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 teams and roles


The exact composition of the teams whose principal job is to enable their coworkers to make data-driven decisions will vary as a business's entire data strategy matures. However, many misinterpret what it means to run a mature data-driven business. They believe that at some point all data will and should be governed, and that the team that develops the first QlikView data exploration and discovery projects with will be that governing body.

While a mature data-driven business does count with a large set of governed data and a talented data governance team, it should never be without new, unknown datasets, or without ideas about how to exploit existing datasets in new ways. It is also unrealistic that the same team enforce conformity at the same time that they must strive to innovate. It is for that reason that every mature data-driven business should have both a data research and development (R&D) team, and a data governance team. Each team will have a different data...