{"id":427,"date":"2015-08-28T07:28:42","date_gmt":"2015-08-28T04:28:42","guid":{"rendered":"http:\/\/kusuaks7\/?p=32"},"modified":"2025-01-30T12:52:18","modified_gmt":"2025-01-30T12:52:18","slug":"data-science-team-building-power-collaborative-analytics","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/data-science-team-building-power-collaborative-analytics\/","title":{"rendered":"Data Science Team Building: The Power of Collaborative Analytics"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tData science teams are bringing new business values across the enterprise supply chain\u0097so much so that the business units who do not have access to a central Data Center have all set up their own, home-grown data science teams with internal experts. In their book about the data science profession, Tom Davenport & DJ Patil describes data science to be the Sexiest Job of the 21st<\/sup> Century (Data Scientist: The Sexiest Job of the 21st<\/sup> Century<\/em>).\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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\nTraditional BI versus<\/em> data science<\/strong><\/h3><\/h3>\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\tJust to give some examples of how the traditional BI roles have evolved into the new data scientist roles:\n
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  1. While the traditional BI analyst simply analyzes and reports the quarterly sales for a specific zone, the data scientist may provide future predictions and sales forecasts based on historical sales data.<\/li>\n \t
  2. While the traditional BI analyst will primarily depend on customer-feedback forms and occasional surveys to gather customer data, the modern data scientist will utilize novel technological platforms like the social media or forum posts to discover valuable insights about different customer segments.<\/li>\n \t
  3. While the traditional BI analyst may limit the data analysis to finding average cost of shipment of specific goods, the data scientist will also review shipment patterns and interrelationships with other cost data to identify ways to reduce or optimize shipment costs.<\/li>\n<\/ol>\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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    Typical data scientist profile<\/strong><\/h3><\/h3>\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\tIt is probably difficult to pin down an ideal data scientist profile because in current practice, in-house data science teams are formed with experts with a wide variety of academic and professional backgrounds. Booz Allen Hamilton’s field guide on data science seems to suggest that proficiency in key technical disciplines is required in order to be considered for a role in a data-science team. Subjects like computer science, domain expertise, or mathematics are high in demand in this profession. As Hamilton’s primer points out, a computer science degree is essential for data processing tasks; domain expertise helps the data scientist understand the problem and the processes for measuring the problem; and advanced math skills are required for understanding the business goal of algorithms.\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\tAlong with the above core skills, data-science team members must also be skeptical and curious by nature, technically astute with solid quantitative orientation, and demonstrate an aptitude for collaboration and communicative approach to problem solving.\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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    Data science team composition<\/strong><\/h3><\/h3>\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\tBooz Allen Hamilton’s 110-page primer called The Field Guide to Data Science<\/a> provides in-depth details of data-science team building. The field guide summarizes some basic characteristics of ideal data-science teams. The underlying assumption here is that collectively, the members of an effective data science team will possess the following qualities:\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\nAbility to ask questions: <\/strong>The natural curiosity to discover relationships between data, and ask pertinent questions is an essential prerequisite.\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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    \n\t\t\t\t\t\t\t\t\t\nCreative problem solving:<\/strong>\u00a0The inclination to ignore established methods and devise new problem-solving approaches is a welcome attribute.\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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    \n\t\t\t\t\t\t\t\t\tTenacity:<\/strong>\u00a0The ability to remain focused for extended periods of time while designing and testing solutions is absolutely necessary. Many times, apparent solutions may fall through\u0097necessitating fresh rounds of designing and testing from start to finish.\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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    \n\t\t\t\t\t\t\t\t\tDetail-orientation:<\/strong>\u00a0Nothing can be left to guesswork in data science projects; so team members must be highly detail-oriented to minimize the risk of intuitive judgments.\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\tIn response to a common complaint that data science team members are often reluctant to explore new approaches to problems, Booz Allen Hamilton\u0092s field guide recommends creating an environment of trust and communication across all levels, instead of deference to authority.\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\tTo ensure your DST provides the greatest value, some unique support roles must also be present in the core team. The data scientist is often aided and mentored by supporting team members, a few of whom are described here:\n