{"id":5673,"date":"2020-06-25T16:29:05","date_gmt":"2020-06-25T16:29:05","guid":{"rendered":"http:\/\/etereman.com\/remanu\/?p=5673"},"modified":"2020-06-25T16:56:28","modified_gmt":"2020-06-25T16:56:28","slug":"with-great-data-comes-great-responsibility","status":"publish","type":"post","link":"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/","title":{"rendered":"With Great Data Comes Great Responsibility"},"content":{"rendered":"\t\t
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Insights are often born out of frustration.  I routinely urge my colleagues to pay attention to those moments of repetitive frustration and annoyance and use them to identify opportunities for improvement.  Not all are actionable, but I find that most are, and the solutions can be simple. <\/p>\n

I\u2019ve recently felt a growing disdain for the manner in which information is presented.  Ever opened a spreadsheet someone has sent you and spent a few minutes guessing at what the header abbreviations mean and how the data relates? Have you ever asked yourself, \u201cWhat am I looking at here? Do I need a secret decoder ring?!\u201d Worse yet, is the data used to deliberately mislead? (Watch a few commercials, you\u2019re bound to see it).<\/p>\n

Here are the problems I see regularly with the data the world presents to me:<\/p>\n

    \n
  • Bias<\/strong> \u2013 A lot of the data we see is architected purposely to make us think the way the creator wants us to. Beware of the objectives and motives of the content creator and pay special attention to the exact way the statistics are described.<\/li>\n
  • Sourcing\/Quality of Data<\/strong> \u2013 Bad data equals bad intelligence and faulty conclusions. This often happens by accident because of combining different data sets or working with data sets you don\u2019t know well. <\/li>\n
  • Poor Labeling <\/strong>\u2013 Use of abbreviations, unique terminology and proprietary metrics all add confusion. If you need an extensive data dictionary to read reports, chances are good that your data is too complicated.<\/li>\n
  • Assumptions <\/strong>\u2013 When the people gathering the data think they know what it will mean before it\u2019s collected, they tend to see the data they want to. Even with the best intentions, data can be explained as meaning something it doesn\u2019t.<\/li>\n<\/ul>\n

    There is a common theme in these problems, the intentional or accidental misrepresentation of what the data says. This is particularly dangerous when it\u2019s based in numeric, objective data. People tend to see precision as the same as correctness. I can tell you that 1.2% of people eat their ice cream cones from the bottom up and many would be inclined to believe me because the number is just so exact. But, what if that\u2019s just my way of interpreting that, when surveyed, 98.8% of people say they eat their ice cream cones from the top down?*<\/p>\n

    As a member of our BI (Business Intelligence) team I know how difficult it is to avoid these pitfalls.  Even when trying diligently to seek truths from the numbers, our preconceived notions have an effect on our interpretations.  It\u2019s a slippery slope.  When asking questions that have never been asked before, there are seldom the exact data sets you\u2019d need to answer them neatly. That forces some compromise, assumption, and interpretation.  But, none of us can afford to wait for ideal conditions and perfect data in order to make our decisions (we\u2019d be waiting forever).  To help offset these inevitable challenges, here are some things I wish everyone who shares information with the world would consider:<\/p>\n

      \n
    • Be transparent<\/li>\n
    • Use Visual Representation<\/li>\n
    • Summarize Findings and Insights<\/li>\n
    • Expose Weaknesses in Inputs<\/li>\n<\/ul>\n

      There\u2019s a common theme to these, too \u2013 namely, be transparently honest with your data and presentation. As the world creates and collects more and more data, we\u2019ll all get better at spotting and calling out mistakes. Our responsibility as data aggregators and interpreters is to be good stewards of the data. We must ensure we are wary of the mistakes people can make with (with good intentions or otherwise) and to provide our method so that our results can be reviewed as needed. Get ahead of the curve and give people what we really need, well-presented information in concise and transparent form \u2013 and be prepared to adjust your interpretations.   <\/p>\n

      <\/p>\n

      * First, I assumed that if people don\u2019t eat their ice cream from the top down, then they must eat it from the bottom up \u2013 but that wasn\u2019t what the statistic said. And second, where did that data come from? For all we know, some blogger could have just made it up-<\/em><\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      Insights are often born out of frustration.  I routinely urge my colleagues to pay attention to those moments of repetitive frustration and annoyance and use them to identify opportunities for improvement.  Not all are actionable, but I find that most are, and the solutions can be simple.  I\u2019ve recently felt a growing disdain for the […]<\/p>\n","protected":false},"author":36,"featured_media":5674,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false},"categories":[5,12],"tags":[918,486,958,959,961,960],"acf":[],"yoast_head":"\nWith Great Data Comes Great Responsibility - REMANU<\/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:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"With Great Data Comes Great Responsibility - REMANU\" \/>\n<meta property=\"og:description\" content=\"Insights are often born out of frustration.  I routinely urge my colleagues to pay attention to those moments of repetitive frustration and annoyance and use them to identify opportunities for improvement.  Not all are actionable, but I find that most are, and the solutions can be simple.  I\u2019ve recently felt a growing disdain for the […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/\" \/>\n<meta property=\"og:site_name\" content=\"REMANU\" \/>\n<meta property=\"article:published_time\" content=\"2020-06-25T16:29:05+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-06-25T16:56:28+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/etereman.com\/remanu\/wp-content\/uploads\/sites\/2\/2020\/06\/RU464.png\" \/>\n\t<meta property=\"og:image:width\" content=\"700\" \/>\n\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"The Rhythm of Reman\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"The Disrupter \" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/\"},\"author\":{\"name\":\"The Rhythm of Reman\",\"@id\":\"https:\/\/etereman.com\/remanu\/#\/schema\/person\/324daf6b244ef5d3c67e0ec761579224\"},\"headline\":\"With Great Data Comes Great Responsibility\",\"datePublished\":\"2020-06-25T16:29:05+00:00\",\"dateModified\":\"2020-06-25T16:56:28+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/\"},\"wordCount\":702,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/etereman.com\/remanu\/#organization\"},\"keywords\":[\"bias\",\"data\",\"data accuracy\",\"interpretation\",\"quality of data\",\"visual representation\"],\"articleSection\":[\"management\",\"Reman U articles\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/\",\"url\":\"https:\/\/etereman.com\/remanu\/2020\/06\/25\/with-great-data-comes-great-responsibility\/\",\"name\":\"With Great Data Comes Great Responsibility - 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