Incorporating Data Scientists into Organizations with Consideration to Complexity and Team Leadership Theories

A paper exploring leadership theories related to creating processes for onboarding technical personnel.

Caroline is a CIA veteran who conducted long-term quantitative research and analysis with expertise in Social Network Analysis concepts. Additional analytic experience includes synthesizing database trends with all-source intelligence, collaborating with software developers to create applications to streamline analysis of large datasets, and providing ongoing geospatial analysis of drug trafficking routes with regard to regional security.

Data science expertise has been in high demand over the last decade and will likely continue to grow among organizations of all sizes, which is why organizations should consider applying complexity and team leadership theories to develop processes that will incorporate data scientists into their mission and help them find solutions and meaning in their work. One method of incorporating data scientists into an organization is to allow for a period of interims across the organization as the data scientists are initiated as employees. This method prioritizes developing new employees and connecting them to the organization over pushing new employees towards immediate efficiency. During this period, new data scientists would be shifted among various teams made up of other subject matter experts while also spending periods of time returning to the data scientist “hub” to enable problem solving using the complementary skills of other data scientists. This method is not only supported by complexity and team leadership theories, but also will likely provide human connection and enable people to collaborate to formulate a picture of the future for themselves and the organization, two activities that help make meaning for individuals and groups, according to insight from Viktor Frankl in, Man’s Search for Meaning (2006).

Background

Despite the growth in educational programs to develop data scientists, there remains a gap in the available supply of people with data science skills (Harris, Shetterley, Alter, Schnell, 2013). Due to the gap, leaders in organizations are challenged to not only build teams of data scientists with complementary skills (2013), but build meaning and connection related to the organization’s goals.

Applying Complexity Leadership Theories

According to one definition of leadership in the context of complexity theories by Hazy (2008):

A leader agent is said to influence other agents, called followers, when

it offers a set of choices, tasks and resources — together constituting a program

of action within the collective — that is adopted by the followers. When this

occurs, the individual actions of the followers and the leader become inter

correlated. They begin to act as a system (p. 284)

Viewing organizational systems as organic complex adaptive systems shows promise in demonstrating how leaders can facilitate movement among teams that leads to innovation. Leadership from this perspective recognizes, “the deep relationship between individual activity and the whole (Wheatley, 1992). Choices about how to solve problems are initiated by the collective action of members working towards a collective benefit and by the organizational structure of “people, resources, and capabilities” in an efficient and effective way, as defined by peak performance (Hazy, 2006). As data scientists move and collaborate across teams, their ideas and understanding of the organization at large will almost certainly grow and they will be naturally empowered by exposure and awareness to solve increasingly high level problems.

Accomplishing goals in increasingly complex and data-heavy environments will require leaders to allow employees to have real-time interactions and improvisation, however it does not mean a complete lack of structure. Complexity theorists propose that the structures just require elements of adaptation and formal control (Bryman, A., Uhl-Bein, M., & Marion, R, 2014). This might include scheduling interims for data scientists as well as team building initiatives, but allowing data scientists and teams to determine their future collaboration schedules based on needs and changes in the projects.

Complexity leadership recognizes that bottom-up processes have been evidenced to motivate employees to take initiative towards innovation more than financial incentives and formal processes (Koch, R. & Leitner, K., 2008). As noted by Hazy (2008) the program of action that occurs from cooperative leader-follower relationships, “becomes an attractor for agent (follower) choices,” from leadership’s implementation of programs, followers choose to cooperate with one another. The cooperation is an important aspect in breaking down human created in-group/out-group barriers in organizations.

Similar to the focus within team leadership theory (Burke, DiazGranados, and Salas, 2014), complexity leadership responsibilities lie in enabling a set of functions that are carried out by the group. The mix of fluidity and structure in both complexity leadership theory and team leadership theory means that assigned leaders will have to focus on group movement and dynamics as they also introduce data scientists to the broader organizational system.

Applying Team Leadership Theories

As of research from 2001, 68% of fortune 500 companies are using self-managed teams (Burke, DiasGranados and Salas, 2014) and, as of a 2008 study, of training professionals in 185 organizations, 94% of the respondents indicated that their organization used teams (2014) which makes effective team leadership a priority across various industries. In terms of supporting co-located teams, team leadership theory takes a functional approach that views leaders’ contributions as linking teams to their environment and assisting in solving team problems (Zaccaro et al., 2001). Leaders of teams are responsible for affecting team processes, like coordination and motivation (2001) and leaders create enabling conditions for team performance (Hackman, 2002). They are also responsible for facilitating trust, which is a history-dependent and interactional process, more easily developed when individuals are perceived as part of the in-group (Kramer, 2014). Team leaders are responsible for building trust within their team and among partners, this process, which consists of facilitating shared experiences, contributes to team motivation (Zaccaro, et al., 2004).

Having data scientists integrate with subject matter expert teams will likely widen the in-group mentality of individuals on various teams by providing shared experiences and building trust. The interaction, consisting of a team of developing data scientists and a team of subject matter experts, along with each team’s response to organizational needs and movement towards collective goals would make the collaboration akin to a multiteam system (MTS). The complexity in MTSs is that leaders of each team must be able to navigate horizontal leadership between peer leaders and vertical leadership within the organization (Burke, DiasGranados and Salas, 2014). This involves aligning across the teams in terms of strategy development and coordination (2014). When leaders can coordinate amongst themselves and their teams, it will probably improve the “organizational disconnect” Salminen, Milenković, and Jansen discuss (2017) which occurs between the data scientists and other departments. The authors note that it can be difficult to “infuse” data-driven mindsets, throughout the organizations (2017).

Researchers found that in leading technical teams, one of the top problems leaders reported in a 1994 study was a “lack of team member involvement reflected in apathy, lack of commitment and low energy,” (Gemmill and Wilemon, 1994). In considering confronting team members, the leaders perceived that they risked causing further apathy as well as being met with denial (1994). Although the workplace has changed since 1994, it is likely the human need for motivation remains present and team leaders can be part of the solution by enabling new process that help technical teams connect to the broader organization’s people and mission.

Conclusion

Complexity and team leadership theories emphasize that leaders facilitate processes and meaning for individuals and teams in their organizations. The leadership challenge of incorporating data scientists into an organization would likely be aided by consideration of complexity leadership and team leadership which provide an avenue to take on the leadership challenge with consideration to systems, planning, adaption, and human psychological needs.

Literature Review Conclusion
The literature on how to use leadership theories to better incorporate data scientists into organizations is limited. A great deal of research is focused on defining the data scientist role, looking to arrive at a universal definition and guide the field in developing data scientists. Some research is focused on creating data-driven cultures, which begins to touch on leadership theories and strategies. Business strategists have touched on how to build the right data science team, but overall, there is space to study how leaders will facilitate data scientists influence on the organization through complexity led processes such as initially moving the data scientists throughout the organization to create shared knowledge, experiences, motivation, and trust between the data scientists and the other subject matter experts that will help move the organization forward in making the most of its data potential.

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