Every organization has a culture that strongly influences employee beliefs, thinking, decision-making, and behaviors. According to MIT Professor Edgar Schein, an organization's culture is:
A pattern of shared basic assumptions that the group learned as it solved its problems that has worked well enough to be considered valid and is passed on to new members as the correct way to perceive, think, and feel in relation to those problems.
In his book The Reengineering Alternative,William Schneider identifies four common corporate cultures, which are described in greater detail in the sections that follow:
Some cultures are more conducive to data science than others. For example, a collaboration data science culture tends to be more open to curiosity, transparency, exploration, and discovery — all of which are conducive to data science. On the other hand, a data science control culture tends to place more value on certainty over curiosity. Instead of transparency, leadership operates in a cloistered environment of secrecy. Instead of exploration and discovery, goal-setting, planning, and meeting milestones are the objectives.
Unfortunately, an organization's culture can be deeply ingrained and difficult to change, even on a small scale. And if the existing culture is counterproductive to the data science team's mission, it can totally undermine a team's efforts.
When you're trying to get a new data science team up and running, one of the first steps is to identify the culture in which the team will operate, so the team will be more aware of any resistance it may encounter.
A control culture has a distinct pecking order characterized by a corporate hierarchy with an emphasis on compliance. Everyone in a control culture knows who they work for and who works for them. The role of the individual is to comply with the supervisor's requirements. The head of these organizations communicates a vision, and everyone down the line is responsible for implementing it.
Data science teams often struggle in control cultures for several reasons, including the following:
Even with these challenges, many data science teams succeed in organizations with strong control cultures, which often rely heavily on their data and ability to use it to make data-driven decisions.
A competence culture is centered on knowledge and skills and tends to be organized into areas of expertise, so specialization is rewarded. The most highly competent individuals in the organization become the de facto managers. They set the standards and create and delegate tasks. This culture is prevalent in organizations driven by specialized knowledge such as engineering firms and software development firms.
Competence cultures tend to struggle with the data science mindset. Data science tends to be interdisciplinary. Team members are more generalists than specialists. In addition to a familiarity with statistics, mathematics, programming, and storytelling, data science team members need general knowledge that spans all functional areas of the organization. Cultures that put a lot of emphasis on specialization may have trouble appreciating what the data science team has to offer. They may also have trouble accepting the fact that the data science team requires cooperation from other functional areas to do its job; the best questions often come from outside the data science team.
In a cultivation culture, leadership focuses on empowering and enabling people to become the best possible employees. These organizations tend to be structured like a wheel, with employees at the center surrounded by mentors and resources to make each employee successful.
A great deal of emphasis is placed on expressing yourself. Charismatic individuals can quickly rise in the ranks according to their ability to harness the collective talent of team members to solve problems.
Generalists do well, so a data science team is a natural fit in a cultivation culture. However, don't expect quick, decisive action on anything your team proposes. Decision-making can be a long, drawn-out process, because it is highly participatory and organic. The drive is toward consensus, which can be difficult to reach with a large number of diverse opinions.
True cultivation cultures are rare. Some organizations may think they have a cultivation culture, but if you look closely, you'll see that they don't really follow a lot of the key practices. A lot of these organizations are just control cultures with a thin veneer of a cultivation culture.
A collaboration culture is similar to a control culture in that decision-making power is concentrated in the upper levels of the organization. However, instead of a strict top-down management structure, authority is concentrated in groups across the organization. Collaboration is mostly within these groups rather than among them. These collaborative groups tend to make decisions via brainstorming sessions along with some experimentation.
You're likely to encounter the collaborative culture in training organizations, in which leaders tend to be team builders and coaches, and in family-run businesses, where authority is based on relationships.
Compared to the control and competence cultures, the collaborative culture is more open to change, which makes collaborative organizations more likely to embrace a data science mind-set. However, keep in mind that authority is concentrated in pockets and may or may not be pushed down to the team level. These organizations are only slightly more democratic than those with a control culture.
If your organization has a collaboration or cultivation culture, it will have an easier time embracing the key components of a data science mindset, because they value generalists and are accustomed to communicating and collaborating across teams. You can expect more resistance in a strong control or competence culture. Here are a couple suggestions for overcoming such resistance:
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