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  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
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      • Chance Constraints and Value At Risk
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      • Marketing Analytics and Customer Satisfaction
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      • Primary Scales of Measurement
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      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
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      • N-Gram - Frequcy Count and phase mining
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    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
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      • Planning for data visualisation
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      • Content Connection and Chart Legitibility
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      • Introduction
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    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
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    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
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      • Mitesh Thakker
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  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
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​Content Connection and Chart Legitibility

Picture
Content connection
Giving meaning to dataviz and the numbers that comprise them requires a shift from facts to emotional connection. The key here is a sufficient use of contrast which can be used to make quick connections with our audiences. Where there is contrast present on a page, it is where our attention goes.
Contrast can be introduced in 4 primary ways: size, colour, shape, contrived contrast



​
 




​Size contrast:
 It's having elements of two different size, and the difference is striking enough to attract our audience's attention.
Example:
 
David McCandless's work demonstrating the difference between the amount of time US adults spend watching television every year - 200 billion hours - to the amount of time required to create a wonderful resource like Wikipedia – 100 million hours.
Colour contrast: Color can be used very effectively to make elements of our chart pop. It can also be used if we're designing in a more muted color to send other elements to the background, so that our audience doesn't spend much time looking at them. We want to do that with distracting, or the unimportant content on our page.
Example:
 
Note: Colour blindness needs to be taken into account
Shape contrast: The best way to think of this is through iconography. When an icon takes on a shape to represent something that is different from another icon with another shape, audiences can clearly identify what the differences are there. Thus, we can use those effectively in our presentations.
Example:
 
Every icon has a different meaning and the image in its entirety is trying to represent some concept. Also, by changing the contrast of these icons, we can help reinforce the message to our audience.
Contrived contrast: Planned full application of contrast. This includes pre-attentive attributes like boxes, arrows, things that we design into our chart to draw attention to certain elements that we want our audience to see.
 
This is a famous data visualization called The Lumascape from Luma, an investment bank that works in the marketing technology space. They have categorized all the companies working in the space through their logo. They have also applied an excellent example of contrived contrast. They placed boxes around similar companies, or companies operating in the same space. Then they placed arrows around the infographic to show the flow of information, instead of a process.
In conclusion,
  • Contrast separates the important data from the unimportant data through visual context.
  • Visual contrast comes in four primary shapes and forms – size, colour, shape and contrived.
  • Each technique is effective at drawing the audience’s attention to elements of our charts.
 
Notes:
  • Careful attention to detail will ensure dataviz is effective and efficient in communicating insight
  • “Work product” graphics do not need to feature refined execution
  • Effort – and two or more application – transforms visual discovery output into presentation worthy graphics
  • Removal of clutter from a chart is the most impactful way to improve readability
 
Improving Chart Legibility
 
 
 
There are a few tests that we can apply to advance our data visualizations and help us make charts that are much better. They include things like direct labelling and annotations to make them much more effective.
The Spartan Test: Every single element of the chart is removed one by one and we check to see if the meaning of the chart changes. If it doesn’t, then the element did not have much impact on the meaning, and we can remove it permanently. This way, we ensure that we are including only those elements which are truly important and communicate some kind of meaning on the chart.
The Peek Test: Where is your eye drawn? When we open the chart for the first time, notice which part of the chart your eye is drawn to first. That is the most noticeable part of the chart and it is likely that the audience will notice that part first as well.  Is this the part of the chart where you want them to look? If not, then alter the contrast or colour or shape in order to draw attention to the chart element you want to be noticed.
The Colleague Test: Think your visual is perfect? Have a colleague take a look. Give them minimal context and 10-15 seconds to process and ask what they would take away.

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  • Home
  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
      • Simulation Optimization
    • Analytics for Marketing >
      • Marketing Analytics and Customer Satisfaction
      • Customer Satisfaction
      • Measurements and Scaling Techniques – Introduction
      • Primary Scales of Measurement
      • Comparative Scaling
      • Non-Comparative Scaling
      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
      • Text Summarization
      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
      • Data Journey
      • Planning for data visualisation
      • Visualisation Component
      • Content Connection and Chart Legitibility
    • Customer Insights >
      • Introduction
      • What is Descriptive Analytics?
      • Survey Overview
      • Net Promoter Score and Self-Reports
      • Survey Design
      • Passive Data Collection
      • Media Planning
      • Data Visualization
      • Causal Data Collection and Summary
      • Asking Predictive Questions
      • Regression Analysis
      • Data Set Predictions
      • Probability Models
      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
  • Let's Talk
  • MBA Project Sharing
  • About Us
    • Good Read >
      • IIMC says PepsiCo CEO Indra Nooyi was an average student
      • India’s middle class figures in Fortune’s Top Ten list of those who matter
      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
      • About social media and web 2.0
      • Building Your Own Start-up Technology Company, Part 1
      • Building Your Own Start-up Technology Company, Part 2
      • Building Your Own Start-up Technology Company, Part 3
      • Building Your Own Start-up Technology Company, Part 4
      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
      • Home
      • How to Think Creatively
      • A Lighthearted Looks at Project Management and Sports Analogies
      • Why Trust Matters More Than Ever for Brands
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas