{"id":22708,"date":"2021-03-25T07:54:00","date_gmt":"2021-03-25T07:54:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/results-are-not-the-biggest-factor-in-data-science-success\/"},"modified":"2023-08-29T10:41:48","modified_gmt":"2023-08-29T10:41:48","slug":"results-are-not-the-biggest-factor-in-data-science-success","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/results-are-not-the-biggest-factor-in-data-science-success\/","title":{"rendered":"Results Are Not The Biggest Factor In Data Science Success"},"content":{"rendered":"\t\t
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The most important factor in determining if a given data science project will succeed or fail in a business environment is not<\/em><\/strong> the quality of the results. In an ideal world, that would be the case, but unfortunately it isn\u2019t true in the real world that we live in. I know I have some explaining to do with that comment, so read on!<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Solid Results Don\u2019t Even Get You Halfway Home<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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First of all, I\u2019ll make clear that absolutely, positively producing accurate results is crucially important<\/strong>. Every professional creating data science processes must ensure that results are valid and accurate every time. At the end of the day, however, from the viewpoint of the people who sponsor data science and analytics projects, the results themselves are at most<\/em> 50 percent of the criteria that will determine if they view the project a success.<\/p>\n

At least<\/em> 50 percent of the success of a project will be based on how well the results are put together in a presentation and how that presentation is delivered. Is the presenter able to position the results effectively? Can the presenter interpret things in a way that makes sense to the audience so they will be comfortable taking action? When building a data science process, you can\u2019t just focus on generating compelling results, however tempting that may be. You must also leave time to focus on the right interpretation, positioning, and selling of the results to the businesspeople who asked for the analysis.<\/p>\n

A business team won\u2019t care about your weeks of effort and all the gory details you waded through to get to the results. They care about the results. You must get the results across to the business sponsors effectively, or the results may as well not exist<\/strong>. Producing great results is necessary, but not sufficient to having the project viewed as a success.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Raw Results Can Seem Dull<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In the marketing industry<\/a>, models can be used to identify which customers should be contacted with an offer. After the offers are made, the lift from those efforts can be measured exactly. If it worked, more of the same can be done. If it didn\u2019t work, the team can stop and try something else. Great mathematically and financially, but not very exciting in and of itself.<\/p>\n

One of the biggest budgets on a lot of companies\u2019 books is mass advertising in the form of TV, radio, newspapers, and so on. Such media do have an impact. However, it is nearly impossible to get a highly accurate measure of that impact, the methods to do so are notoriously tricky, <\/strong>and, in my opinion based on my experience using them, dicey at best. Yet advertising is still pervasive, even when there are other, more measurable options available that budgets could shift to. Why is that?<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A Lesson From The Advertising Industry<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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What do ad agencies do when they pitch their plans? They show up with a multimedia presentation. They\u2019ll have music. They\u2019ll have video. They\u2019ll have new catch phrases. They\u2019ll talk about how much customers will love the brand. They\u2019ll get everyone tearing up with a touching scene of a family interacting with a product in a way that makes their life better. The agency will get the audience so pumped up about the story they\u2019ve heard that they\u2019re ready to sign up. Even the fact that the advertising can\u2019t be measured cleanly on the back end that doesn\u2019t matter much, because the audience has bought into the vision and the excitement of what that ad agency is suggesting they do. In other words, agencies do a great job telling their story<\/strong>.<\/p>\n

The intention is not to pick on ad agencies (please don\u2019t send the nasty letters). Rather, the intent is to compliment them! While advertising is not nearly as measurable as some other activities, it has maintained a huge share of expenditures. That is in part because of the advertising industry\u2019s ability to make what they do compelling to their sponsors through a great story. Advertising agencies fully understand and leverage the power of presentation and communication, and data science and analytics teams can learn a lot from those ad agencies. Imagine how successful a data science project can be if highly measurable actions based on solid analytics are paired with the excitement level that advertising activities instill in the business community<\/strong>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Results Are Necessary, But Not Sufficient<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It takes hard work and practice to develop the ability to distill a lengthy and complex set of results down to digestible sound bites. At times, you will feel you are watering things down too much. You will feel like you are focusing too much on fluffy slides instead of meaty algorithms. While it is necessary to have the details to defend the findings available, the details shouldn\u2019t be brought out until necessary. The business team\u2019s eyes will glaze over, they\u2019ll tune out, and they won\u2019t act on the results if discussion gets too technical. You must deliver results in a way that keeps the sponsors engaged and interested. To do that, you need to put in the effort to create a compelling presentation and you must accept that to do that you will have to stop chasing a little extra lift in favor of making sure you present successfully. Producing great results is necessary, but not sufficient<\/strong> to having your data science project viewed as a success.<\/p>\n

Note: This blog is based on content from chapter 8 of my book <\/em>Taming The Big Data Tidal Wave<\/em><\/a>, Wiley 2012.<\/em><\/p>\n

Originally published by the <\/em>International Institute for Analytics<\/em><\/a><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

The most important factor in determining if a given data science project will succeed or fail in a business environment is not the quality of the results.<\/p>\n","protected":false},"author":1089,"featured_media":19031,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[120,97,94,1457],"ppma_author":[3791],"class_list":["post-22708","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-analytics","tag-artificial-intelligence","tag-data-science","tag-results"],"authors":[{"term_id":3791,"user_id":1089,"is_guest":0,"slug":"bill-franks","display_name":"Bill Franks","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/Bill-Franks-150x150.jpeg","user_url":"http:\/\/bill-franks.com\/index.html","last_name":"Franks","first_name":"Bill","job_title":"","description":"Bill Franks is the Director of the Center For Statistics and Analytical Research within the School of Data Science and Analytics at Kennesaw State University. He is also Chief Analytics Officer for The International Institute For Analytics (IIA) and serves on the advisory board of Aspirent, DataSeers, and Kavi Global."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22708","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/1089"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=22708"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22708\/revisions"}],"predecessor-version":[{"id":31793,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/22708\/revisions\/31793"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/19031"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=22708"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=22708"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=22708"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=22708"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}