{"id":265407,"date":"2015-11-05T13:20:37","date_gmt":"2015-11-05T21:20:37","guid":{"rendered":"http:\/\/customerthink.com\/?p=265407"},"modified":"2017-12-14T00:09:21","modified_gmt":"2017-12-14T08:09:21","slug":"getting-more-value-from-data-6-facts-about-the-structure-and-practice-of-data-science","status":"publish","type":"post","link":"https:\/\/customerthink.com\/getting-more-value-from-data-6-facts-about-the-structure-and-practice-of-data-science\/","title":{"rendered":"Getting More Value from Data: 6 Facts About the Structure and Practice of Data Science"},"content":{"rendered":"
\r\nThe value of data is measured by what you do with it, and organizations are relying on data scientists to extract that value. I recently conducted a survey of data professionals<\/a> to\r\n better understand what it means to be a data scientist. I discovered a few things in this study that can help organizations optimize the value of their\r\n data. While I wrote about these findings in prior posts, I want to summarize the major points here, in a more concise way.\r\n<\/p>\r\n \r\n While some of these points below seem rather mundane or obvious, it’s important to note that these ideas are no longer only opinions; they are backed up by\r\n empirical data. This is how data science really works.\r\n<\/p>\r\n \r\n 1. There are a handful of different skills that make up the field of data science.<\/strong>\r\n While we measured five distinct skill types, a\r\n \r\n factor analysis of proficiency ratings of these five skills resulted in three distinct skill types\r\n <\/a>\r\n :\r\n<\/p>\r\n \r\n 2. There are different kinds of data scientists.<\/strong>\r\n Our study identified\r\n \r\n four distinct job roles among these data professionals\r\n <\/a>\r\n :\r\n<\/p>\r\n \r\n Respondents were asked to select which of the job roles best described their work. They could choose one or any combination of job roles. The correlation\r\n across job roles (1 = selected; 0 = not selected) was quite low (average r<\/em> was -.07; highest r<\/em> was -.30), suggesting that these four job\r\n roles are distinct from each other.\r\n<\/p>\r\n \r\n 3. Different job roles require different skill sets.<\/strong>\r\n \r\n Data professionals in different job roles have different skill sets\r\n <\/a>\r\n . Not surprisingly, data professionals who identified as Developers reported the highest levels of proficiency in Technology and Programming skills\r\n compared to their counterparts. Additionally, Researchers reported the highest levels of proficiency in Statistics and Math while data professionals who\r\n identified as Business Management reported the highest levels of proficiency in Business. Finally, data professionals who identified as Creative reported\r\n moderate ratings across all skill sets, suggesting they are indeed jack-of-all-trades.\r\n<\/p>\r\n \r\n 4. Finding a data professional who is proficient in all data science skill areas is extremely difficult.<\/strong>\r\nData professionals rarely possess proficiency in all five skill areas<\/a> at the\r\n level needed to be successful at work. In fact, the chance of finding a data professional with expert skills in all five data science skills is akin to\r\n finding a unicorn; they just don’t exist.\r\n<\/p>\r\n \r\n 5. A team approach is an an effective way of approaching data science projects.<\/strong>\r\nWe found that data professionals who worked with other data professionals who had complementary skills were more satisfied with their work<\/a> than when they did\r\n not work with another data professional. For example, Business Management professionals were more satisfied with the outcome of their work when they had\r\n quantitative-minded experts on their team (e.g., Math & Modeling and Statistics) compared to when they did not have them on their team. Also,\r\n Researchers were more satisfied with their work outcome when they were paired with experts in Business and Math & Modeling. Developers were more\r\n satisfied with their work outcomes when paired with an expert in Business. Creatives\u2019 satisfaction with their work product is not impacted by the presence\r\n of other experts; this finding is likely due to the fact that Creatives are not able to contribute sufficiently to teamwork success because they are not\r\n highly proficient in any of the data skills (see point 3 above).\r\n<\/p>\r\n \r\n 6. You can find out what kind of data scientist you are for free.<\/strong>\r\n As part of our study of data scientists, we at AnalyticsWeek<\/a> developed the Data Skills Scoring\r\n System (DS3), a free web-based self-assessment survey that measures proficiency across five broad data science skills: business, technology, math and\r\n modeling, programming and statistics. The DS3 takes less than 5 minutes to complete. Our hope is that the DS3 can optimize the value of data by improving\r\n how data professionals work together. If you are a data professional, the DS3 can help you:\r\n<\/p>\r\n\r\n Facts about Data Science\r\n<\/h3>\r\n
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