How to foster a collaborative data team

A collaborative team works jointly toward the same goal, whether contributing to a product feature or achieving a quarterly or yearly objective or key result. It is a well-oiled machine that consists of team members from various units who consistently and effectively communicate to achieve their joint targets quickly and efficiently.

Consider how your team would establish a standard release process. In a collaborative team, proper communication and a pre-discussed release implementation plan would mean that each team would know, roughly, when to start the release on their own items, especially if they are dependent on other teams finishing their respective parts. For example, the data engineering team will know when the DevOps teams will finish the deployment of their items, and the quality assurance team will know when the other teams will finish so that they can test the newly released features.

A team that collaborates well does not require micromanagement as a developer or engineer can safely assess what falls under their remit and when others need to be involved. This article explains how to create a collaborative team that you can trust to make the right decisions and communicate to their managers any pain points and potential improvements.

What happens when a data team isn’t aligned?

In larger companies where over a hundred team members are working on the same project, misalignment can wreak havoc and cause de-motivation for a large number of team members. Having unclear product requirements and noticing this at the user acceptance testing stage can cause mistrust between teams. For user acceptance testing to happen, prior development needs to take place and this usually involves a lot of data teams’ collaborating together, including architects, engineers, and business intelligence teams. This often means the company needs to spend additional time and effort to get the final product aligned with the revised requirements.

In addition, if a data science team’s deliverables on data mining and data modeling segmentation are delayed, then the business intelligence team can’t deliver a fully populated segmentation dashboard on time. These kinds of errors can throw entire projects off the rails, causing unnecessary pressure on other teams further down the line, who then have to work overtime or faster than expected to stick to the client’s desired deadline. In turn, this causes unnecessary hostility between teams, potentially creating a silo. Moreover, during a retrospective meeting to discuss what could have been done better, teams might start pointing at each other, providing a negative environment for upcoming cross-team projects.

According to Miro, siloed teams happen “when different teams or team members in the same company purposely don’t share valuable information with other members of the company.” A siloed team is therefore hyper-focused on its own set of responsibilities and not fully aware of how its efforts contribute toward the company’s overarching goals and final product. Company-wide training on teamwork, organized project kickoff sessions, and project-dedicated channels with members from several departments can help to reduce the risk of silos and enhance the communication of timelines and expectations.

In addition, alignment and accountability are vital as this helps large groups of people know their value and understand what they need to do to solve the current issue. For instance, TechTarget discusses the importance of teams understanding the overall goal and end result of a project so that the individual teams can figure out how each department can help reach that goal.

It’s also important for the team’s structure, and those leading it to be flexible to the company’s needs and environmental changes. Flexibility allows you to truly meet your company or your clients’ needs to the best of your ability.

Fostering a collaborative data team

Now that you know how vital collaboration is, the following sections outline ways you can create a company culture that encourages different teams across your organization to independently work toward achieving the same goals.

Clear communication at all levels

Company objectives help define central goals that every team needs to aim for, whereas company deadlines and task requirements convert goals into tangible targets. This isn’t really about short-term projects that only require a few weeks’ work from one or two teams; it’s for projects that span a quarter or more and involve the effort of multiple teams, sometimes in parallel. Emotional intelligence has been linked to effective collaboration and helps to build trust and manage team conflicts.

Kickoff meetings for major projects might also help teams understand the overall expectations, how large a project is, and who is involved. A kickoff meeting introduces upcoming projects and their relative timelines and provides time for questions at the end. Allowing all involved teams to ask questions gives members of the different teams the opportunity to understand each other’s concerns and points of view. It can help provide answers to questions like the following:

  • What is the expected client deliverable?
  • What are the key dates to keep in mind?
  • Who is the project manager and central point of reference?
  • Why is my part important in the overall scheme of things?

A sense of camaraderie

Due to the COVID-19 pandemic and the increasing popularity of remote and hybrid work systems, achieving collaborative teams and a sense of camaraderie has become more of a challenge. Nonetheless, companies can achieve this in a number of ways to unite people, whether under a physical roof or in a Zoom call. Activities such as a team volunteering outing or a lunch and learn, where teams sit down for lunch together and discuss lighthearted topics, promote an informal setting where colleagues can get to know each other on a more personal level. They also help promote an engaging work environment for team members, not only because they enjoy their work but also because they enjoy catching up with colleagues.

Working in pairs can also instill a sense of dependency and the idea of working toward the same goal. For example, Meta suggests that employing pair writing builds trust, and having team members expose themselves to others’ ideas broadens their personal perspective.

Diverse perspectives

Some companies choose to rotate people from different teams to work on different projects from time to time, which can promote diversity and introduce alternative perspectives to your projects. However, you should be aware that diversity can be a hurdle; if team members do not perceive their peers to be like them, they are not as likely to share idea. Such differences can be anything from education to age or tenure. Nonetheless, in the end, diversity helps the company produce a more well-rounded product; employees from diverse backgrounds will provide varied feedback, and each team member might notice something different that can be improved.

Diversity will also reflect positively on the company itself, indicating an accepting environment for all and heightening team morale.

Data cleanliness and correctness

Keeping your data clean—i.e., making sure the data is correctly inputted at a core level with no duplicate or junk data—will reduce frustrations at a later stage. For instance, duplicate data rows will skew the data science model, forcing data scientists to deviate from their work and try to understand the data underneath their model.

Proper data governance ensures that data is of the highest quality and is organized, clearly documented, and available to those who need it. It also monitors who has access and how the data itself is being used. Data governance also helps lower data management costs and improve decision-making by providing clearer information for business leaders. Good data governance should also encourage data literacy between teams and identify data assets and the informal governance processes that manage them. If all of your teams understand the relevant terminology and structures for your data in the same way, collaboration will be greatly improved.

Retrospective meetings

After a project has been completed and submitted, retrospective meetings can be useful to highlight what went right and what went wrong. This will promote the techniques that made the project successful and review the lessons learned to avoid recurring mistakes. These mishaps might even help develop potential OKRs for the next year or quarter. When the teams’ strengths and weaknesses are properly communicated, they help clearly highlight what each team member needs to work on, encouraging everyone to work toward the same goal.

Final notes

Clear communication of project and company goals, a sense of camaraderie, diversity, reflections on past achievements and mistakes, data cleanliness, and proper data governance are all vital for motivating colleagues to collaborate and work together. A happy, highly motivated team not only strives to produce quality but also is more likely to approach their manager to communicate what went well and to indicate areas for improvement.

A sense of camaraderie between team members also allows them to get to know each other more, instilling a higher level of trust and a more comfortable communication setting. Constructive criticism will also be received better when a level of friendship has already been established between two team members. Any potential frustrations or delays will also be handled better when team members know that they are not alone in this situation and that they have each other’s support and understanding. Companies looking to scale or bolster their teams may need help in finding the right people to do so.


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