Book Image

Hands-On Business Intelligence with Qlik Sense

By : Pablo Labbe, Clever Anjos, Kaushik Solanki, Jerry DiMaso
Book Image

Hands-On Business Intelligence with Qlik Sense

By: Pablo Labbe, Clever Anjos, Kaushik Solanki, Jerry DiMaso

Overview of this book

Qlik Sense allows you to explore simple-to-complex data to reveal hidden insights and data relationships to make business-driven decisions. Hands-On Business Intelligence with Qlik Sense begins by helping you get to grips with underlying Qlik concepts and gives you an overview of all Qlik Sense’s features. You will learn advanced modeling techniques and learn how to analyze the data loaded using a variety of visualization objects. You’ll also be trained on how to share apps through Qlik Sense Enterprise and Qlik Sense Cloud and how to perform aggregation with AGGR. As you progress through the chapters, you’ll explore the stories feature to create data-driven presentations and update an existing story. This book will guide you through the GeoAnalytics feature with the geo-mapping object and GeoAnalytics connector. Furthermore, you’ll learn about the self-service analytics features and perform data forecasting using advanced analytics. Lastly, you’ll deploy Qlik Sense apps for mobile and tablet. By the end of this book, you will be well-equipped to run successful business intelligence applications using Qlik Sense's functionality, data modeling techniques, and visualization best practices.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Qlik Sense and Business Intelligence
3
Section 2: Data Loading and Modeling
6
Section 3: Building an Analytical Application
11
Section 4: Additional Features

Data loading process

When using Qlik Sense (Cloud, Personal, or Enterprise) you can load data from several sources, including spreadsheets, text files, Microsoft Access bases, SQL databases, Big Data data lakes, REST APIs, and much more. We can load data without learning a script language, despite the fact that we can use a script to leverage our access to data, and improve our control over the process. We can apply transformations such as creating calculated fields based on pre-existing information or aggregate data. The final goal is to create a data model that can be used to create dashboards and provide information to the users to analyze information.

The data loading process (demonstrated in the following diagram) consists of gathering data from different sources, associating those sources (transformed in tables) by key fields, applying transformations (creating derived fields...