{"id":553,"date":"2017-12-01T04:10:15","date_gmt":"2017-12-01T04:10:15","guid":{"rendered":"http:\/\/kusuaks7\/?p=158"},"modified":"2025-03-31T16:17:49","modified_gmt":"2025-03-31T16:17:49","slug":"five-reasons-why-your-data-science-project-could-fail-and-what-you-can-do-to-avoid-it","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/five-reasons-why-your-data-science-project-could-fail-and-what-you-can-do-to-avoid-it\/","title":{"rendered":"Five Reasons Why Your Data Science Project Could Fail \u2013 And What You Can Do to Avoid It"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to learn Data Science? Browse courses<\/a>\u00a0like\u00a0Data Science Training and Certification<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\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
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\n\t\t\t\t\t\t\t\t\tLosing sight of the BIG picture<\/strong>\u00a0\u2013 As data scientists, our job is to \u201cextract a signal from noise,\u201d and be able to glean insights hidden in the data which will ultimately have an impact on business. Hence,\u00a0the inability to zoom in and out of the problem being solved and having an obsession with accuracy and depth at the cost of breadth can cause projects to fail. As an example, you may get 85% accuracy from your models in a month but moving from 85% to 90% could take you a year. For every iteration- is it really worth the cost and effort to work towards being super detailed and accurate? To find the right balance of depth and breadth, get feedback from your stakeholders quickly. Engage them early to ensure your recommendations generate value for them and fit into the larger scheme of things.\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
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\n\t\t\t\t\t\t\t\t\t\nLack of engagement with key stakeholders<\/strong>\u00a0\u2013 The right people, processes and culture is bedrock for building viable frameworks and infrastructure for data science. If you hear quotes like \u2013 \u201cwe have some data science folks but no one really knows what they do,\u201d then it signals a lack of executive sponsorship and engagement with stakeholders.\u00a0 Just hiring data scientists is not enough \u2013 this talent needs to be integrated into the existing organization and new structures that enable value creation, need to be invented if required. One approach could be that data science professionals are tied to business units, and share responsibility for BU performance. Whatever approach is taken, and like most strategic initiatives \u2013 success is driven by executive sponsorship and buy-in from senior management.\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
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\n\t\t\t\t\t\t\t\t\tPutting the \u2018How\u2019 before the \u2018Why\u2019<\/strong>\u00a0\u2013 When you begin with scoping a problem, freeze the \u201cwhat\u201d and \u201cwhy\u201d first. Problem formulation meetings that start with \u201dhow\u201d become inevitably shortsighted.\u00a0 This can happen a lot if you have a group of bright, technically inclined folks raring to dive into the latest tools and technologies at the word go. However, good problem statements\u00a0always\u00a0capture the \u201ctrigger\u201d which caused the problem to surface. Putting the data before the question can cause you to be lost in the enormity of the data and tools being used. \u00a0This can also lead you away from solving the right problem.\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
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\n\t\t\t\t\t\t\t\t\tNot solving the\u00a0right\u00a0problem<\/strong>\u00a0\u2013 One of my favorite quotes from the statistician John Tukey captures the essence of this issue perfectly \u2013 \u201cAn approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem\u201c. This challenge manifests itself in many forms \u2013 if you don\u2019t know what problem to solve, how does it fit in the overall scheme of things? \u00a0Avoid this by asking for a use case and getting diverse opinions. You need to identify the problem and solve it in a manner that will result in the right outcome.\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
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\n\t\t\t\t\t\t\t\t\tHiring Data Scientists who are Unicorns<\/strong>\u00a0\u2013 Data science is a field that requires an interdisciplinary skillset. You need to be good at math and statistics, which yields a foundation of methods to analyze and interpret data. Domain knowledge is required to understand the data and the (business) processes that shall benefit from the analysis. Coding is a prerequisite to bring the theory to action. It is hard to find one person who has all of these capabilities. Unicorns don\u2019t exist, or are extremely rare to come by. Hence, instead of struggling to hire them, build diverse teams. Data science is a team sport, and constructing a team with a strong combination of these skills will help you avoid getting into a hiring quagmire!\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
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\n\t\t\t\t\t\t\t\t\tOriginally posted at insideBIGDATA<\/a>\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":"

Ready to learn Data Science? Browse courses\u00a0like\u00a0Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab.Losing sight of the BIG picture\u00a0\u2013 As data scientists, our job is to \u201cextract a signal from noise,\u201d and be able to glean insights hidden in the data which will ultimately have an impact<\/p>\n","protected":false},"author":106,"featured_media":3071,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[1652],"class_list":["post-553","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":1652,"user_id":106,"is_guest":0,"slug":"afrozy-ara","display_name":"Afrozy Ara","avatar_url":{"url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/medium_066fb4d3-e4e8-410a-a8d6-5cff4a15e189.png","url2x":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/medium_066fb4d3-e4e8-410a-a8d6-5cff4a15e189.png"},"user_url":"","last_name":"Ara","first_name":"Afrozy","job_title":"","description":"Afrozy Ara<\/a> is the Head of Data Science practice at Incedo<\/a>. Her team partners with clients across FS, Pharma and Telecom to solve their most challenging Data Science problems leveraging disruptive technologies like NLP, ML and AI.  Prior to Incedo, she was an Engagement manager at Mu Sigma leading global engagements for Fortune 500 and hyper growth clients in the West coast."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/553","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\/106"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=553"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/553\/revisions"}],"predecessor-version":[{"id":37527,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/553\/revisions\/37527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3071"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=553"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=553"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=553"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}