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