{"id":130,"date":"2014-08-29T04:32:58","date_gmt":"2014-08-29T04:32:58","guid":{"rendered":"http:\/\/blogs.sps.ed.ac.uk\/digistis\/?p=130"},"modified":"2014-10-13T10:01:54","modified_gmt":"2014-10-13T10:01:54","slug":"thinking-about-my-producing-data-talk","status":"publish","type":"post","link":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/2014\/08\/29\/thinking-about-my-producing-data-talk\/","title":{"rendered":"Thinking about my Producing Data talk"},"content":{"rendered":"

\"producing\"<\/a><\/p>\n

I am thinking about my ten minute introduction for next week’s Producing Data<\/a> event.<\/p>\n

I want to talk about data visualisation as a profession and\u00a0its position in the data supply chain. I am reminiscent as I write of Micheal Pryke’s very\u00a0interesting paper on the “money eye<\/a>”\u00a0when he says “\u201cNumbers are not everything\u201d. Interestingly,\u00a0\u00a0Pryke’s is one of the few papers where\u00a0fieldworker ventures into the realm of computational\u00a0architecture groups\u00a0that are responsible of shaping the way (analyst and\u00a0ourselves) perceive markets.<\/p>\n

\"volatility<\/a><\/p>\n

Next tuesday 2nd of September we\u00a0will have the privilege of hosting one of them. I met Hermann Zchiegner of\u00a0www.two-n.com<\/a>\u00a0thanks to our common\u00a0friend and colleague Gordon Haywood in 2012. Hermann was\u00a0Edinburgh to work to a new data visualisation\u00a0project at Wood MacKenzie and I interviewed him. Here is one\u00a0excerpt:<\/p>\n

Hermann\u00a0Zchiegner<\/strong>: “Working with data you are working with something that is\u00a0there, that is factual. Applying a visualization layer on top\u00a0of data brings a certain bias with it. You know, based on the\u00a0same dataset, it can create two very different sorts of\u00a0pictures. This is one of the biggest challenges of data\u00a0visualization anyway\u2026to have as much as you can an unbiased\u00a0view, unless is serving the purpose. I want to help me making\u00a0an argument with these sets of data. That\u2019s a different kind\u00a0of story. There is this unbiased view: give me the data, let\u00a0me help you understand the data, let the data speak to you.\u00a0The role of the dataviz guy is in a certain way very much so a\u00a0creative process because you are creating something out of the\u00a0context of the data that in itself becomes something new, that\u00a0stands for itself. There is this big difference between data\u00a0visualization and this big thing that is becoming more and\u00a0more predominant that is infographics. Data visualization\u00a0relies of data sets where we have data notes in the hundreds\u00a0of thousands. Infographics usually deals with small datasets\u00a0and its purpose is always artistic: I drawing you a picture of\u00a0a fact. Whilst data visualization is open ended because you\u00a0are just creating a framework for the data to speak but you\u00a0are not creating an end product. You are really just creating\u00a0the framework.”<\/p>\n

What I really wanted to understand at that time was when and to what extent\u00a0pictures bias numbers. I really wanted to find an evidence\u00a0of this performativity<\/em>. Hermann’s words hinted at that\u00a0direction.<\/p>\n

My research progressed from that into the study of how even\u00a0the simple figurations – the dot, the line, the list, the two by\u00a0two matrix make a difference. I read with great enjoyment\u00a0Micheal Lynch description of the ‘device of the dot’, Tim Ingold\u00a0book – an entire book! – on the line. And Chris Carter and Mark\u00a0Kornberger’s analysis of how a list can make a difference,\u00a0creating competition between previously unrelated entities and\u00a0magnifying irrelevant difference (such as the difference\u00a0between ‘first’ and ‘second’).<\/p>\n

I worked over the last few\u00a0years on the idea that even big numbers and complex calculations\u00a0need to come down to something people can communicate and\u00a0understand. Something that they can use to take decisions.My own empirical field of research being the IT market – IT\u00a0business consultancy and analysis – I started doing research in\u00a0collaboration with Neil Pollock on the (in)famous Magic Quadrant<\/strong>.<\/p>\n

\"gartner-e-discovery-magic-quadrant-2012\"<\/a><\/p>\n

\n

A very simple but very influential figuration in business\u00a0analysis. What people see in it? I found that watching the\u00a0Magic Quadrant the trained eye can recognise at a glance that\u00a0a mature market has the shape of a “rugby ball”. I also\u00a0discover that to be effective, a certain (very well defined)\u00a0number of entities need to be represented. When not enough\u00a0entities (vendors) are naturally available in a market, the\u00a0“range” need to be somehow produced. We therefore also\u00a0discovered that the matrix is not only about taking away,\u00a0simplifying. it is also about adding sometime.<\/p>\n

However, fascination for more complex\u00a0visualisations and dynamic datasets never abandoned me. Since my last meeting\u00a0with Gordon I also drafted a new course, part of our new\u00a0distance learning programme in “Making Use of Digital\u00a0Research<\/strong><\/a>” (yes thanks for finding the title creative!). The\u00a0course is about “Understanding Data Visualisation” and will host talks from a number of data designers. There we\u00a0will discuss with students the difference between pie chart,\u00a0bar chart, heat maps and all the most popular figurations to\u00a0represent dynamic datasets.\u00a0Also Gartner seems to have\u00a0turned\u00a0to a more dynamic magic quadrant <\/a>nowadays!<\/p>\n

Let’s see where we are\u00a0when Hermann visits us next in 2016!<\/p>\n

Ciao,<\/strong><\/p>\n

Gian Marco<\/strong><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

I am thinking about my ten minute introduction for next week’s Producing Data event. I want to talk about data visualisation as a profession and\u00a0its position in the data supply chain. I am reminiscent as I write of Micheal Pryke’s very\u00a0interesting paper on the “money eye”\u00a0when he says “\u201cNumbers are not everything\u201d. Interestingly,\u00a0\u00a0Pryke’s is one   …Continue Reading<\/a><\/p>\n","protected":false},"author":130,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/posts\/130"}],"collection":[{"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/users\/130"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/comments?post=130"}],"version-history":[{"count":24,"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/posts\/130\/revisions"}],"predecessor-version":[{"id":136,"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/posts\/130\/revisions\/136"}],"wp:attachment":[{"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/media?parent=130"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/categories?post=130"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sps.ed.ac.uk\/digistis\/wp-json\/wp\/v2\/tags?post=130"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}