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Synopsis

The human brain isn't wired to interpret binary code or swiftly comprehend written information; that is why data visualization is crucial for complex ideas and concepts communication. There is, however, hardly anything more dull and time-consuming than charts and graphs construction from scratch. Our Charts Collection (Part 1) offers a variety of 100% customizable charts, bars and graphs, which will save you hours of work and allow you to focus on more important tasks.

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Slide highlights

If you need to understand and explain outliers, the normal tendency and the range of information in your values, distribution charts are your friends. Scatter Plot, Mekko, Line, Column and Bar charts serve these purposes the best.

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Mekko, Line, Column and Bar charts are all tools used in data analysis to understand and explain outliers, the normal tendency, and the range of information in your values. Mekko charts are useful for comparing different data sets and their composition. Line charts are great for showing trends over time. Column and Bar charts are ideal for comparing different categories of data.

The best charts for explaining outliers in data are distribution charts. Specifically, Scatter Plot, Mekko, Line, Column and Bar charts serve these purposes the best.

The Charts Collection offers a variety of 100% customizable charts, bars, and graphs. You can customize them according to your needs to understand and explain outliers, the normal tendency, and the range of information in your values. The types of charts available for customization include Scatter Plot, Mekko, Line, Column, and Bar charts.

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It is quite possible that in your presentation, you need to demonstrate how a piece of data performed during a specific period of time. This task requires specific chart types such as Pyramid, Line, Dual-Axis Line or Column.

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To compare and contrast one or many value sets and communicate the low and high values in the data sets to your team and stakeholders, charts like Column, Mekko, Bar, Pie, Line, Scatter Plot and Bullet are the optimal choice.

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Application

Scott Berinato, a senior editor at Harvard Business Review (HBR) and the author of Good Charts Workbook, uncovers the secrets of data visuals that really work.

Start with idea generation

Although managers don't necessarily think of visualization as a tool to support idea generation, Berinato says, they use it to brainstorm all the time whether it is on whiteboards or on butcher paper. "Like idea illustration, idea generation relies on conceptual metaphors, but it takes place in more informal settings, such as off-sites, strategy sessions, and early-phase innovation projects. It's used to find new ways of seeing how the business works and to answer complex managerial challenges: restructuring an organization, coming up with a new business process, codifying a system for making decisions," he says.

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Idea generation can be used in various ways to address complex managerial challenges. It can be used in strategy sessions to develop new business processes or to restructure an organization. It can also be used in early-phase innovation projects to find new ways of understanding how the business operates. Additionally, idea generation can be used to codify a system for making decisions. These are just a few examples, and the possibilities are endless depending on the specific challenge at hand.

Idea generation can be used in restructuring an organization in several ways. It can be used to brainstorm new business processes, to develop a system for making decisions, and to find new ways of seeing how the business works. It can also be used in strategy sessions and early-phase innovation projects to come up with creative solutions to complex managerial challenges.

Idea generation can be used in early-phase innovation projects to find new ways of seeing how the business works and to answer complex managerial challenges such as restructuring an organization, coming up with a new business process, or codifying a system for making decisions. It relies on conceptual metaphors and often takes place in informal settings like off-sites, strategy sessions, and early-phase innovation projects.

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Work on your visual discover

Visual discovery is the most complicated quadrant because it consists of two categories: testing a hypothesis and mining for patterns, trends and anomalies. Berinato says: "The former is focused, whereas the latter is more flexible. The bigger and more complex the data, and the less you know going in, the more open-ended the work."

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Visual discovery can enhance the understanding of complex data by allowing for the testing of hypotheses and mining for patterns, trends, and anomalies. It provides a focused approach when there is a specific hypothesis to test, and a more flexible, open-ended approach when the data is large and complex and less is known about it initially. This dual approach can help uncover insights that might otherwise be missed in the data.

Visual discovery in data science plays a crucial role in testing hypotheses and mining for patterns, trends, and anomalies. It is particularly useful when dealing with large and complex data sets, where the initial knowledge about the data is limited. The process is more open-ended, allowing for a flexible approach to data analysis.

Visual discovery can be used in market research in two main ways: testing a hypothesis and mining for patterns, trends, and anomalies. The former is a focused approach where a specific hypothesis is tested using visual data. The latter is a more flexible approach where the data is explored to identify patterns, trends, and anomalies that may not have been initially apparent. This can be particularly useful in market research as it can reveal insights about consumer behavior, market trends, and potential opportunities that were not previously known.

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Confine your visuals

In the visual confirmation phase, you're looking for answers to these questions: Is what I suspect actually true? and What are some other ways of depicting this idea? According to Berinato, gaining the spreadsheets manipulation skills and swift prototyping tools knowledge are useful in this phase.

Move on to the visual exploration

As an example, Berinato uses Tesla Motors' Data Scientist, Anmol Garg. Garg has used visual exploration to tap into the vast amount of sensor data Tesla cars produce. He developed an interactive chart that shows the pressure in a car's tires over time. "In true exploratory form, [Grag] first created the visualizations and then found a variety of uses for them: to see whether tires are properly inflated when a car leaves the factory, how often customers reinflate them, and how long customers take to respond to a low-pressure alert; to find leak rates; and to do some predictive modeling on when tires are likely to go flat. The pressure of all four tires is visualized on a scatter plot, which, however inscrutable to a general audience, is clear to its intended audience," Berinato writes.

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The future prospects of using visual exploration in the automobile industry are promising. As seen in the example of Tesla Motors' Data Scientist, Anmol Garg, visual exploration can be used to tap into the vast amount of sensor data produced by cars. This data can be visualized to monitor various aspects such as tire pressure over time. This can help in identifying whether tires are properly inflated when a car leaves the factory, how often customers reinflate them, and how long customers take to respond to a low-pressure alert. It can also be used to find leak rates and do some predictive modeling on when tires are likely to go flat. Thus, visual exploration can play a crucial role in predictive maintenance, quality control, and enhancing customer service in the automobile industry.

Visual exploration can enhance the driving experience by providing valuable insights from the data collected by the car's sensors. For instance, Tesla's Data Scientist, Anmol Garg, developed an interactive chart that shows the pressure in a car's tires over time. This visualization helped in several ways such as checking if the tires are properly inflated when a car leaves the factory, tracking how often customers reinflate them, and predicting when tires are likely to go flat. Thus, visual exploration can help in predictive modeling, maintenance, and improving overall driving experience.

There are numerous types of sensor data that could be visualized using interactive charts. This could include temperature data, humidity data, light intensity data, sound level data, motion detection data, and many more. The type of sensor data that can be visualized is only limited by the types of sensors available and the ability to interpret and present the data in a meaningful way.

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Statistics

Business solutions platform, CSG, put together important statistics that prove the power of data visualization:

  • High-quality infographics are 30 times more likely to be read than plain text
  • If a scientific claim is presented in pure words or numbers, 68% of people will believe that the information is accurate and truthful. With a simple graph accompanying the claim, the number rises to 97%
  • The use of data visualizations could shorten business meeting by 24%
  • Managers in organizations with visual data recovery tools are 28% more likely to find timely information
  • Companies with the most advanced analytics capabilities are 2 times more likely to be in the top quartile of financial performance within their industries, 2 times more likely to use data very frequently when making decisions, 3 times more likely to execute decisions as intended and 5 times more likely to make decisions much faster than market peers
  • Business intelligence with data visualization capabilities will offer a Return On Investment (ROI) of $13.01 back on every dollar spent.
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