing BI tools. They are all critical to the success of an organization.