Imagine a data scientist said to you, \u201ccollecting and cleaning data is laborious and tedious, but once I had the data, an infinite variety of views opened up.\u201d While sounding like a familiar lament from any contemporary data conference, this quote is 250 years old. It was written by Joseph Priestley, the polymath who created some of the first visualisations of visualise historical data. \u00a0<\/p>\n
Priestley\u2019s quote reminds us that we should not forget that we\u2019ve been wrestling with data analytics and visualisation for a long time. The challenges the pioneers faced are the same ones we face today. Aspiring data-driven organisations can learn as much from the pioneers as any contemporary text book.\u00a0<\/p>\n
William Playfair, another pioneer of visualisation in the early 19th Century, said this about data analysis: \u201cAs knowledge increases among mankind, and transactions multiply, it becomes more and more desirable to abbreviate and facilitate the modes of conveying information from one person to another, and from one individual to the many.\u201d He\u2019s talking about the modern challenge – summarise information to enable more people to understand a system.\u00a0<\/p>\n
Playfair\u2019s words emphasise how much the first data analysts realised the absolute necessity of visualising data to get their message across. Raw data is invaluable but must be transformed into something else to make a difference.<\/p>\n
The battle between letting the data speak for itself and creating a vivid visualisation that grabs the audience began two hundred years ago. Playfair said, \u201cnothing is more dry and tedious as statistics,\u201d and data must \u201cset the mind and imagination to work\u201d. \u00a0<\/p>\n
But a contemporary of Playfair, the medical statistician William Farr, said, \u201cstatistics should be the driest reading of all\u201d. Reconciling these two viewpoints continues to challenge us today!<\/p>\n
The pioneers experimented, creating the wonderful line and bar charts that are so effective today. Yet, they also gave us pie charts, leading to many crimes against data that persist nowadays.\u00a0<\/p>\n
Finding the \u201cbest\u201d charts has been a driver for pioneers in data visualisation. American engineer Willard Cope Brinton published his “Graphical methods for presenting facts\u201d\u00a0in 1914 with a clear belief that \u201cfacts do not speak for themselves. When they do speak for themselves, the wrong conclusions are often drawn from them. Unless the facts are presented in a clear and interesting manner, they are about as effective as a phonograph record with the phonograph missing.\u201d<\/p>\n
His book offers today\u2019s data analysts an extremely rich and wise set of instructions and ideas about the dos and don\u2019ts of using graphics to explain data insights. Notably, Brinton slams the pie chart as \u201cinaccurate\u201d and applauds bar graphs as a better way to convey data comparisons.\u00a0<\/p>\n
Brinton is incredibly modern – he foresaw the data-driven enterprise when he said the executive of the future will depend on his powers of accurate data analysis. Brinton also realised that merely having the data and visualising it is not enough. To be a data-driven organisation, people at all levels need to be better educated in data literacy.\u00a0<\/p>\n
Even as data analysis and visualisation are integral to digital businesses and are supported by advances in modern business intelligence, including AI and machine learning, some fundamentals have usefully been set out and debated for years, if not centuries.\u00a0<\/p>\n
Andy\u00a0Cotgreave, Technical evangelist and senior director, Tableau<\/p>\n
Andy\u00a0has a strong passion for visual analysis and data technologies. He is a visual analysis expert at Tableau and has a strong perspective on the issue of delivering basic digital skills. He has been with Tableau in various roles since 2011 ranging from product consultant to product marketing to his current role as Senior Technical Evangelist. Prior to joining Tableau Andy was a data analyst at the University of Oxford.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Data analysts may feel their struggle to make data comprehensible through great visualisation are dealing with a 21st Century problem. But as Tableau\u2019s Andy Cotgreave explains, it is a problem with a 250-year history that\u2019s well worth understanding.<\/p>\n","protected":false},"author":3,"featured_media":15436,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,398],"tags":[169,177,735,805],"pillar":[],"class_list":["post-15435","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-public","tag-analytics-and-insight","tag-business-intelligence","tag-data-visualisation","tag-tableau"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-06-16 21:14:27","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/15435","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=15435"}],"version-history":[{"count":0,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/15435\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/15436"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=15435"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=15435"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=15435"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=15435"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}