Inside a Data Science Team: 5 Tips to Avoid Communication Problems

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Data science professionals frequently coordinate their workloads, host meetings to discuss and share ideas, and collaborate to solve problems. But all it takes for things to fall apart is a lack of clear communication.

Data Science is a team sport that involves a variety of professionals working together to solve technological problems. However, you need good communication for your team to run like a well-oiled machine. You may be thinking that poor communication isn’t that big of a deal. If so, you’re wrong.

Communication problems are common

Not everyone thinks or works the same. We’re all fundamentally different. Thus, when you put different people on the same team, unless they’ve worked with each other before or are very social, chances are that they’ll run into problems.

The primary goal of any Data Science team is to solve a specific problem or set of problems. Unless your goals are communicated effectively and your advice is understood clearly, none of your efforts will pay off. To that end, here are our five top tips for improving communication among your team members.

Select the right people for the job

The specific qualifications of your Data Science team members will depend on the size and scope of your company. Choosing the right set of people for the job will help you foster healthy communication and teamwork.

To give you a better idea, a startup likely needs just one person (or a very small team) to maintain its technical and data infrastructure. That’s where data engineers come in—they build databases, conduct routine maintenance, and execute production-level code in real time. Data engineers are skilled in both hardware and software building but tend to perform less data analysis.

When a company grows, it typically starts focusing on data experimentation and machine learning to gain a competitive edge. This is where data scientists come into play. A data scientist analyzes and visualizes data from databases, communicating the results of his or her findings to the company in a clear manner. In practice, a data scientist usually hands off a machine learning algorithm to data engineers, who then implement it.

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Writing efficient code. Solving network requirements. Resolving bottlenecks. Improving machine learning models. Researching new frontiers of AI algorithms. These are all the core aspects of data science work. But far more important is the ability to communicate. With our example above, several things could go wrong if the data engineers were to misunderstand or misinterpret what the data scientist wants them to implement. Clarity is key.

There’s a common theme here: without proper communication, progress comes to a halt. Communication is often an undervalued yet critical skill. It’s absolutely crucial that you evaluate how your candidates rank in terms of communication and team-oriented skills before hiring them. Someone who has decades of technical experience may nonetheless be horrible at communicating their ideas. Consider how well they would function in your team.

Put more simply, which would you prefer: a team of incredibly talented professionals who all work independently of each other because they don’t get along well and can’t communicate, or a team of less experienced professionals who collaborate effectively to break down large problems to get things done? The answer should be clear.

Develop and maintain project docs

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Nothing is more frustrating than feeling lost and clueless on the job. In an Data Science team, ambiguous goals and poor allocation of tasks are telltale predictors of disaster. Up-to-date project documentations will help avoid these problems.

Your project docs need not be long winded and exhaustive. Coherent and focused guidelines will sharpen your team’s focus and provide a clear outline of what they are expected to do. You should, at the minimum, document the following:

  • Mission and project goals
  • Client needs and work specifications
  • Available data, software, and hardware
  • Repositories of data sources the project can utilize
  • Possible techniques that will be used

Though it takes considerable time and effort to prepare thorough project docs, it’s a worthwhile endeavor that can pay off in the end. Even if your team members can’t communicate too well with each other, they can at least rely on your outlines to guide their work.

Use version control consistently

Code files, source docs, and data flows are the pieces that drive data science work. However, the process of producing, maintaining, reusing, and modifying these documents can be long and painful. This is why you absolutely must maintain a constantly updated code repository, together with documentation, to reflect the entire tree of changes, merges, masters, and branches.

Any modern version control system will equip you with a comprehensive suite of capabilities to achieve everything you need. Without such a system in place, your team members are bound to make mistakes and get lost in a thick swamp of stray paperwork and fragments of code.

Additionally, you must keep track of software and hardware environments, such as computer architecture, operating system versions, software toolchains, library decencies, and external dependencies. All of this improves clarity and allows your team to better coordinate their tasks.

Use issue and project tracking to control workflow

In the domain of project management for an IT team, the single biggest mistake you can make is relying too heavily on personal productivity software such as Excel and Project. Each member of your IT team should be able to flag issues, create tickets, and track progress at any given moment via a single, uniform, and accessible system.

With a comprehensive project tracking platform in place, each member can do just that, and more. Project planning and project documentations can be integrated. Work can be allocated and scheduled. Hours and billing can be monitored. Most importantly, progress can be tracked—by everybody.

As a team member, you’ll no longer have to rely on your manager to give you an update. You have the right tool at your disposal to figure out exactly where the project stands, how everybody is doing, and how the current project state compares to the end goal. Once again, this greatly boosts communication, allowing all team members to stay on top of updates as the project progresses.

Host regular one-on-one and scrum sessions

As the team and projects grow, your top priority should be holding regular one-on-one sessions between individual members and the manager so they can evaluate progress and resolve issues as they arise.

Zach Woods Startup GIF by Silicon Valley Additionally, you should host collaborative scrum sessions that allow the entire team to get together and discuss any tasks that everyone is working on, as well as any common issues. These meetings need not belong. In fact, it’s often preferred that they last less than 15 minutes so they do not interfere with normal workflow.

The point of these meetings is to set up your work environment in such a way that everybody can chip in, make comments, evaluate progress, and get a clear overview of current projects. If anyone feels disconnected or isolated from the team, you’re bound to run into communication problems that can hamper success.

Successful communication recipe

Be on the lookout for how your Data Science team can enhance your organization’s primary goal! The purpose of a Data Science team is to add value to your organization by solving problems and offering support that others can’t. To help them, go beyond the team boundary and outline what the team can do for your organization. Try to create opportunities for your team to collaborate with other people in your organization, too. By encouraging collaboration, you often allow your teams to gain greater insight into your business, making them more useful in the future.

With your knowledge and skills, you can make important changes using data, such as proposing new ideas. Investigate new ways to help move your organization forward. Perhaps you need to propose new hypotheses and test them. Maybe this requires a new product that solves your clients’ problems. Maybe it takes predicting consumer behavior in a new way or in a new application.

Regardless of the particular needs of your organization, you should strive to be a catalyst for change. In this data-driven world, nobody is better equipped than you for the task.