1.2 Why does visualization matter?
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The world produces 2.5 quintillion bytes of data every day, and 90% of all data has been created in the last two years. The increased popularity of big data and data analysis projects have made visualization more important than ever. It is unlikely for any single person to wade through data line-by-line and make observations.
Data visualization can illustrate insights better than traditional descriptive statistics ways. A perfect example of this is , created by Francis Anscombe in 1973. The table includes four different datasets with almost identical variance, mean, a correlation between X and Y coordinates, and linear regression lines. All four sets are identical when examined using simple summary statistics. It's hard to catch distinct patterns.
However, the patterns vary considerably in visualization. You can see a linear regression model applies to graphs x1 and x3, but a polynomial regression model fits for x2. The graph x4 shows a high correlation coefficient, even though the other data points do not indicate any relationship between the variables.
Sales and marketing. Research from the media agency Magna predicts that 50% of all global advertising dollars will be spent online by 2020. As a result, marketing teams must pay close attention to sources of web traffic and how their web properties generate revenue. Data visualization makes it easier to see traffic trends over time as a result of marketing efforts.
Politics. A common use of data visualization in politics is a geographic map that displays the party each state or district voted for.
Finance. Finance professionals must track the performance of their investment decisions. For example, the candlestick chart is used to analyze price movements over time and display essential trends.
