Practical Goal-Setting Tips for Data Teams

In mid-November 2024, I ran a quick poll among my LinkedIn connections to learn how data teams are approaching 2025 goal-setting. The timing felt right, since this is the season when many of us are planning for the year ahead. Of the 16 respondents, 75% said their team is in the process of drafting goals, while only 6% had finalized them. Another 13% admitted they hadn’t started, and 6% selected “Other,” mentioning cases like fiscal years that do not align with the calendar year.

Before we proceed, let me clarify: this guidance is for data teams setting goals for themselves, not for advising stakeholders or the broader company on their goals. The bottom line is that many of us are still busy finalizing our own goals for the new year. With that in mind, I’d like to share some tips drawn from my own time-tested experience.

1. Get started with confidence. If your data team has never set a single goal before, don’t worry. The fact that you’re reading this article means you’re already heading in the right direction. Remember, goal-setting gets easier and less intimidating the more you practice. Now is the perfect time to start.

2. Reflect on the past. If your data team has set goals in prior years, dig them up and review them. If your team hasn’t, but a similar team in your company has, ask for copies of their past goals. Once you’ve gathered this material, ask yourself: What worked well? What didn’t make sense? What was abandoned? Looking back at the past is a great way to spark ideas for the future.

3. Align with stakeholders’ goals. Data teams are often viewed as service providers supporting their stakeholders’ orgs. The closer your goals align with business priorities, the clearer your team’s impact will be. Everyone’s itching to prove the value of analytics. This is how you do it.

4. Connect goal-setting with strategic planning. In an ideal world, especially in mature and stable groups, your data team will already have a strategic plan that outlines what you aim to accomplish. This material might focus on the next 12 months or include a longer-term vision, such as a three- to five-year plan. Use your strategic plan as a foundation to shape your goals.

5. Crowd-source within your team and among stakeholders. Good ideas can come from anywhere. Start by running a brainstorming session with your data team. For even better results, invite key stakeholders to join the session. This will help you create a strong list of potential goals. It also gives everyone a sense of ownership and a bit more skin in the game.

6. Prioritize based on impact. If you’ve been following along with these tips, your list of possible goals is probably far too long by now. Sort them by potential impact (or use a fancy method like ICE scoring). Pick a reasonable cut-off for what you will and won’t pursue. Don’t try to do too much. A shorter list will be easier to remember, track, and communicate.

7. Avoid composite goals. If you’ve got too many goals and can’t bring yourself to cut any, you might be tempted to combine them into one massive composite goal. Please don’t. I’m begging you. Composite goals are hard to remember and even harder to evaluate. They have no place in the House of Dershewitz.

8. Pick a standard format for all of your goals. Does your company have a standard for goal statements, like SMART or OKRs? If so, use it. If not, choose your favorite format and stick with it. Consistency shows you’re serious and makes the goals easier to manage.

9. Action! Once you’ve finalized your list of goals, keep them in an easily accessible place, make (and honor) a calendar for goal reviews, and start executing. To prove the value of analytics, you must follow through on the goals you set. That’s how you maintain trust.

10. Don’t be shy about mid-year updates. A lot can change. If a goal no longer makes sense, don’t feel like you need to keep plugging away at it. Be flexible and adjust.

Bonus tips for AI-assisted brainstorming

If you’ve got access to a work-approved AI chatbot, I’ve got three more tips. AI can be a helpful brainstorming partner for data teams. Here’s how:

11. Generate ideas. Provide strategic plans or past goals as input, and ask AI to suggest a list of potential goals.

12. Rank goals. Supply a draft list and ask AI to prioritize those goals based on alignment with business priorities or feasibility.

13. Find collaboration opportunities. Share stakeholders’ goals with AI and ask for recommendations on how your data team can add value. For instance, AI might spot a stakeholder goal to “increase customer retention by 10%” and suggest a complementary goal to “build a churn prediction model.”

This is optional, but if you’ve got it, use it. AI tools can save time and help streamline the brainstorming process.

Narrowing down to the essentials

As a thought exercise, if I could only pick three goals for my data team in 2025, I’d choose:

1. One focused on innovation

2. One linked to a top business priority

3. One aimed at improving operational excellence

But that’s just me.

I’ll leave you with a diagram of common goals that data teams set. If you want to read more, here’s the whole article.

Good luck. May your goal-setting season be quick, painless, and productive.