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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Take your QlikView skills to the next level and master the art of creating visual data analysis for real business needs
About This Book
- Explore how to create your own QlikView data laboratory and how to develop QlikView applications using agile project methods
- Implement advanced data visualization and analysis for common business requirements from the sales, finance, marketing, inventory, operations, and human resources departments
- Learn from real-life experience shared in this book that will give you the upper hand in your next QlikView project
Who This Book Is For
This book is intended for developers who want to go beyond their technical knowledge of QlikView and understand how to create analysis and data visualizations that solve real business needs. You should have a basic understanding of advanced QlikView functions.
What You Will Learn
- Apply advanced QlikView techniques such as set analysis and nested aggregation in order to deliver common business requirements
- Understand real business requirements for sales, finance, marketing, and human resources departments
- Discover when to apply more advanced data visualization such as frequency polygons, bullet graphs, and XmR charts
- Go beyond native QlikView and include geographical analysis, planning, and sentiment analysis in your QlikView application
- Troubleshoot common errors we discover at the moment we visualize data in QlikView
- Develop a plan to master Qlik Sense data visualization
In Detail
Just because you know how to swing a hammer doesn't mean you know how to build a house. Now that you've learned how to use QlikView, it's time to learn how to develop meaningful QlikView applications that deliver what your business users need.
You will explore the requirements and the data from several business departments in order to deliver the most amazing analysis and data visualizations. In doing so, you will practice using advanced QlikView functions, chart object property options, and extensions to solve real-world challenges.
Style and approach
This hands-on guide follows the story of a company implementing QlikView as its enterprise data discovery solution. Each chapter starts with an understanding of the business requirements and the data model, and then helps you create insightful analysis and data visualizations. Each chapter expands on what was done in the previous chapter as we follow this continuously improving iterative process.
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Seitenzahl: 319
Veröffentlichungsjahr: 2016
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Table of Contents
Mastering QlikView Data Visualization
Mastering QlikView Data Visualization
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: April 2016
Production reference: 1200416
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-78217-325-0
www.packtpub.com
Credits
Author
Karl Pover
Reviewers
Ralf Becher
Miguel Ángel García
Michael Tarallo
Commissioning Editor
Kartikey Pandey
Acquisition Editor
Tushar Gupta
Content Development Editor
Rohit Singh
Technical Editor
Siddhesh Patil
Copy Editor
Priyanka Ravi
Project Coordinator
Izzat Contractor
Proofreader
Safis Editing
Indexer
Monica Ajmera Mehta
Graphics
Kirk D'Penha
Disha Haria
Production Coordinator
Conidon Miranda
Cover Work
Conidon Miranda
About the Author
Karl Pover is the owner and principal consultant of Evolution Consulting, which provides QlikView consulting services throughout Mexico. Since 2006, he has been dedicated to providing QlikView presales, implementation, and training for more than 50 customers. He is the author of Learning QlikView Data Visualization, and he has also been a Qlik Luminary since 2014. You can follow Karl on Twitter (@karlpover) or on LinkedIn (https://mx.linkedin.com/in/karlpover). He also blogs at http://poverconsulting.com/.
First and foremost, I would like to thank my wife, Pamela. I owe you several long weekends.
Thanks to the team at Evolution Consulting, especially Julian Villafuerte, Carlos Reyes, and Jaime Aguilar, for taking on more responsibility. A special thanks to Julian for taking the time to review the final version of this book, and Alejandro Morales for helping me develop a few extensions.
As always, thanks to my parents, Judy and Bill, for their love and support throughout my life.
I am grateful to all the technical reviewers, and especially Ralf Becher, who contributed material to this book. I also appreciate the work done by Rohit Kumar Singh and the rest of the Packt team, who gave me a little extra time to make this a great book.
Last, but not least, thanks to all the customers, past and present, who have always asked for the impossible.
About the Reviewers
Ralf Becher has worked as an IT system architect and as an IT consultant since 1989 in the areas of banking, insurance, logistics, automotive, and retail. He founded TIQ Solutions in 2004 with partners. Based in Leipzig, his company specializes in modern, quality-assured data management. Since 2004, his company has been helping its customers process, evaluate, and maintain the quality of company data, helping them introduce, implement, and improve complex solutions in the fields of data architecture, data integration, data migration, master data management, metadata management, data warehousing, and business intelligence.
Ralf is an internationally-recognized Qlik expert with a strong position in the Qlik community. He started working with QlikView in 2006, and he has contributed to QlikView and Qlik Sense extensions. He has also contributed add-on solutions for data quality and data integration, especially for connectivity in the Java and Big Data realm. He runs his blog at http://irregular.bi/.
