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To start with chart-making rules is to forgo strategy for execution it’s to pack for a trip without knowing where you’re going. When should I use a bar chart? How many colors are too many? Where should the key go? Do I have to start my y-axis at zero? Visual grammar is important and useful-but knowing it doesn’t guarantee that you’ll make good charts. Managers who want to get better at making charts often start by learning rules. It’s not ‘Here are our Q3 financial results,’ it’s ‘Here’s where we missed our targets.’” Project the idea that you’re showing a reflection of human activity, of things people did to make a line go up and down. As the presentation expert Nancy Duarte puts it, “Don’t project the idea that you’re showing a chart. Automatically converting spreadsheet cells into a chart only visualizes pieces of a spreadsheet it doesn’t capture an idea. Convenient is a tempting replacement for good, but it will lead to charts that are merely adequate or, worse, ineffective. One drawback, though, is that it reinforces the impulse to “click and viz” without first thinking about your purpose and goals. Thanks to the internet and a growing number of affordable tools, translating information into visuals is now easy (and cheap) for everyone, regardless of data skills or design skills. Complex systems-business process workflows, for example, or the way customers move through a store-are hard to understand, much less fix, if you can’t first see them. Without visualization, detecting the inefficiencies hidden in the patterns and anomalies of that data would be an impossible slog.īut even information that’s not statistical demands visual expression. Ten takeoffs and landings produce as much data as is held in the Library of Congress.
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But each time the Osprey gets off the ground or touches back down, its sensors create a terabyte of data. A typical example: At Boeing the managers of the Osprey program need to improve the efficiency of the aircraft’s takeoffs and landings. Decision making increasingly relies on data, which comes at us with such overwhelming velocity, and in such volume, that we can’t comprehend it without some layer of abstraction, such as a visual one. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.ĭata is the primary force behind this shift. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone-but that convenience can lead to charts that are merely adequate or even ineffective.īy answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? and Am I declaring something or exploring something? He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial.
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But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers.