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

Data exploration, visualization, and discovery


Data visualization is not something that is done at the end of a long, costly Business Intelligence (BI) project. It is not the cute dashboard that we create to justify the investment in a new data warehouse and several Online Analytical Processing (OLAP) cubes. Data visualization is an integral part of a data exploration process that begins on the first day that we start extracting raw data.

The importance and effectiveness of using data visualization when we are exploring data is highlighted using Anscombe's quartet. Each of the following scatterplots analyzes the correlation between two variables. Correlation can also be explained numerically by means of R-squared. If we were to summarize the correlations of each of the following scatterplots using R-squared, we would discover that the number is be the same for each scatterplot, .816. It is only by visualizing the data in a two-dimensional space do we notice how different each correlation...