Miguel Ángel García is a business intelligence consultant and QlikView solutions architect. Having worked through many successful QlikView implementations from inception to implementation and performed across a wide variety of roles on each project, his experience and skills range from presales to application development and design, technical architecture, and system administration, as well as functional analysis and overall project execution.
Miguel is the coauthor of the book QlikView 11 for Developers, published in November 2012, and its corresponding translation to Spanish, QlikView 11 para Desarrolladores, published in December 2013. He has also participated as a technical reviewer in several other QlikView books.
Miguel runs a QlikView consultancy, AfterSync (http://aftersync.com/), through which he helps customers discover the power of the Qlik platform. He currently has the QlikView Designer, QlikView Developer, and QlikView System Administrator certifications, issued by Qlik, for versions 9, 10, and 11.
Michael Tarallo is a senior product marketing manager at Qlik. He has more than 17 years of experience in the Data Integration and Business Intelligence space from both open source and proprietary BI companies. Currently at Qlik, he is responsible for a broad spectrum of Marketing and Sales enablement activities for QlikView and Qlik Sense. He is best known for working with the Qlik Community and providing its members with valuable information to get them started with Qlik Sense, which includes the creation of high-quality video content. He has produced numerous videos ranging from promotional to instructional. Prior to Qlik, Mike worked for UPS, Information Builders, Pentaho, and Expressor. His career has spanned from data analysis, customer support, and account management to a solution architect and leader, crafting customer solutions, and painting visions of the "art of the possible" with the companies' software. He humbly admits that he is "a confident jack of all trades but a master of many."
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Preface
This may be a horrible way to start a book, but in all honesty my first real-world QlikView experience was a failure. I was assigned to do a proof-of-concept with a prospective client's IT department, and they insisted that I share every mouse click and keystroke on a large projection screen with them. I had taken a QlikView designer and developer course and was developing a QlikView template in my spare time, but this hadn't prepared me for the live development of a real application.
I fumbled around the screen as I developed their first data model and charts. They must have doubted my competence, and I was embarrassed. However, I was surprised to hear that they were impressed with how little time it had taken me to convert raw data to interactive data visualization and analysis. I had created the required indicators and finished their first application within three days.
The goal of the proof-of-concept was to demonstrate the value that QlikView could provide to the prospective client's company, and it all seemed to have gone well. After all, I had created an attractive, functional QlikView application that was filled with the indicators that the IT department had requested. However, I failed to demonstrate QlikView's value directly to the business users; in the end, the prospective client never purchased QlikView.
All was not lost because I ultimately learned that, although it is important to understand all of QlikView's technical features, we can't display its value by only memorizing the reference manual. If we really want to master QlikView, we have to go beyond the technical functionality and learn what business value QlikView enables us to deliver. Moreover, we must bring about a data discovery initiative that changes a company's culture.
This first experience occurred ten years ago and these first failures have given way to success. I am lucky to have the opportunity to work as a QlikView consultant and participate in projects that encompass multiple organizations and various functional areas. All of their difficult challenges and excellent ideas have helped me to constantly learn from our mutual successes and failures.
During the last ten years that I've implemented QlikView projects, I've found that many businesses share much of the same advanced data analysis goals. For example, most sales departments in every company dream about having an easy way to visualize and predict customer churn. We will go over these common, but complicated, business requirements that you can apply to your own company.
As a QlikView master, you have to be just as comfortable discussing the most appropriate performance indicator with a business user, as you are with scripting out a data model that calculates it. For this reason, at one end, we will explain the business reasons for a particular visualization or analysis and, at the other end, we will explain the data model that is necessary to create it.
We will then develop different types of data visualization and analysis that look to push the boundaries of what is possible in QlikView. We will not focus on QlikView syntax or function definitions. Instead, we will see how to apply advanced functions and set analysis to real business problems. Our focus on the business problem will also lead us to look beyond QlikView and see what other tools we can integrate with it.
Practice leads to mastery, so I've included sample data models and exercises throughout this book. If they apply to your business, I recommend that you copy and paste these exercises over your own data to see what feedback you get from your business users. This extra step of adjusting the exercise's code to make it work with a different dataset will confirm your understanding of the concept and cement it in your memory.
Ultimately, I hope that, by sharing my experience, I will help you succeed where I first failed. In doing so, when you finally fail, it will be because you are attempting to do something beyond what I have done. Then, when you finally overcome your failure and succeed, I can learn from you, the master.
What this book covers
Chapter 1, Data Visualization Strategy, begins our journey to create a data-driven organization using QlikView.
Chapter 2, Sales Perspective, explains the data model's importance to data visualization, and shows us how to create advanced analyses, such as customer stratification, churn prediction, and seasonal trends.
