{"id":8768,"date":"2020-07-01T07:47:04","date_gmt":"2020-07-01T07:47:04","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=8768"},"modified":"2023-11-30T15:29:59","modified_gmt":"2023-11-30T15:29:59","slug":"a-complete-data-science-team-requires-more-than-just-data-scientists","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/a-complete-data-science-team-requires-more-than-just-data-scientists\/","title":{"rendered":"A complete data science team requires more than just data scientists"},"content":{"rendered":"\t\t
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No one role is most important when it comes to a complete data science team \u2014 but some spring to mind more than others.<\/strong><\/em><\/h4>\n<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Organizations across the spectrum, from startups to enterprises, in industries from finance to healthcare, are realizing the benefits of artificial intelligence (AI).<\/p>\n\n\n\n

In fact, 14 percent of global CIOs have already deployed AI, and 48 percent will\u00a0deploy\u00a0it by 2020, according to\u00a0Gartner<\/a>. However, building out the necessary team to successfully undertake these AI projects is more complex than simply hiring data scientists.<\/p>\n\n\n\n

Yet many organizations operate under this misconception. Many don\u2019t see that the building and executing of successful, ethical, and insightful AI solutions requires a\u00a0well-rounded\u00a0data science team, and not just a few idealistic data scientists who can do it all.<\/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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For AI to deliver business value, organizations need to identify the right business use cases. Predictive insights from AI need to be made consumable through data stories, and a deeper understanding of human behavior is essential for the right decisions.<\/p>\n

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As well as bringing in the perceived \u201ccore roles,\u201d data science teams might not realize they need roles such as data translators,\u00a0data storytellers<\/a>, and even behavioral psychologists to achieve these goals<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The essential roles<\/h2>\n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It\u2019s arguable that\u00a0no one role is more important than another\u00a0when it comes to creating a complete data science team. There are, however, some roles that spring to mind sooner than others. One of the first positions companies\u00a0are keen to\u00a0hire for is a data scientist, who typically uses statistics and machine learning (ML) to analyze and identify predictive insights.<\/p>\n\n\n\n

Organizations advancing along their AI journey will also identify their need for a visualization designer, who should have information design and UX skills to bring to life the visual intelligence layer of the data insights.<\/p>\n\n\n\n

A machine learning (ML) engineer also plays a key role in data science teams as they package the ML models into an end-to-end application. They use their deep programming skills with a mastery in handling data to\u00a0automate\u00a0the entire workflow.<\/p>\n\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

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\u2026and the ones teams don\u2019t realize they\u2019re missing<\/h2>\n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In addition to these well-known roles within data science teams, there are some that\u00a0fly under the radar, but can arguably be just as important. Companies should recognize the importance of\u00a0data science translators.<\/p>\n\n\n\n

They act as the bridge between business users and data science engineers by identifying the most impactful projects and business challenges that can be solved by data. In fact, McKinsey\u00a0estimates<\/a>\u00a0that demand for translators in the United States alone may reach two to four million by 2026.<\/p>\n\n\n\n

Bringing in a\u00a0behavioral psychologist can help data science teams\u00a0interpret patterns into actionable insights\u00a0that power decision-making and deliver business value. Behavioral psychologists understand why people behave the way they do and can help data scientists by\u00a0giving insights into\u00a0purchase decisions or customer churn.<\/p>\n\n\n\n

For example, a company seeking to predict server failure would only need to look at past performance in terms of the memory usage level and the load on the server. However, throw real people into the mix, and a data-driven approach needs to be complemented by a human dimension.<\/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 company looking to predict employee attrition would need insights on a lot more than employee history or performance. Here\u2019s where a social scientist would come in to provide insights into factors such as demographics, personal preferences, career stage, and past emotional response to improve the accuracy of the predictions being made.<\/p>\n

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Finally, any growing data science team that wants to effectively communicate the context and narrative of data insights will need a data storyteller. Data storytellers do much more than just creating visualization dashboards: They craft captivating narratives from the data insights that are digestible for even the least technical of team members.<\/p>\n

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It\u2019s the data storyteller\u2019s job to help non-data-scientist team members and executives understand the insights through captivating data stories and help them convert them into business decisions.<\/p>\n

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In fact, the importance of these roles coming together in data science teams was accurately predicted by Google\u2019s Chief Economist even a decade ago, when he said<\/a> that \u201cThe ability to take data \u2014 to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it \u2014 that\u2019s going to be a hugely important skill in the next decades.\u201d<\/p>\n

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\u201cThe ability to take data \u2014 to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it \u2014 that\u2019s going to be a hugely important skill in the next decades.\u201d \u2014 Hal Varian<\/p>\n<\/blockquote>\n

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Building a well-rounded data science team is a lot more demanding than many organizations realize. As they progress towards an environment where data ultimately becomes culture<\/em>, organizations will find a growing need for each of these roles.<\/p>\n

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Yet as the tech skills crisis<\/a> continues, the efforts of teams keen to progress along their data journey and hire the necessary talent will remain hindered. Once this skills gap is filled, organizations can move full steam ahead towards AI adoption and reap its many rewards. In the meanwhile, being prepared to fight for talent and expertise is essential to stay ahead of the competition.<\/p>\n

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Many organizations don\u2019t see that the building and executing of successful, ethical, and insightful AI solutions requires a well-rounded data science team, and not just a few idealistic data scientists who can do it all. No one role is most important when it comes to a complete data science team \u2014 but some spring to mind more than others.<\/p>\n","protected":false},"author":315,"featured_media":8769,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[395,94,394],"ppma_author":[1994],"class_list":["post-8768","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-ai-solutions","tag-data-science","tag-data-scientist"],"authors":[{"term_id":1994,"user_id":315,"is_guest":0,"slug":"ganes-kesari","display_name":"Ganes Kesari","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/Ganes_Kesari-150x150.jpeg","user_url":"http:\/\/gramener.com","last_name":"Kesari","first_name":"Ganes","job_title":"","description":"Ganes Kesari is the Co-founder and Chief Decision Scientist at Gramener<\/a>, a data science company that helps organizations present data insights as stories. He advises executives on data-driven leadership and helps organizations adopt a culture of data for decision-making. He is a TEDx speaker and Contributor to Forbes and Entrepreneur. Find his latest work here<\/a> and reach out to him on LinkedIn<\/a>, where he shares insights regularly."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/8768","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\/315"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=8768"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/8768\/revisions"}],"predecessor-version":[{"id":34553,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/8768\/revisions\/34553"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/8769"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=8768"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=8768"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=8768"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=8768"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}