{"id":25323,"date":"2019-03-20T11:47:59","date_gmt":"2019-03-20T11:47:59","guid":{"rendered":"https:\/\/digileaders.com\/?p=25323"},"modified":"2019-12-23T16:39:35","modified_gmt":"2019-12-23T16:39:35","slug":"key-ingredients-to-being-data-driven","status":"publish","type":"post","link":"https:\/\/digileaders.com\/key-ingredients-to-being-data-driven\/","title":{"rendered":"Key ingredients to being data driven"},"content":{"rendered":"<p id=\"8008\" class=\"graf graf--p graf-after--figure\">Companies love to exclaim \u201cwe\u2019re data driven\u201d. There are obvious benefits to being a data driven organisation, and everyone nowadays has more data than they can shake a stick at. But what exactly does an organisation need to be \u201cdata driven\u201d?<\/p>\n<p id=\"eedf\" class=\"graf graf--p graf-after--p\">Just because you have a ton of data, and you\u2019ve hired people to analyse it or build models, does that make you data driven? No. That\u2019s not enough.<\/p>\n<p id=\"6559\" class=\"graf graf--p graf-after--p\">Although we think a lot about data and how to use it. Being data driven needs to be a priority at the executive level and become part of the culture of the organisation; more so than simply having a team with the necessary capabilities.<\/p>\n<p id=\"6c34\" class=\"graf graf--p graf-after--p\">Here are the baseline qualities that I believe are necessary to be effective in your \u201cdata driven-ness\u201d. Now I\u2019m making up words.<\/p>\n<h4 id=\"a581\" class=\"graf graf--h3 graf-after--p\">To be data\u00a0driven:<\/h4>\n<ul class=\"postList\">\n<li id=\"ac0f\" class=\"graf graf--li graf-after--h3\">Test design and analysis is owned by analytics\/data science teams.<\/li>\n<li id=\"73a4\" class=\"graf graf--li graf-after--li\">Dashboards are already in place that give stakeholders self-serve access to key metrics. (Otherwise you\u2019ll have low value ad-hoc requests to pull these metrics, and it\u2019ll be a time sink.)<\/li>\n<li id=\"b09e\" class=\"graf graf--li graf-after--li\">Analytics\/Data Science teams<strong class=\"markup--strong markup--li-strong\">\u00a0collaborate<\/strong>\u00a0with the business to understand the problem and devise an appropriate methodology.<\/li>\n<li id=\"f54b\" class=\"graf graf--li graf-after--li\">Data governance and consistent usage of data definitions across departments\/the organisation.<\/li>\n<li id=\"7073\" class=\"graf graf--li graf-after--li\">You have a data strategy.<\/li>\n<\/ul>\n<p id=\"0b2d\" class=\"graf graf--p graf-after--li\">You\u2019ll notice that there is a lack of fancy hype buzzwords above. You don\u2019t need to be \u201cleveraging AI\u201d or calling things AI that are in fact hypothesis tests, business logic, or simple regression.<\/p>\n<p id=\"6189\" class=\"graf graf--p graf-after--p\">I don\u2019t believe fancy models are required to consider yourself data driven. A number of the points listed above are references to the attitudes of the organisation and how they <em class=\"markup--em markup--p-em\">partner and collaborate<\/em>\u00a0with analytics and data science teams\u00a0. I love building models as much as the next data scientist, but you can\u2019t build next level intelligence on a non-existent foundation.<\/p>\n<p id=\"ce65\" class=\"graf graf--p graf-after--p\">To clarify, I\u2019m not saying every decision in the organisation needs to be driven by data to be data driven. In particular, if you\u2019re going to make a strategic decision regardless of the results of a test or analysis, then you should skip doing that test. I\u2019m a big advocate of only allocating the resources to a project if you\u2019re actually going to USE the results to inform the decision.<\/p>\n<p id=\"b40d\" class=\"graf graf--p graf-after--p\">Let\u2019s take a look at the points from above.<\/p>\n<h4 id=\"f81f\" class=\"graf graf--h3 graf-after--p\">Test design and analysis is owned by analytics\/data science\u00a0teams:<\/h4>\n<p id=\"552e\" class=\"graf graf--p graf-after--h3\">Although data science and analytics teams often come up with fantastic ideas for testing. There are also many ideas that come out of a department that is not in analytics. For instance, in eCommerce the marketing team will have many ideas for new offers. The site team may want to test a change to the UI. This sometimes gets communicated to the data teams as \u201cwe\u2019d like to test \u201cthis thing, this way\u201d. And although these non analytics teams have tremendous skill in marketing and site design, and understand the power of an A\/B test; they often do not understand the different trade-offs between effect size, sample size, solid test design, etc.<\/p>\n<p id=\"1a3b\" class=\"graf graf--p graf-after--p\">I\u2019ve been in the situation more than once at more than one company where I\u2019m told \u201cwe understand your concerns, but we\u2019re going to do it our way anyways.\u201d And this is their call to make, since in these instances those departments have technically \u201cowned\u201d test design. However, the data resulting from these tests is often not able to be analysed. So although we did it their way, the ending result did not answer any questions. Time was wasted.