Chapter 3, Financial Perspective, illustrates the usage of metadata to format an income statement, a balance sheet, and a cash flow statement.
Chapter 4, Marketing Perspective, walks us through various types of visualization that reveal customer profiles, potential markets, social media sentiment, and the sales pipeline.
Chapter 5, Working Capital Perspective, describes how to analyze days sales of inventory, days sales outstanding, and days payable outstanding, at both a high and a detailed level. It also explains how they are important in order to determine customer stratification.
Chapter 6, Operations Perspective, shows us how to analyze our service levels, predict supplier lead times, and investigate whether on-time deliveries depend on the supplier.
Chapter 7, Human Resources, reveals how to visualize personnel productivity and personal behavior analysis.
Chapter 8, Fact Sheets, demonstrates an ad hoc design method to create a customer fact sheet that includes bullet graphs, sparklines, and a customized UX.
Chapter 9, Balanced Scorecard, details a more formal design method to build an information dashboard containing balanced scorecard metrics.
Chapter 10, Troubleshooting Analysis, takes a look at resources and methods to debug problems in our QlikView applications.
Chapter 11, Mastering Qlik Sense Data Visualization, explains what Qlik Sense means to a QlikView developer and proposes a plan to master Qlik Sense data visualization.
What you need for this book
To complete the exercises in this book, you will need to download and install QlikView Desktop from Qlik (http://www.qlik.com) and the exercise files from the Packt website (https://www.packtpub.com/).
Who this book is for
This book is for those who have some QlikView experience and want to take their skills to the next level. If you are just beginning with QlikView, please read QlikView 11 for Developers, by Miguel Garcia and Barry Harmsen, before reading this book.
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Chapter 1. Data Visualization Strategy
What is the difference between graphic design and data visualization? What distinguishes our actions when we design a website from when we design an executive dashboard? What separates somebody who creates a meaningful icon from another who creates an insightful bar chart?
While both graphic design and data visualization aim to create effective visual communication, data visualization is principally concerned with data analysis. Even though we, who design dashboards and charts, are motivated to create something aesthetically pleasing, we are more passionate about what the data can tell us about our world. This desire to explore our universe via data, and then, communicate our discoveries is the reason that we dedicate our time to learning how best to visualize it.
In this is book, our mission is to create a data-driven business. We start our journey by defining a series of strategies to create and share knowledge using data visualization. In parallel, we propose how we can effectively organize ourselves, our projects, and the applications we develop so that our whole business starts to use insightful visual analysis as quickly as possible. Also, as we survey the entire perspective of our data visualization strategy, we review how we are going to implement it using, arguably, the best data exploration and discovery tool—QlikView.
Let's take a look at the following topics in this chapter:
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 behaves:
Some tools make it cumbersome to visualize data as soon as it is extracted. Most traditional BI solutions have separate tools for each phase of their implementation process. They have one tool that extracts data, another that creates the OLAP cubes, and yet another that constructs visualizations.
QlikView is a tool that allows us to extract, transform, model, and visualize data within the same tool. Since we can visualize data from the moment it is extracted and throughout the rest of the extraction, transformation, and load (ETL) process, we are more likely to discover data anomalies at an earlier stage in the development process. We can also share our discoveries more quickly with business users, and they in turn can give us important feedback before we invest too much time developing analytical applications that don't provide them with real value. Although QlikView is considered a BI software, it stands out amongst its peers due to its extraordinary ability to explore, visualize, and discover data.
In contrast, the implementation of a traditional BI tool first focuses on organizing data into data warehouses and cubes that are based on business requirements created at the beginning of the project. Once we organize the data and distribute the first reports defined by the business requirements, we start, for the first time, to explore the data using data visualization. However, the first time business users see their new reports, the most important discovery that they make is that we've spent a great amount of time and resources developing something that doesn't fulfill their real requirements.
We can blame the business user or the business requirements process for this failure, but nobody can exactly know what they need if they have nothing tangible to start from. In a data discovery tool like QlikView, we can easily create prototypes, or what we later explain as Minimally Viable Products (MVPs), to allow business users to visualize the data within a matter of days. They use the MVP to better describe their needs, discover data inadequacies, and among other things, confirm the business value of the analysis with their executive sponsors. Only after making and sharing these first discoveries do we invest more of our resources into organizing an iteratively more mature data analysis and visualization.
Note
Data Visualization Strategy 1: Use data visualization as an integral part of data exploration and discovery from the very beginning, and all throughout our project.
We've established a general data visualization strategy to support our data exploration and discovery. Now, let's review the strategies that we assign to the teams who are tasked with not only exploring the data directly, but also making sure everyone else in the business can perform their own data exploration.
