{"id":3713,"date":"2016-10-03T10:13:00","date_gmt":"2016-10-03T10:13:00","guid":{"rendered":"http:\/\/localhost\/projects\/horsesforsources\/data-chicken-egg_100316\/"},"modified":"2016-10-03T10:13:00","modified_gmt":"2016-10-03T10:13:00","slug":"data-chicken-egg_100316","status":"publish","type":"post","link":"https:\/\/www.horsesforsources.com\/data-chicken-egg_100316\/","title":{"rendered":"What came first the data chicken or the data egg? Calling time on #bloodyuselessdata"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" style=\"display: block; margin-left: auto; margin-right: auto;\" src=\"\/storage\/app\/uploads\/public\/57f\/230\/a27\/57f230a271083108362899.png\" alt=\"\" width=\"554\" height=\"353\" \/><\/p>\n<p>The modern digital world is awesome in so many ways. But sometimes I get sent a piece of data, and I just wonder <em>why<\/em>? Not to be an advert, but I recently started to use a grammar \/ spelling plug-in called <a href=\"https:\/\/www.grammarly.com\" target=\"_blank\" rel=\"noopener\">Grammarly<\/a>\u00a0(it just corrected the spelling of itself as I\u2019d forgotten to capitalise). I find it helpful as it does things like contextual spelling \u2013 so it\u2019s a bit better at spotting some of the common mistakes I make \u2013 for example, I am always typing deliverying instead of delivering which Word seems not to find.<\/p>\n<p>Anyway, I get an email from them every week giving me a run-down on how I used it that week \u2013 I have displayed one of my latest stats in the picture.<\/p>\n<p><img decoding=\"async\" style=\"display: block; margin-left: auto; margin-right: auto;\" src=\"\/storage\/app\/uploads\/public\/57f\/20a\/cc3\/57f20acc3723d296464401.png\" \/><\/p>\n<p>When I first received this, I thought that this was great \u2013 look at me I\u2019m 99% more active than everyone else. Then the following week my number of words went down 30%, and it worried me \u2013 thinking I\u2019d been just as busy. As the weeks rolled on, I have made the same number of mistakes and my stats fluctuate \u2013 but they just don\u2019t <em>help<\/em> me. It went from &#8220;That\u2019s interesting&#8221;, &#8220;Wow&#8221; to &#8220;So what?&#8221; in about two weeks. It may just be that I am not learning from my mistakes&#8230;.<\/p>\n<p>Coincidentally, I wrote about the importance of learning from mistakes in one of my last blogs (<a href=\"\/blog\/jamie-snowdon\/fail-fast-bs_090416\" target=\"_blank\" rel=\"noopener\">Why the slogan \u201cFail Fast\u201d is bullsh*t if you want to succeed with OneOffice<sup>TM<\/sup><\/a>) and I think this is where much of the data explosion is failing. We\u2019re provided with all this <em>useless\u00a0data<\/em>, but without much guidance on what to do about it, how to act on it \u2013 how to <em>improve<\/em> for the future. I think that is the issue with all the data \u2013 we just don\u2019t know what to bloody do with it!<\/p>\n<p><span style=\"font-size: 14pt; font-weight: bold; color: #000000;\">Bottom line: \u00a0To avoid #bloodyuselessdata you need a data chicken, not just a data egg<\/span><\/p>\n<p>HfS Research is calling time on bloody useless data. Pure data collection and na\u00efve analysis are table stakes in the digital world. The only way to create business value is to provide a feedback loop. The data needs to be used to inform the person in a positive way and iterate the process. Just hoarding data with no purpose is pointless (and potentially expensive) and next level of statistical reports which generate little action are almost as bad. For data to have any power it needs to inform the process and it needs to be actionable. <br \/>From now on HfS will be looking for examples of useless data and calling it out. If you have any examples, please contact me via twitter @thewizeone #bloodyuselessdata or jamie.snowdon@hfsresearch.com.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The modern digital world is awesome in so many ways. But sometimes I get sent a piece of data, and&#8230;<\/p>\n","protected":false},"author":8,"featured_media":3714,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[838,831],"tags":[132],"organization":[],"ppma_author":[1028],"class_list":["post-3713","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kpo-analytics","category-smac-and-big-data","tag-analytics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What came first the data chicken or the data egg? 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