Những khái niệm này cũng được hỗ trợ trực tiếp của các kiến trúc thông tin Nguyên tắc mô tả trong chương 2, làm cho dữ liệu''có sẵn trong một định dạng có thể sử dụng cho các hoạt động hàng ngày và phân tích. . ''Chương này mô tả khái niệm chính và thực hành sau BI, cho phép sự sẵn có và khả năng sử dụng tối đa hoá giá trị dữ liệu của bạn | PartX Business Intelligence These concepts are also in direct support of the Information Architecture Principle described in Chapter 2 making data readily available in a usable format for daily operations and analysis. This chapter describes key concepts and practices behind the BI enabling the availability and usability that maximizes your data s value. Data Warehousing Data warehousing is a key concept behind both the structure and the discipline of a BI solution. While increasingly powerful tools provided in the latest release of SQL Server ease some of the requirements around warehousing it is still helpful to understand these concepts in considering your design. Star schema The data warehouse is the industry-standard approach to structuring a relational OLAP data store. It begins with the idea of dimensions and measures whereby a dimension is a categorization or group by in the data and the measure is the value being summarized. For example in net sales by quarter and division the measure is net sales and the dimensions are quarter time and division organization . Deciding which dimensions and measures to include in the warehouse should be based on the needs of the business bringing together an understanding of the types of questions that will be asked and the semantics of the data being warehoused. Interviews and details about existing reports and metrics can help gain a first approximation but for most organizations a pilot project is needed to fully define requirements. Best Practice Organizations that are not familiar with what BI solutions can deliver have a difficult time understanding the power they represent. Kick-start your effort by developing a simple prototype that demonstrates some of what is possible using data that everyone understands. Choose a small but relevant subset of data and implement a few dimensions and one or two measures to keep implementation time to a minimum. Business needs can then provide a basis for building the star schema .
