{"version":"1.0","provider_name":"insideBIGDATA","provider_url":"https:\/\/insidebigdata.com","author_name":"Contributor","author_url":"https:\/\/insidebigdata.com\/author\/contributor\/","title":"Who Done It? 3 Possible Suspects in this Halloween\u2019s Bad Data Horror Movie, And How Data Teams Can Make It Out Alive - insideBIGDATA","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"YFLotq6Yqv\"><a href=\"https:\/\/insidebigdata.com\/2023\/10\/31\/who-done-it-3-possible-suspects-in-this-halloweens-bad-data-horror-movie-and-how-data-teams-can-make-it-out-alive\/\">Who Done It? 3 Possible Suspects in this Halloween\u2019s Bad Data Horror Movie, And How Data Teams Can Make It Out Alive<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/insidebigdata.com\/2023\/10\/31\/who-done-it-3-possible-suspects-in-this-halloweens-bad-data-horror-movie-and-how-data-teams-can-make-it-out-alive\/embed\/#?secret=YFLotq6Yqv\" width=\"600\" height=\"338\" title=\"&#8220;Who Done It? 3 Possible Suspects in this Halloween\u2019s Bad Data Horror Movie, And How Data Teams Can Make It Out Alive&#8221; &#8212; insideBIGDATA\" data-secret=\"YFLotq6Yqv\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/*! This file is auto-generated *\/\n!function(c,d){\"use strict\";var e=!1,o=!1;if(d.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},c.wp.receiveEmbedMessage);else if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(!t);else if(!(t.secret||t.message||t.value));else if(\/[^a-zA-Z0-9]\/.test(t.secret));else{for(var r,s,a,i=d.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),n=d.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),o=new RegExp(\"^https?:$\",\"i\"),l=0;l<n.length;l++)n[l].style.display=\"none\";for(l=0;l<i.length;l++)if(r=i[l],e.source!==r.contentWindow);else{if(r.removeAttribute(\"style\"),\"height\"===t.message){if(1e3<(s=parseInt(t.value,10)))s=1e3;else if(~~s<200)s=200;r.height=s}if(\"link\"===t.message)if(s=d.createElement(\"a\"),a=d.createElement(\"a\"),s.href=r.getAttribute(\"src\"),a.href=t.value,!o.test(a.protocol));else if(a.host===s.host)if(d.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener(\"message\",c.wp.receiveEmbedMessage,!1),d.addEventListener(\"DOMContentLoaded\",t,!1),c.addEventListener(\"load\",t,!1);function t(){if(o);else{o=!0;for(var e,t,r,s=-1!==navigator.appVersion.indexOf(\"MSIE 10\"),a=!!navigator.userAgent.match(\/Trident.*rv:11\\.\/),i=d.querySelectorAll(\"iframe.wp-embedded-content\"),n=0;n<i.length;n++){if(!(r=(t=i[n]).getAttribute(\"data-secret\")))r=Math.random().toString(36).substr(2,10),t.src+=\"#?secret=\"+r,t.setAttribute(\"data-secret\",r);if(s||a)(e=t.cloneNode(!0)).removeAttribute(\"security\"),t.parentNode.replaceChild(e,t);t.contentWindow.postMessage({message:\"ready\",secret:r},\"*\")}}}}(window,document);\n<\/script>\n","thumbnail_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/08\/Data_shutterstock_1055190668_special.jpg","thumbnail_width":1100,"thumbnail_height":550,"description":"In this contributed article, Lior Gavish, CTO and Co-Founder of\u00a0Monte Carlo, outlines some of the ways companies can erase themselves from ever appearing in these bad data horror stories, ranging from simple tips to bolster governance within their organization, to tools and best practices that will save data teams the time, hassle, and headache that comes with dealing with bad data."}