{"id":3334,"date":"2019-02-27T12:11:00","date_gmt":"2019-02-27T11:11:00","guid":{"rendered":"urn:uuid:2888b5ae-e73f-4eb3-aada-42da2e923b96"},"modified":"2024-05-03T16:45:01","modified_gmt":"2024-05-03T14:45:01","slug":"digital-shift-why-do-data-science-projects-fail","status":"publish","type":"post","link":"https:\/\/www.datahouse.ch\/en\/blog\/digital-shift-why-do-data-science-projects-fail\/","title":{"rendered":"Digital Shift: why do data science projects fail?"},"content":{"rendered":"\n
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There are many reasons why data science projects fail. You can find out more from the slides of Daniel Meister\u2019s presen\u00adtation series entitled \u201cDigital Shift\u201d.<\/p>\n\n\n\n

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Abbildung 1: Venn-Diagramm der k\u00fcnstlichen Intelligenz <\/em><\/figcaption><\/figure><\/div>\n<\/div>\n\n\n\n
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One thing is clear: a lot can go wrong in the initial phase of a business analysis, often because the focus is wrong. The question must therefore always clearly be: \u201cWhere can we actually optimise our (core) business further? What additional infor\u00admation do we require for this? Do we have a realistic chance of getting this data?\u201d The complete summary of the event can be found under Digital Shift<\/a>. You can download the presen\u00adtation here<\/a>.<\/p>\n<\/div>\n\n\n\n

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There are many reasons why data science projects fail. You can find out more from the slides of Daniel Meister\u2019s presen\u00adtation series entitled \u201cDigital Shift\u201d. One thing is clear: a lot can go wrong in the initial phase of a business analysis, often because the focus is wrong. The question must therefore always clearly be: … Continued<\/a><\/p>\n","protected":false},"author":15,"featured_media":4265,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[81,29,115,68,70],"tags":[],"class_list":["post-3334","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-data-science","category-data-science-en","category-diverse","category-news"],"acf":[],"image":{"url":"https:\/\/www.datahouse.ch\/wp-content\/uploads\/Blog_190227_DE_datashift_data_science_Banner-grafik.webp"},"_links":{"self":[{"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/posts\/3334","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/comments?post=3334"}],"version-history":[{"count":2,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/posts\/3334\/revisions"}],"predecessor-version":[{"id":4404,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/posts\/3334\/revisions\/4404"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/media\/4265"}],"wp:attachment":[{"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/media?parent=3334"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/categories?post=3334"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datahouse.ch\/en\/wp-json\/wp\/v2\/tags?post=3334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}