{"id":568,"date":"2025-04-07T12:01:00","date_gmt":"2025-04-07T12:01:00","guid":{"rendered":"https:\/\/pressbooks.palni.org\/leveragingdatavisualizationtocommunicateeffectively\/?post_type=chapter&p=568"},"modified":"2025-07-07T21:34:03","modified_gmt":"2025-07-07T21:34:03","slug":"leverage-data-visualizations-to-show-trends-and-insights","status":"publish","type":"chapter","link":"https:\/\/pressbooks.palni.org\/leveragingdatavisualizationtocommunicateeffectively\/chapter\/leverage-data-visualizations-to-show-trends-and-insights\/","title":{"raw":"Leverage Data Visualizations to Show Trends and Insights","rendered":"Leverage Data Visualizations to Show Trends and Insights"},"content":{"raw":"
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It is true that data visualization is part data science and part art. That being said, even the most creative art is supported by theories that explain why it works.<\/p>\r\n

\u2014Michiko I. Wolcott, CMC, 20-year veteran of consulting in data and analytics<\/p>\r\n<\/blockquote>\r\n

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What Do You Think?<\/strong><\/p>\r\n

Sarah, a marketing executive, is eager to present data findings to her VP, but struggles to interpret and communicate them effectively. Sarah has invited her colleague, Alex, to give the presentation. Alex is passionate about uncovering trends through data visualization. Any time a question comes up about working with the company\u2019s data, Sarah is always told, \u201cGo ask Alex.\u201d Sarah forwarded her last year\u2019s data to Alex and scheduled a virtual conference call.<\/p>\r\n

As Sarah joins the meeting, she is met with a complicated data visualization shared on her screen. Before she can even say \u201chello,\u201d Alex exclaims, \u201cSarah, I\u2019ve spent hours analyzing the data, and I believe I\u2019ve found some incredible trends that can help drive next year\u2019s marketing strategy!\u201d<\/p>\r\n

Sarah looks at the visualization and nervously responds, \u201cThat\u2019s great, Alex! But I must admit, this data visualization seems a little overwhelming. How can we simplify it for our presentation?\u201d Alex turns on his camera, \u201cI understand your concern, Sarah. We need to find a way to break down the complexity and present the trends in a clear and concise manner. How about we focus on the key insights and simplify the visualization to make it more understandable?\u201d<\/p>\r\n

Sarah, with a sigh of relief, responds, \u201cThanks, Alex, I like this plan! So what were the most significant trends discovered?\u201d<\/p>\r\n

Alex leans in, \u201cWell, one trend that stands out is the correlation between our social media campaigns and website traffic. There is a substantial increase in website visits following each campaign launch.\u201d<\/p>\r\n

Alex smiles and responds, \u201cLet\u2019s use a line chart to showcase the website traffic over time, with clear markers indicating the dates of each social media campaign. This way, we can demonstrate the direct impact of our campaigns on generating traffic!\u201d<\/p>\r\n

Alex sees Sarah taking notes. She looks up, and he can see her excitement as she responds, \u201cI can already visualize it. And I know the perfect story to include\u2014we have a great testimonial from a customer about our July campaign to go with it. What other trends have you found?\u201d<\/p>\r\n

Alex showed Sarah three more insightful trends. The collaborative nature of their discussion heightened their determination to deliver a compelling and informative story with insightful data visualizations.<\/p>\r\n

In this scenario, Alex and Sarah faced the challenge of presenting a complex data visualization that shows trends. Think about your own organization. Do you look for insight from your data to strategize goals for next year?<\/p>\r\n\r\n<\/div>\r\n

Introduction<\/h1>\r\n

In the age of information overload, harnessing the power of data has become paramount for organizations seeking a competitive edge. In the scenario, Sarah recognized the ability to extract meaningful trends and insights from complex datasets was a skill she needed\u2014a skill that can translate into a strategic advantage. Alex was able to transform the raw data and suggest visuals, but it was Sarah who saw a need to incorporate a story. As they collaborated on visual narratives that would bring data to life, they brainstormed ways to make them more accessible (easier to understand) and engaging for their main decision-maker, their VP.<\/p>\r\n

Within this chapter, we will delve into the principles and best practices of leveraging data visualizations to unveil trends and insights. We will explore various visualization techniques and tools, highlighting their strengths and applications in different contexts. Graphical displays of data make it easier to spot patterns and detect unusual values. Besides the graphical displays to help us understand our data, this chapter also introduces benchmarking, tools used to visualize and persuade, statistical analysis tools, and visuals to support decision-making.<\/p>\r\n

We will also explore techniques to effectively communicate insights derived from data visualizations for diverse audiences, ensuring that the impact of the findings is maximized. So let\u2019s embark on a journey to leverage data visualizations, unravel the hidden stories within the data, and uncover trends and insights that can shape the future of our organizations.<\/p>\r\n

Chapter 3 addresses the following learning objectives.<\/p>\r\n\r\n

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Learning Objectives<\/p>\r\n\r\n<\/header>\r\n

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At the end of this chapter, students should be able to:<\/p>\r\n\r\n

    \r\n \t
  1. LO 1: Determine insights.<\/li>\r\n \t
  2. LO 2: Use benchmarking, decision-making, and strategic analysis tools.<\/li>\r\n \t
  3. LO 3: Interpret research industry trends and recognize bias.<\/li>\r\n \t
  4. LO 4: Create and use benchmarking frameworks to provide context for problems, support decisions, and measure performance.<\/li>\r\n \t
  5. LO 5: Create and use decision-making tools to provide context for problems, support decisions, and assess risk.<\/li>\r\n \t
  6. LO 6: Conduct preliminary statistical analysis, predictive statistics, and understand Six Sigma.<\/li>\r\n<\/ol>\r\n

    Key Terms:<\/em> Balanced Scorecard, decision trees, descriptive statistics, Disruptor, Ishikawa, MarketLine, Pareto Diagrams, PEST, PESTLE, Porter\u2019s Five Forces, posters, regression analysis, Six Sigma, sparklines, SWOT<\/p>\r\nDownload Chapter 3 PowerPoint (10 MB).<\/a>\r\n\r\nDownload Chapter 3 Student Files (7.5MB).<\/a>\r\n\r\n<\/div>\r\n<\/div>\r\n

    3.1 An Introduction to Anscombe\u2019s Four Datasets<\/h1>\r\n
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    The goal is to turn data into information and information into insight.<\/p>\r\n

    \u2014Carly Fiorina, former CEO of Hewlett-Packard<\/p>\r\n<\/blockquote>\r\n

    One of the best ways to see the importance of making a visual of data is to introduce the Anscombe datasets. In 1973, Anscombe built four datasets of 11 points each. Statistically they are very similar: Averages, variances, correlation, and even the linear regression formula are the same. Here are the four datasets slightly modified from the original. The modifications are highlighted in Figure 3.1.<\/p>\r\nFigure 3.1\u2014Modified Anscombe\u2019s Quartet<\/strong>\r\n\r\n\"A\r\n\r\nNote: The yellow highlighting are numbers that are slightly different from the original Anscombe\u2019s quartet dataset.\r\n

    When calculated, the following statistical information is true for each of the four datasets<\/strong>.<\/p>\r\n\r\n