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SEP 2017

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ThE lEadER SEPTEMBER 2017 49 Analytics teams at work: using data to solve open-ended, creative problems Analytics teams are considered mission-critical to business processes because the employees who comprise these teams – known as data scientists – help develop business hypotheses and then write the code to test the concepts. As depicted in Figure 1, the multidisciplinary skill set of data scientists can be broken down into fi ve fundamental characteristics that describe the data side of the job and fi ve complimentary business skills. A leader from one of the fi rst teams studied described her job as being "a creative, artistic, data-driven story teller." The unique capabilities of data scientists are typically applied to four core job activities, plus a leadership role and support position. Key aspects of the six activities are shown in Figure 2. The process often begins and ends with an emphasis on the creative science side, and the middle part is an extensive focus on data collection and analysis. In reality, the process is not as linear as shown, as there are starts, stops and feedback loops. The teams studied ranged in size from 8 to 40 people, with virtually all requiring that members be capable of performing any of the four core job activities. As shown in Figure 2, the activities of data scientists require a balance of concentrated, heads-down work and communication with others. Concentration is typically greatest during data collection and analysis periods, but most team members worked on multiple projects – and not always at the same time or point in the process – so individual needs for concentration and communication varied widely within teams. Perhaps most importantly, 65 to 70 percent of the work performed by analytics teams was done by individuals working alone, coming together with others as required to share ideas. The most effective teams were comprised of data scientists who were highly satisfi ed with their ability to control communication and concentration levels. Additional characteristics common to virtually all of the teams included: • unpredictable, ongoing and rapid change in project focus and team structure • broad, multidisciplinary, dynamic, internal and external collaboration networks that cut across organizational boundaries • emphasis on informal, customized and tacit information exchange • face-to-face and virtual, often asynchronous, presence across multiple time zones • substantial cultural, ethnic and generational diversity Understanding, enabling and measuring performance As the discussion suggests, understanding the capabilities, characteristics and processes of analytics teams involved the use of a range of user-centric data collection and analysis methods. In fact, the overarching concept was to draw from business models and methods in collecting and analyzing comprehensive information about analytics teams. The process always began Figure 1: The fundamental skills & capabilities of data scientists Figure 2: Analytics processes cover four core activities, as well as a leadership role and technical support

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