Learning QlikView Data Visualization


Learning QlikView Data Visualization
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Overview
Table of Contents
Author
Support
Sample Chapters
  • 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

Book Details

Language : English
Paperback : 156 pages [ 235mm x 191mm ]
Release Date : September 2013
ISBN : 1782179895
ISBN 13 : 9781782179894
Author(s) : Karl Pover
Topics and Technologies : All Books, Big Data and Business Intelligence, Enterprise Products and Platforms

Table of Contents

Preface
Chapter 1: First Things First
Chapter 2: Rank Analysis
Chapter 3: Trend Analysis
Chapter 4: Multivariate Analysis
Chapter 5: Distribution Analysis
Chapter 6: Correlation Analysis
Chapter 7: Geographical Analysis
Chapter 8: What-if Analysis
Chapter 9: Dashboards and Navigation
Index
  • Chapter 1: First Things First
    • Project background
    • People
      • Ownership
      • Driven
      • Honest
      • Flexible
      • Analytical
      • Knowledgeable
      • Team player
    • Data
      • Reliable
      • Detailed
      • Formal
      • Flexible
      • Referential
    • Tools
      • Fast and easy implementation
      • Business empowerment
      • Enterprise-ready
    • QlikView
      • Installing QlikView
        • Important general configuration
      • Let's start discovering data
        • Opening our first QlikView application
    • Summary
    • Chapter 2: Rank Analysis
      • What is rank analysis?
      • Bar chart
        • Objects to support bar charts
          • Listbox
          • Search object
          • Current selections box
      • Data visualization style guide for bar charts
        • Rule 1 – use adequate labeling
          • Chart labels
          • Dimension and metric labels
          • Axes labels
        • Rule 2 – convert color into data
          • Associative
          • Highlighting
        • Alerts
        • Heat map
        • Rule 3 – add more detail
          • Additional dimensions
          • Additional expressions
        • Rule 4 – throw away chartjunk
        • Rule 5 – respect usability
          • Caption
          • Inside the chart
        • Rule 6 – be honest
          • Chart width to height ratio
          • Axis not forced to zero
      • Summary
      • Chapter 3: Trend Analysis
        • What is trend analysis?
        • The line chart
          • Objects to support line charts
        • Data visualization style guide for line charts
          • Rule 1 – use adequate labeling
            • Chart labels
            • Dimension and metric labels
            • Axes labels
          • Rule 2 – convert color into data
            • Associative
            • Dynamic highlighting
            • References
          • Rule 3 – add more detail
            • Additional dimension
            • Additional metric
          • Rule 4 – throw away chartjunk
            • Axis and grid lines
          • Rule 5 – respect usability
            • Caption
            • Inside the chart
          • Rule 6 – being honest
            • Chart width to height ratio
            • Axis not forced to zero
        • Summary
        • Chapter 4: Multivariate Analysis
          • What is multivariate analysis?
          • Table charts
            • Heat map
            • Mini-charts
              • Straight table for multiple metrics
              • Pivot table for multiple dimensions
          • Data visualization style guide for table charts
            • Rule 1 – use adequate labeling
            • Chart labels
            • Dimension and metric labels
            • Rule 2 – convert color into data
            • Rule 3 – add more detail
            • Rule 4 – throw away chartjunk
              • Number format
              • Table grid
            • Rule 5 – respect usability
            • Rule 6 – be honest
          • Summary
          • Chapter 5: Distribution Analysis
            • What is distribution analysis?
            • Histogram chart
              • Important functions
            • The histogram specific properties
            • Objects to support histogram charts
              • The input box
          • Data visualization style guide for histogram charts
            • Rule 1 – use adequate labeling
              • Dimensional reference lines
              • Important functions
            • Rule 2 – convert color into data
            • Rule 3 – add more detail
              • Frequency polygon
              • Box plot
            • Rule 4 – throw away chartjunk
            • Rule 5 – respect usability
            • Rule 6 – be honest
          • Summary
            • Chapter 6: Correlation Analysis
              • What is correlation analysis?
              • Scatterplot chart
              • Data visualization style guide for scatterplot charts
                • Rule 1: use adequate labeling
                  • Trendlines
                • Rule 2 – convert color into data
                  • Important function
                • Rule 3 – add more detail
                  • Z axis
                  • Trails
                  • Animation
                • Rule 4 – throw away chartjunk
                • Rule 5 – respect usability
                • Rule 6 – be honest
              • Summary
              • Chapter 7: Geographical Analysis
                • What is geographical analysis?
                • QlikMarket
                • Area map chart
                  • Dimensions
                  • Metrics
                • Data visualization style guide for area map charts
                  • Rule 1 – use adequate labeling
                  • Rule 2 – convert color into data
                  • Rule 3 – add more detail
                  • Rule 4 – throw away chartjunk
                  • Rule 5 – respect usability
                  • Rule 6 – be honest
                • Summary
                • Chapter 8: What-if Analysis
                  • What is what-if analysis?
                  • Global variable what-if analysis
                    • Global variables
                  • Detailed variable what-if analysis
                    • Detailed variables
                  • Summary
                  • Chapter 9: Dashboards and Navigation
                    • What is a dashboard?
                    • The dashboard application
                      • Document settings
                      • Variables
                      • Layout
                      • Supporting objects
                      • Lines
                      • Text objects for images
                      • Current selections box
                      • The search object
                      • The loaded listboxes
                        • Important functions
                      • Multibox
                      • The bookmark object
                      • Arranging objects
                      • Listboxes for dates
                    • Key Performance Indicators (KPI's)
                      • Icons
                      • Quick global perspective
                      • The gauge chart
                      • KPI breakdown
                        • The KPI table
                      • Brief analysis
                        • Migration
                        • The container object
                        • Buttons and actions
                    • Summary

                    Karl Pover

                    Karl Pover is co-owner of Evolution Consulting (http://www.evolcon.com), which provides QlikView consulting services throughout Mexico. Since 2006, he has been dedicated to providing QlikView pre-sales, implementation, training, and expert services. He has worked in more than 50 companies and government agencies, and set up QlikView competence centers that expand the globe. Most importantly, he has formed a team of highly capable consultants that together have done far more than him. Recently, he has started a blog (http://www.poverconsulting.com) that will continue to share his experiences in the world of data discovery.
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                    Frequently bought together

                    Learning QlikView Data Visualization +    QlikView for Developers Cookbook =
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                    What you will learn from this book

                    • Define the characteristics of the people, data, and tools involved in a data discovery project
                    • Perform data discovery that helps you validate data quality
                    • Understand and execute rank, trend, multivariate, distribution, correlation, geographical, and what-if analysis
                    • Create bar, line, scatterplot, heat map, table, histogram, box plot, and geographical charts
                    • Prevent data visualization manipulation and the formulation of incorrect conclusions
                    • Create a dashboard to present your case and monitor future actions
                    • Eliminate non-data and non-usability ink for a clean display
                    • Pack more detail into each chart with techniques to add animation, trails, and sparklines, along with creating a trellis chart

                    In Detail

                    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.

                    Approach

                    A practical and fast-paced guide that gives you all the information you need to start developing charts from your data.

                    Who this book is for

                    Learning QlikView Data Visualization is for anybody interested in performing powerful data analysis and crafting insightful data visualization, independent of any previous knowledge of QlikView. Experience with spreadsheet software will help you understand QlikView functions.

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