What Data Teams Argue About and How to Resolve It

I’ve always been fascinated by conflict. We argue when we’re passionate about something we firmly believe in, and when our point of view differs from others around us. I see it as an interesting challenge to help individuals or groups with differing perspectives reach a consensus.

Here are some common points of argument that I’ve seen crop up within - and beyond - data teams:

1. Team members disagree on how to prioritize the work they’re doing together.?
2. A senior-ranked stakeholder drops in with a sudden demand and causes a data team’s priorities to flip-flop.?
3. Lack of clarity on job responsibilities. For example, asking someone to do a job they haven’t got the skills to do, or don’t enjoy doing.?
4. Insufficient communication about an initiative that comes out of nowhere and clobbers you.?

Let’s dig a bit deeper into each one with an example of how this kind of conflict arises for data teams. Time willing, I’ll offer solution ideas.

1, Team Members Disagreeing on Priorities

Say there’s an initiative that spans multiple teams. This often happens when specialized expertise is required from someone on a different team, and they get recruited in as a contributor. Disagreements can arise about the importance of their work supporting that initiative versus other tasks. One way to address this is to have a clear prioritization framework and a regular review process with all respective managers to ensure alignment. Shared goals can also help.

2. Flip-Flopping Priorities

It’s frustrating for a data team to work on something only to have it deprioritized later. This kind of thrash hurts morale and efficiency. To avoid it, set clear goals for your team and ensure the majority of the work ties back to those goals. If new priorities arise, assess whether they align with the goals you've. For short cycles, sprint planning can help, as it involves regular prioritization decisions with stakeholder input. If you manage to get it right, everyone feels like a winner.

3. Lack of Clarity on Job Responsibilities

The classic case in a data org is asking a data scientist to create a dashboard, even though dashboard-making may not be within their role description. You need the dashboard, but the data scientist may not be great at it, it will take a long time, and they might be unhappy doing it. You can either give them a pep talk or try to find someone else to lend a hand. Having clear role definitions and a process for task allocation can prevent such conflicts.

4. Undercommunication and Overlooking Others’ Needs

This can happen in the data engineering world when someone deprecates a data table with insufficient notice and replaces it with an incomplete alternative. This can cause a mighty bit of unhappiness. To prevent such problems, gather requirements in advance and communicate every step of the way, ensuring all stakeholders are informed and prepared for changes. Of course this is easier said than done, but it’s good as an aspirational idea.

I chose to write about conflict today because I was reminded of a recent conversation with a peer about “influence without authority,” which I see as a superpower within data teams. The scenarios I’ve described here all benefit from having someone around who can influence individuals and teams to work well with one another.

Here’s my challenge to you: Pinpoint common conflicts in your own team and develop creative strategies to address them.