<\/p>\n<h4 id=\"2ce4\" class=\"graf graf--h3 graf-after--p\">Dashboarding is in\u00a0place:<\/h4>\n<p id=\"ba14\" class=\"graf graf--p graf-after--h3\">This is a true foundational step. So much time is wasted if you have analysts pulling the same numbers every month manually, or on an ad-hoc basis. This information can be automated, stakeholders can be given a tour of the dashboards, and then you won\u2019t be receiving questions like \u201cwhat does attrition look like month over month by acquisition channel?\u201d It\u2019s in the dashboard and stakeholders can look at it themselves. The time saved can be allocated to diving deep into much more interesting and though provoking questions rather than pulling simple KPIs.<\/p>\n<h4 id=\"6e78\" class=\"graf graf--h3 graf-after--p\">Analytics\/Data Science teams collaborate with the business on defining the problems:<\/h4>\n<p id=\"7985\" class=\"graf graf--p graf-after--h3\">This relationship takes work, because it is a relationship. Senior leaders need to make it clear that a data-driven approach is a priority for this to work. In addition, analytics often needs to invite themselves to meetings that they weren\u2019t originally invited to. Analytics needs to be asking the right questions and guiding analysis in the right direction to earn this seat at the table. No relationship builds over night, but this is a win-win for everyone. Nothing is more frustrating than pulling data when you\u2019re not sure what problem the business is trying to solve. It\u2019s Pandoras Box. You pull the data they asked for, it doesn\u2019t answer the question, so the business asks you to pull them more data. Stop. Sit down, discuss the problem, and let the business know that you\u2019re here to help.<\/p>\n<h4 id=\"ea49\" class=\"graf graf--h3 graf-after--p\">Data governance and consistent usage of data definitions across departments\/the organisation:<\/h4>\n<p id=\"bb59\" class=\"graf graf--p graf-after--h3\">This one may require a huge overhaul of how things are currently being calculated. The channel team, the product team, the site team, other teams, they may all be calculating things differently if the business hasn\u2019t communicated an accepted definition. These definitions aren\u2019t necessarily determined by analytics themselves, they\u2019re agreed upon. For an established business that has done a lot of growing but not as much governance can feel the pain of trying to wrangle everyone into using consistent definitions. But if two people try to do the same analysis and come up with different numbers you\u2019ve got problems. This is again a foundation that is required for you to be able to move forward and work on cooler higher-value projects, but can\u2019t if you\u2019re spending your time reconciling numbers between teams.<\/p>\n<h4 id=\"1c07\" class=\"graf graf--h3 graf-after--p\">You have a data strategy:<\/h4>\n<p id=\"be5d\" class=\"graf graf--p graf-after--h3\">This data strategy is going to be driven by the business strategy. The strategy is going to have goals and be measurable. The analyses you plan for has a strong use case. People don\u2019t just come out of the woodwork asking for analysis that doesn\u2019t align to the larger priorities of the business. Things like \u201cdo we optimise our ad spend or try to tackle our retention problem first?\u201d comes down to expected dollars for the business. Analytics doesn\u2019t get side-tracked answering lower value questions when they should be working on the problems that will save the business the most money.<\/p>\n<h4 id=\"4130\" class=\"graf graf--h3 graf-after--p\">In summary:<\/h4>\n<p id=\"512a\" class=\"graf graf--p graf-after--h3\">I hope you found this article helpful. Being data driven will obviously help you to make better use of your data. However, becoming data driven involves putting processes into place and having agreement about who owns what at the executive level. It\u2019s worth it, but it doesn\u2019t happen over night. If you\u2019re not yet data driven, I wish you luck on your journey to get there. Your analysts and data scientists will thank you.<\/p>\n<hr \/>\n<p>Originally posted <a href=\"https:\/\/towardsdatascience.com\/key-ingredients-to-being-data-driven-f76ab8140bc\">here<\/a><\/p>\n<p class=\"p1\" style=\"text-align: center;\"><span class=\"btn-container\"><a class=\"btn btn-md btn-primary\" href=\"#\">More thought leadership <i class=\"far fa-caret-right\"><\/i><\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Companies love to exclaim \u201cwe\u2019re data driven\u201d. There are obvious benefits to being a data driven organisation, and everyone nowadays has more data than they can shake a stick at. But what exactly does an organisation need to be \u201cdata driven\u201d? Just because you have [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":25326,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"class_list":["post-25323","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","topic-ai","topic-data-analytics","topic-transformation"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Key ingredients to being data driven | Digital Leaders<\/title>\n<meta name=\"description\" content=\"There are obvious benefits to being a data driven organisation, and everyone nowadays has more data than they can shake a stick at.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/digileaders.com\/key-ingredients-to-being-data-driven\/\" \/>\n<meta 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