{"id":28244,"date":"2022-01-16T06:00:00","date_gmt":"2022-01-16T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=28244"},"modified":"2022-01-14T13:45:06","modified_gmt":"2022-01-14T21:45:06","slug":"explanatory-vs-exploratory-which-data-visualization-is-right-for-your-organization","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/01\/16\/explanatory-vs-exploratory-which-data-visualization-is-right-for-your-organization\/","title":{"rendered":"Explanatory vs. Exploratory \u2014 Which Data Visualization Is Right for Your Organization?"},"content":{"rendered":"\n
\"\"<\/figure><\/div>\n\n\n\n

In this special guest feature, Chad Reid, VP of marketing and communications at Jotform<\/a>, argues that data visualization remains one of the best tools you can use to highlight relevant information for stakeholders, but to use it to its full potential, it is key to understand the difference between explanatory versus exploratory data analysis and know when to use each. He provides three strategies for better data visualization. Chad is a frequent contributor to various tech and business publications \u2014 and an absolute wizard with a Vitamix.<\/em><\/p>\n\n\n\n

The human brain is powerful, but it has limits \u2014 one being the ability to multitask effectively. According to Cleveland Clinic research<\/a>, a scant 2.5% of the population can focus on more than one responsibility at a time. As a result, people faced with huge amounts of data struggle to process it well.<\/p>\n\n\n\n

This can create difficulty for tech leaders trying to use raw data points to justify budget expansions or push a client deal over the finish line. Too much unstructured data won\u2019t make sense to these stakeholders \u2014 but data visualization<\/a> can help.<\/p>\n\n\n\n

The Value of Data Visualization Techniques<\/strong><\/p>\n\n\n\n

In basic terms, data visualization involves turning data into graphics. Pie charts, graphs, maps, and tables are all examples of data visualization.<\/p>\n\n\n\n

 When data is structured this way, it makes more sense and becomes significantly more digestible. People can see context and patterns while noticing trends. They can also make sense of seemingly dissimilar data points and mountains of information much more easily.<\/p>\n\n\n\n

When people understand what they\u2019re seeing, they also become less hesitant to make decisions based on that information. In one study, 64% of participants<\/a> were ready to make binding choices after seeing data in a mapped-out format.<\/p>\n\n\n\n

Data visualization remains one of the best tools you can use to highlight relevant information for stakeholders. To ensure that you use it to its fullest potential, it’s key to understand the difference between explanatory versus exploratory data analysis and know when to use each.<\/p>\n\n\n\n

Explanatory vs. Exploratory Data<\/strong><\/p>\n\n\n\n

When planning your data visualization \u2014 whether it\u2019s a chart, an infographic, a table, or some other form \u2014 you need to determine if it\u2019s designed to explain or explore the information you\u2019re presenting.<\/p>\n\n\n\n

While digging through massive data stores, you\u2019re looking for interesting tidbits that might have connections. You likely have some raw data. though it might not yet make a lot of sense. Consider an archeological dig that brings up hundreds of pieces of glazed ceramics. Are these pieces from one item or several? And how was the item used? The only way to answer those questions is to determine the context, or the \u201chow\u201d and \u201cwhy.\u201d The same is true of your many pieces of data.<\/p>\n\n\n\n

If you hope to bring your data to life, you\u2019ll need to create an explanatory data visualization. You\u2019ve already done the exploring \u2014 with your explanatory data visualization, you can offer actionable insights based on what you\u2019ve collected.<\/p>\n\n\n\n

Ready to get started? Here are a few tips for better data visualization.<\/p>\n\n\n\n

1. Add text and labels to visuals.<\/strong><\/p>\n\n\n\n

Label your charts. Use colorful keys. Make annotations. Do what it takes to help your viewers grasp what they\u2019re seeing. If you only plot out numbers, you aren\u2019t providing any context or explanation. Don\u2019t assume that everyone will draw the correct conclusions without help.<\/p>\n\n\n\n

2. Opt for a clutter-free experience.<\/strong><\/p>\n\n\n\n

User experience (UX) matters when it comes to data visualization. Avoid letting your charts and tables get too messy or over-complicated. Ideally, you\u2019ll want to temper the number of complex designs. Follow a simple adage: \u201cWhen in doubt, take it out.\u201d Unless something\u2019s absolutely necessary, it doesn\u2019t belong in your visual.<\/p>\n\n\n\n

3. Ensure your visuals support your conclusions.<\/strong><\/p>\n\n\n\n

You\u2019ve made a conclusion based on your exploratory data research. Does your visualization buoy that conclusion and help others understand it? You can\u2019t assume anything. Review your graphic to ensure it tells the right story. If it doesn\u2019t, it could be time to choose a different format.<\/p>\n\n\n\n

For example, infographics might look pretty, but they\u2019re rarely laser-focused on one specific point. Unless you\u2019re short on time, play around with a few data visualization types to see which best communicates your data discoveries.<\/p>\n\n\n\n

The next time you plan to share data, consider putting it into a digestible and understandable explanatory visualization format. You\u2019ll improve your chances of educating your audience while helping others see why <\/em>the data matters and what it means \u2014<\/em> rather than just a bunch of numbers.<\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this special guest feature, Chad Reid, VP of marketing and communications at Jotform, argues that data visualization remains one of the best tools you can use to highlight relevant information for stakeholders, but to use it to its full potential, it is key to understand the difference between explanatory versus exploratory data analysis and know when to use each. He provides three strategies for better data visualization.<\/p>\n","protected":false},"author":10513,"featured_media":28245,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[115,87,180,61,56,97,1,70],"tags":[],"acf":[],"yoast_head":"\nExplanatory vs. Exploratory \u2014 Which Data Visualization Is Right for Your Organization? - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/01\/16\/explanatory-vs-exploratory-which-data-visualization-is-right-for-your-organization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Explanatory vs. Exploratory \u2014 Which Data Visualization Is Right for Your Organization? - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this special guest feature, Chad Reid, VP of marketing and communications at Jotform, argues that data visualization remains one of the best tools you can use to highlight relevant information for stakeholders, but to use it to its full potential, it is key to understand the difference between explanatory versus exploratory data analysis and know when to use each. 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