A complete guide to turning your data into many different chart types using QlikView. Starting with data analysis and progressing to visualization, it’s ideal for anyone who wants to convey information in a clear and graphic way.
Explore the basics of data discovery with QlikView
Perform rank, trend, multivariate, distribution, correlation, geographical, and what-if analysis
Deploy data visualization best practices for bar, line, scatterplot, heat map, tables, histogram, box plot, and geographical charts
Communicate and monitor data using a dashboard
While QlikView’s data engine complements our thought processes and gives us the ability to rapidly implement insightful data discovery, we must also learn to use proper analytical and data visualization techniques to enhance our ability to make data more presentable.
Learning QlikView Data Visualization presents a simple way to organize your QlikView data discovery process. Within the context of a real-world scenario and accompanying exercises, you will learn a set of analytical techniques and data visualization best practices that you can customize and apply to your own organization.
We start our data discovery project by reviewing the data, people, and tools involved. We then go on to use rank, trend, multivariate, distribution, correlation, geographical, and what-if analysis as we try to resolve the problems of QDataViz, Inc, a fictitious company used as an example. In each type of analysis, we employ highlighting, heat maps, and other techniques on top of multiple chart types. Once we have a possible solution, we present our case in a dashboard and use performance indicators to monitor future actions.
You will learn how to properly create insightful data visualization in QlikView that covers multiple analytical techniques. By reusing what you’ve learned in Learning QlikView Data Visualization, your organization’s future data discovery projects will be more effective.