The Leader Magazine

SEP 2017

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F E A T U R E A R T I C L E with a lengthy workshop to identify strategic objectives and key performance indicators (KPIs) that were to be achieved. Exploration of daily work processes included using business process mapping, social network analysis, focus groups, observations and badge tracking for documenting individual and team use of space. The multiple methods allowed the researchers to understand where work was being performed, which P-T-P elements were helping or hindering processes – and, ultimately, to determine the impact of various elements on business performance. Key aspects of the overall approach are depicted in Figure 3 (see the blue circles). Importantly, Figure 3 also shows the primary P-T-P elements that contributed to superior performance levels for analytics teams (see the orange circles). Examples of enhanced project-specifi c performance levels achieved by several of the teams in the study included: • R&D: The creation of one new target product that led to revenue growth of 3 percent in a specifi c brand segment • Marketing: A 6 percent increase in the retention of current customers and an increase of 5 percent in new customer accounts • Operations: Prototype cycle times were reduced by 8 percent • Customer service: A 6 percent increase in repeat, sustainable customer business and a 20 percent higher retention rate of the best employees Get the people issues right and fi rst One of the most important fi ndings from the research was that among the ecosystem of interrelated P-T-P elements, the greatest contributors to team success were, by far, elements of the fi rst "P," people (e.g., mission, leadership, values, networks, etc.). This fi nding is consistent with the broader innovation study mentioned earlier, which indicated that all nine people elements had to be addressed fi rst or concurrently to ensure that technology and place issues were positioned for maximum effectiveness. Situations in which technology and place solutions were implemented alone (i.e., without addressing people issues) resulted in average to negative performance outcomes (i.e., it didn't work to "impose" tech and place concepts on teams that weren't ready for them). With jobs based in open-ended problem solving, data scientists overwhelmingly described clear project missions and team leadership as the two most important elements. Mission served to guide and frame expectations, and effective leadership (e.g., access to senior people, clarity of direction and timely decision making) was seen as key to remaining on course throughout projects. Both of these elements were also viewed as the principle factors in mitigating a major problem found in many teams: excessive multi-tasking, or being on too many major projects with concurrent timelines. There wasn't a "magic" number of projects that constituted excessive multi-tasking, but one of the results was data scientists had large, dynamic individual collaboration networks. Networks typically ranged in size from 12-23 people, with 6-10 being core relationships that featured extensive daily interaction. Figure 3: Ecosystem of enablers in creating high-performance workplaces for analytics teams 50 SEPTEMBER 2017 ThE lEadER

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