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Why Data Visualization Isn't Enough: Collecting Viz-Worthy Data in 4 Steps

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Why Data Visualization Isn't Enough 1 Collecting Viz-Worthy Data in 4 Steps Ask any marketing executive and they will all tell you the same thing: capturing reliable marketing attribution information is essential to effectively allocating marketing spend, and the potential ROI is enormous. Marketers have known this for years, and mature companies have taken note. With the rise of count- less new businesses dedicated to providing market- ing technologies, MarTech spending now accounts for roughly a quarter of marketing budgets. In short: companies want to obtain that enormous ROI, and they are willing to spend millions of dollars in the process. But where is all this money really going? Much of the time, these MarTech dollars are devoted toward data visualization tools which help marketers better understand and describe the sto- ries told by their data. Visualization can effectively help teams pinpoint the most important insights in their field of data, but what if the data being visualized is inherently flawed or incomplete? Hint: To see how you can effectively pinpoint data stories and present your data visualizations, check out our complimentary expert panel discussion, "Demystifying Data Storytelling." Often, attempts to ensure the accuracy and com- pleteness of analytics data come too late in the attribution process (or not at all) to create reliable visualizations. Valuable team members can spend hours a day manually cleansing data only to find that significant gaps remain. No matter how slick your data visualizations turn out, they are only as reliable as the data they are built from. This is where a change in perspective can make all the difference in your data-viz game. In order to im- prove your data visualizations, try taking a few steps back and focusing on fixing the foundational prob- lems that lead to incomplete, inconsistent data. Ask yourself, "How can I reset my strategy to focus on data quality?" To help you address this question, here are four steps you can take toward data completeness.

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