{"id":15418,"date":"2019-07-24T00:00:00","date_gmt":"2019-07-23T23:00:00","guid":{"rendered":"https:\/\/members.dataiq.global\/articles\/dont-let-data-cliches-hold-back-progress\/"},"modified":"2019-07-24T00:00:00","modified_gmt":"2019-07-23T23:00:00","slug":"dont-let-data-cliches-hold-back-progress","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/dont-let-data-cliches-hold-back-progress\/","title":{"rendered":"Don\u2019t let data clich\u00e9s hold back progress"},"content":{"rendered":"

However, I can hear your groans about what has become one of those data clich\u00e9s that plague our profession. To satisfy any bloodlust against this one, you can read some comprehensive demolitions of how data isn\u2019t the new oil from data scientists<\/a>\u00a0and others<\/a>.\u00a0<\/p>\n

Searching for the lost data archipelago<\/strong><\/h3>\n

Our industry is, alas, very good at creating clich\u00e9s that give birth to yet more clich\u00e9s. The data warehouse gave birth to the data lake, for example. So, what\u2019s next?\u00a0<\/p>\n

Here\u2019s a game to play. Take the word data and add a body of water after it. You instantly have a selling message – a new clich\u00e9 to convince the market with (try it: how you would sell a data delta? A data glacier? A data waterfall, even?)\u00a0<\/p>\n

Many years ago, I created the term data archipelago as an April Fool\u2019s joke. In my definition, the archipelago\u2019s myriad islands were devices and the water the connecting network links. Within months, a well-known technology vendor launched a video marketing campaign on a data archipelago concept.\u00a0<\/p>\n

The never-ending big data era<\/strong><\/h3>\n

The biggest clich\u00e9 in our industry has become the term big data. Yes, there has been a big change in technology and the term had its moment. Over time, it became a lazily used phrase. The case highlights how our lazy thinking holds back good data analytics and visualisation.\u00a0<\/p>\n

We think we live in the era of big data, which many say began around 15 – 20 years ago. Maybe so, but consider this quote: \u201cMillions of dollars are spent annually on the collection of data in the assumption that the possession of that data will solve the problem at hand.\u201d\u00a0<\/p>\n

It\u2019s from 1914, 105 years ago. It was in the introduction to \u201cGraphic methods for presentation\u201d by Willard Cope Brinton. He described the power of new punch card technology and the Hollerith tabulating machines to process up to 3,000 records of data an hour. This was big data for Brinton and those other data analysts and visualisers of his day.\u00a0<\/p>\n

Even as Brinton alluded to big data in 1914, it already felt like it had been around for some time. Hollerith tabulating machines had been invented to deal with the era\u2019s big data challenges, starting with the US Census of 1890. The 11th US Census was a huge data set that had grown by 25 percent from 50 million to close to 63 million records. So, big data was a quarter of a century-old challenge and opportunity even in 1914.\u00a0<\/p>\n

At all stages of our society\u2019s development we\u2019ve had ideas. As our technology and data increase in size, so has the scale of our ideas. We are in a perpetual \u201cbig data\u201d phase, where ideas will be pushed forward on the wave of technology and data.<\/p>\n

Looking back at the history is great fun and instructive, as I have written in DataIQ before<\/a>, if we are to avoid repeating the mistakes of data visualisation from the past.\u00a0<\/p>\n

Seeing beyond the buzzwords<\/strong><\/h3>\n

As we look around, considering what data platforms to invest in today, it is easy to get excited by whatever the latest clich\u00e9 is (artificial intelligence, anyone?). The important thing to do is to see through the latest buzzwords and think about why we are using data. We need to understand how and why our data is used.\u00a0<\/p>\n

Beyond the clich\u00e9s and beyond the tools themselves, we are confronted with a major cultural challenge. More effort needs to be put into considering how to build a data culture that finds answers to a series of fruitful questions.\u00a0<\/p>\n

What do we want our people to do? How many people should work with data and how do we make them curious? How are you encouraging people to ask open questions and come forward with ideas you hadn\u2019t thought about?\u00a0<\/p>\n

Answers to these kinds of questions must be the starting point for making data analytics productive. Organisations are not transformed by throwing around the latest buzzwords. Your colleagues are not na\u00efve: they need tools that work and the support to use them.\u00a0<\/p>\n

Andy Cotgreave is technical evangelism director at Tableau<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

It\u2019s easy to launch a new clich\u00e9 into this industry – just put the word data in front of anything you can think of. Andy Cotgreave argues that despite the swarms of buzzwords, there really are useful new tools and data visualisation techniques lurking underneath.<\/p>\n","protected":false},"author":3,"featured_media":15419,"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":[169,735,167,805,91],"pillar":[],"class_list":["post-15418","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-public","tag-analytics-and-insight","tag-data-visualisation","tag-organisation-and-culture","tag-tableau","tag-technology"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-21 03:58:19","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\/15418","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=15418"}],"version-history":[{"count":0,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/15418\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/15419"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=15418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=15418"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=15418"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=15418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}