Tip
I often come across customers who have data quality issues. They often battle with whether to hold off investing in QlikView until they've cleaned the data or invest in QlikView regardless of the poor data quality. Those who implement QlikView over poor-quality data data quality and make the problem transparent tend to clean their data more quickly and more effectively.
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 visualization strategy.
Data research and development
The data R&D team is constantly investigating and creating new solutions to our business problems. When we implement our first data exploration and discovery projects using QlikView, it is common to find out that we are part of a cross-functional, investigative, and proactive team. This team can be the keystone of a more formal data R&D team.
At a minimum, the team should consist of data engineers, data visualization designers, and data entrepreneurs. Data scientists and data visualization programmers may be optional in the beginning, but they become important elements to add as we continue to revolutionize how our business uses data.
It is worth repeating that even though this team will start the data exploration and discovery process, it will not evolve into the data governance team. Instead, this team will continue to look for ever more innovative ways to create business value from data. Once the team develops a stable solution with a long life expectancy, they will migrate that solution and transfer their knowledge to the data governance team.
Our data R&D teams will range in size and capacity, but in general, we aim to cover the following roles within a team that uses QlikView as its primary data exploration tool.
Note
The list of roles is not all-inclusive, and our business may have particular necessities or other tools for which we need to add other roles.
As far as data visualization is concerned, every member of the data R&D team uses it to make sense of the data and communicate their discoveries with their peers. As such, they should be given space to experiment with advanced data visualization techniques, even when those techniques may appear obscure, or even esoteric. For example, the following scatterplot matrix may not be suitable for most business users, but may help a data scientist create a predictive model:
Note
Data Visualization Strategy 2: Encourage the data R&D team to experiment with new data visualization techniques.
When the data R&D team creates a stable, long-term analytical solution that is going to be used by business users to make their own discoveries, then they should migrate that solution to the data governance team. At this point, both teams should work together to make the data visualization as clear and simple as possible for the business user. While we may be able to train them to use some new data visualization techniques, we will also have to translate other advanced data visualizations into the more commonly used sort.
Data governance team
Data governance is a fundamental part of enabling our entire business to be data driven. The data that is used across the whole company to support common business activities, such as employee performance reviews, financial investments, and new product launches, should be held to a set of standards that ensures its trustworthiness. Among the standards that the data governance team defines and enforces are business rules, data accuracy, data security, and data definitions. The data governance team's job is no less challenging than that of the data R&D team, not the least being because they are the face of the data for most of the business users.
Data governance has a responsibility to make sure data is visualized in a way that is accessible to all business users. Data visualizations should use proper colors, adequate labeling, and approved metrics. The data governance team is also responsible for helping the business users understand data visualization standards, and support those who are going to actively use data to create their own analyses.
Just like our data R&D team, the exact size and makeup of the data governance team will vary. The following list contains the roles that we wish to fill in a team that uses QlikView as its primary data exploration tool:
The data governance team may experiment with some data visualization techniques to best analyze , for example, data accuracy or QlikView Server log data. However, for the most part, the data governance team is responsible for establishing and enforcing data visualization standards that create trustworthiness, increase accessibility, facilitate maintenance, reduce training time, and promote clear enterprise communication.
Note
Data Visualization Strategy 3: Enable the data governance team to establish and enforce data visualization standards.
Each team has a separate set of tasks and priorities. However, all data teams should take advantage of agile project management. The data governance team should be especially careful not to confuse data governance with the creation of bureaucratic project management methods. Otherwise, any competitive advantage gained by using QlikView for fast, flexible data exploration and discovery will be wasted.
Agile development
QlikView is software that is best implemented using agile project management methods. This is especially true when we work closely with the business user to deliver data visualization and analysis that provide real value.
The exact agile project management method that we use is not important. The most popular methods are Scrum, Lean, and Extreme Programming (XP). We can find plenty of books and other material that help us decide which method best fits our situation. However, we do take time in this book to review the overall principles that define agile project management:
"Manifesto for Agile Software Development
We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value:
Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan
That is, while there is value in the items on the right, we value the items on the left more.
Kent Beck, Mike Beedle, Arie van Bennekum, Alistair Cockburn, Ward Cunningham, Martin Fowler, James Grenning, Jim Highsmith, Andrew Hunt, Ron Jeffries, Jon Kern, Brian Marick, Robert C. Martin, Steve Mellor, Ken Schwaber, Jeff Sutherland, Dave Thomas
© 2001, the above authors, this declaration may be freely copied in any form, but only in its entirety through this notice."
We take the liberty to mix a few key words from the different agile methods throughout the rest of the book. The following is a list of the most important terms that we will use, and the context in which we will use them. We also reference the specific method that uses the term.
User story
In each chapter we will describe a series of business user requirements using user stories. A user story
