Book Image

Microsoft Tabular Modeling Cookbook

By : Paul te Braak
Book Image

Microsoft Tabular Modeling Cookbook

By: Paul te Braak

Overview of this book

Business Intelligence Semantic Models (BISM) is a technology that is designed to deliver analytical information to users through a variety of mechanisms that include model structure, definition, and design. This book demonstrates how to create BISM models so that information can be presented to users in an intuitive and easy-to-use format. Once the model is defined, we also show you how it can be managed and maintained so that the data in it remains current and secure. Microsoft Tabular Modeling Cookbook is an all-encompassing guide to developing, managing, creating, and using analytical models using the Business Intelligence Semantic Model (BISM). This title covers a range of modeling situations and common data analysis related problems to show you the techniques required to turn data into information using tabular modeling. Microsoft Tabular Modeling Cookbook examines three areas of tabular modeling: model development, model management and maintenance, and reporting. This book is a practical guide on how to develop semantic models and turn business data into information. It covers all phases of the model lifecycle from creation to administration and finally reporting. It also shows you how to create models which are designed to analyze data. All sections of BISM modeling from development to management and finally reporting are covered. The sections on development examine a wide range of techniques and tricks required to build models, including moving data into the model, structuring the model to manipulate the data, and finally the formulas required to answer common business questions; all of these are discussed in this book in detail. Finally, the book examines methods of reporting on the data within the model, including the creation of data-driven workbooks and reports for a powerful end user experience.
Table of Contents (18 chapters)
Microsoft Tabular Modeling Cookbook
About the Author
About the Reviewers

Moving averages and last n averages

Moving averages are often used in analysis for two purposes.

Firstly, they are used to remove volatility from single point values. By including a number of prior observations, a smoother estimation of the volatile point is defined.

Secondly, they remove volatility and can provide the general (and expected) trend movement (just as any consecutive set of numbers are an indicator of trend). Since a daily value may include prior N periods, the value of N is often used to support long-term or short-term trends. For example, a trend based on 30 days may be considered a long-term trend, whereas a trend based on five days may be used to provide a short-term trend. This type of smoothing, and a mix of long- and short-term trends is often used in charting analysis for stock prices.

This recipe shows how to perform last N calculations over stock data. The data is from the Australian Stock Exchange between January 1993 and December 2006. There are approximately 2.5 million...