Also known as the cause-and-effect diagram, a scatter plot can help you see whether a set variable influences the other and which direction (positive or negative) the correlation is running towards. Line chart example Scatter plotĪ scatter plot is all about mapping out the correlation between two datasets. While it’s best known for highlighting the ups and downs across various data points, a line chart can also effectively compare the trends between different metrics by plotting multiple lines in a single chart. Unlike the column chart, a line chart runs a line through a series of dots. What if you have more than ten datasets to be stacked against another? A line chart is your best bet. Nine times out of ten, a column chart will do the trick if you’re looking for a side-by-side comparison of 10 or fewer items. With these criteria in mind, use the following overview as further guidelines to single out the visual aid that’d best serve your needs: Column chartĪ column chart refers to a graphical display in which vertical bars – the height of each proportionate to the category it represents – run across the chart horizontally.
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Choosing the right graphics for data visualizationĪs wonderful as data visualization is, figuring out which type of visual aid would best represent the dataset can get tricky. Whether it’s your team, board members or external stakeholders, presenting the data through graphics primes even the most boring of datasets to be readily processed and utilized regardless of who’s on the receiving end. For marketers who must repeatedly ask loaded questions such as which acquisition funnels lead to conversion, which time of the day are prospects most active and the like, visualization can help cut through the pile of raw data standing in the way of getting those questions answered.Īnd the best part? Visualization knows no boundaries. It harnesses the ability to unlock hidden patterns, making it possible to connect the dots between disparate data points at once. On the other hand, the scatter plots make plain the positive correlation binding the variables together right from the get-go. Seriously, to the eyes of an average Joe, the table on the left appears as a random concoction of numbers that tell nothing substantive. The difference should be rather stark unless you happen to be a secret mathematical mastermind. Take a second to absorb the contrast between left and right. Harnessing the power of data visualization Once the datasets have been cleaned and standardized, visualization steps in as the last critical step of the refining process to remodel them into intelligible graphics that put actionable insights on full display. This is where visualization comes into the picture. It needs contextualizing and must be broken down first into something more structured and ultimately actionable. To elaborate, oil goes through a refining process before hitting the pumps.
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It’s more so the fact that data, just like crude, is practically useless in its rawest form.
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What makes Humby’s foresight truly impressive, however, isn’t the eventual rise of data as king in advertising. A decade and a half later, his prediction came to fruition as data completely superseded introspection and guesswork as a bottom line for marketing success. Refining raw data with visualizationĬlive Humby was onto something when he proposed data as the new oil to his fellow C-suite executives at the 2006 Association of National Advertisers (ANA) Master of Marketing summit. Instead, we wanted to highlight how the sentiment behind the phrase has never been more apropos for marketers who are left to stay afloat in an expanding sea of raw data every passing day. But we didn’t dig up a century-old proverb to nitpick on its statistical shortcomings.