{"id":3303,"date":"2014-02-13T16:31:55","date_gmt":"2014-02-13T16:31:55","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=3303"},"modified":"2014-02-13T16:31:55","modified_gmt":"2014-02-13T16:31:55","slug":"overspecialization-throws-data-science-dream-teams-balance","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/overspecialization-throws-data-science-dream-teams-balance\/3303\/","title":{"rendered":"Overspecialization throws data science dream teams off-balance"},"content":{"rendered":"Building a data science team is difficult enough, but growing one without losing the team&#8217;s effectiveness is a major challenge. Here&#8217;s why overspecialization is the wrong approach to growth. You&#8217;ve built a great data science team, and everything&#8217;s going fine until you realize you need more team members. It&#8217;s a scary endeavor to mess around with a highly performing data science team, but if you&#8217;ve done your job right, you&#8217;ll face this challenge at some point. I&#8217;ve talked before about aligning the structure of your data science team to the size and maturity of your organization. This addresses <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/overspecialization-throws-data-science-dream-teams-balance\/3303\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Building a data science team is difficult enough, but growing one without losing the team&#8217;s effectiveness is a major challenge. Here&#8217;s why overspecialization is the wrong approach to growth. You&#8217;ve built a great data science team, and everything&#8217;s going fine until you realize you need more team members. It&#8217;s a scary endeavor to mess around [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11],"tags":[166],"class_list":["post-3303","post","type-post","status-publish","format-standard","hentry","category-industry-news","tag-data-science"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3303","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=3303"}],"version-history":[{"count":2,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3303\/revisions"}],"predecessor-version":[{"id":3305,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3303\/revisions\/3305"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=3303"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=3303"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=3303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}