Book Image

QlikView: Advanced Data Visualization

By : Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover
Book Image

QlikView: Advanced Data Visualization

By: Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover

Overview of this book

QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: • QlikView for Developers by Miguel Ángel García, Barry Harmsen • Mastering QlikView by Stephen Redmond • Mastering QlikView Data Visualization by Karl Pover
Table of Contents (25 chapters)
QlikView: Advanced Data Visualization
Contributors
Preface
Index

Operations data model


Operations involve multiple discrete events that are represented as documents in the ERP system. For example, our customer selling cycle includes a sales quotation, a sales order, a customer delivery, a sales return, a sales invoice, and a sales credit memo. Our supplier purchasing cycle includes a purchase order, a delivery, a return, a purchase invoice, and a purchase credit memo.

Although we can create a transactional fact table that allows us to analyze each discrete event, we are interested in analyzing the relationship between the events more than the events themselves. For example, we want to know how much time it took to deliver a product after receiving its originating purchase order. It would also be insightful to compare the quantity that we delivered with the quantity of the originating purchase order. We would have to work between multiple rows in a transactional fact table to discover this information; and just like a row-based database, we would find it...