Many organizations focus on the data engineering or development qualifications they require to connect specific data sources and manage data projects. But that is only half of what is needed. Soft skills are so important and sometimes overlooked. Soft skills support data management success because they help individuals effectively communicate and collaborate with others, understand and anticipate the needs of stakeholders, and make data-driven decisions.

Here are the top soft skills needed for data teams. Not all people will have all of these skills, but a team should be able to work together to ensure all of these skillsets are covered:

  1. Communication: Data teams need to be able to communicate complex technical information in a way that is easy to understand and actionable for those who are not experts in the field. Being able to clearly and effectively communicate technical information to non-technical stakeholders, as well as being able to present data and insights in a way that is easy to understand and actionable enables quicker time to development. With communication across departments, organizations run the risk of getting it wrong.

  2. Collaboration: Data teams often work on projects that require input and participation from multiple departments and stakeholders. Being able to work well with others to achieve common goals, including being able to effectively communicate with team members with different levels of technical expertise, can lead to more effective project management across projects.

  3. Problem-solving: Data teams often encounter complex data-related problems that require creative and critical thinking to solve. Soft skills such as problem-solving are important for data teams to be able to identify and solve these problems in a timely and effective manner. This also includes being able to think outside of the box.

  4. Leadership: Being able to lead and manage a data team, including being able to set clear goals and objectives, and provide guidance and support to team members are key skills for leadership. Leaders need to be able to delegate and ensure that tasks are being performed, with one focus on the project and the other on the pieces that are required to successfully complete the project.

  5. Adaptability: Being adaptable is an important skill for data team members to function effectively in an agile environment.

  6. Project management: Being able to plan, organize and manage data projects from start to finish, including being able to identify and mitigate risks, and manage timelines, budgets and resources, are essential project management skills. In many cases, organizations have special project management offices (PMOs) or project managers that can manage the processes associated with data projects, but this approach requires collaboration with data teams as well.

  7. Business acumen: Data teams need to understand the business context in which data is being used and be able to make data-driven decisions that align with organizational goals and objectives. This really means understanding how to leverage data to make actionable decisions and ensure that people across the organization are gaining value from data projects. 

The reality is that organizations cannot overlook the need for soft skills. Without them, companies may not be able to achieve project success because they can't achieve buy-in or get the support they need across teams to ensure collaboration on a broad level. At the same time, soft skills and how they are used and prioritized might differ among roles. Soft skills for data engineers may be more aligned to overall data pipeline development and need to identify business and technical cohesion across business domains. Whereas a solution architect might need to leverage more problem solving and leadership skills to see projects through to completion.