We’ve all seen it. A nonprofit strategic planning process includes plenty of reports full of data; PowerPoints full of charts and figures.

Yet, all too often, that data isn’t used to inform decisions in your planning process.

Stop collecting data you’ll never use. This applies to both quantitative data and to community input as well. Yes, it can waste people’s time. And also, it’s a more specific problem I’m worried about. If you aren’t strategic from the start about the data that you will need to inform decisions, you may collect the wrong data entirely.

I’m a researcher at heart. I believe better data collection starts with asking better questions. Some common questions we ask at the start of strategic planning:

1. What happens if we are wildly successful and achieve our mission in the next 25 years? What does success look like?
2. What are the top challenges facing the communities that we work with?
3. What are the top challenges facing ecosystems?
4. What are the gaps – which challenges are not being addressed?
5. How can we better allocate resources to programs that have the most strategic impact?

While those are fairly common questions across the conservation and sustainability organizations I work with, your team may have different questions. If you don’t want to waste resources later, spend time at the beginning thinking about which questions matter for your organization.

Maybe you have more specific questions, such as:

• Which outcomes are most relevant to the communities we serve?
• What are the most cost-effective ways to preserve more biodiversity?
• Which new non-traditional partners should we work with (and why are we better off working together with these new partners)?
• How can we break down silos and rally our team to work together towards shared goals?
• How can we strengthen our organization’s culture through strategic planning?
• How can we better differentiate ourselves from similar organizations?

Your team will be more likely to use the data you collect – if you only collect useful (usable) data. This means designing your data collection approach to focus on answering your top questions.

Here are some tips to get you started:

1. Consider a mix of qualitative data (collected from communities) and quantitative data (often collected from existing sources).
2. Make sure you include data on big-picture outcomes. This means focusing on opportunities to change lives and benefit ecosystems. (You don’t want to get too stuck in the weeds and only focus on projects or daily operations).
3. While it’s important to focus largely on your top questions, it’s also beneficial to ask some open-ended questions that help you get at blind spots or unexpected issues.
4. Write up your data collection process in a concise data plan. It doesn’t have to be fancy. This is a simple document that helps your team get on the same page around what matters most for data collection.

Have designated decision points. The easiest way to ensure you use data in your strategic planning process is to include decision points throughout the process. When I design a Scope of Work for a strategic planning project, I include meetings in each phase of the process where the team comes together to discuss the data.

Kayla Meyers, an evaluation specialist, recently commented, “In my work, I’ve seen that even small shifts—like building in a ‘data pause’ during decision-making—can really help.”

Compile data in visually appealing formats and share with your team. You want to avoid attending just one focus group and making assumptions based upon a few loud voices in the room. I typically aggregate data across all focus groups, interviews, and other types of engagement. Where possible, you can even compare frequency data from community engagement to quantitative data.

One step I find especially helpful is sharing those findings in a concise report with visually appealing charts, figures and images. The key is to keep them simple and easy to digest.

Holly Hendershot, a strategic advisor to nonprofits, shared with me, “I encourage leaders to reflect individually on what the data says (or doesn’t say) and then collectively do sense making. I usually provide a simple worksheet with a few reflection questions for people to consider as they review the insights or data. I am always astounded by the divergent opinions/ conclusions that are formed from the same data!”

Hendershot’s comment is an important reminder. Data does need to be interpreted. This is why it is important to discuss as a team and talk through why some interpretations are different than others, and how to address those differences.

Consider the different categories of data and how they are used differently. Qualitative data from participants at focus groups is not representative of the population – yet it’s still useful! Veronica LaFemina, a strategic advisor to nonprofits, pointed out in a comment to me that community engagement helps show relevance and importance. She noted, “Ensuring folks actually care about (and care about investing in) a particular solution is important.”

Keep in mind that while not all community feedback is directly useful, you build more trust with communities when you strive to integrate – at a minimum – a couple of their suggestions.

Quantitative data often gives you insights into trends. This can be helpful to see what is changing in the world. It can give you clues around which of your strategies will need to evolve. Quantitative data also tells you information on the effectiveness of your programs. As you get more data on what works and what doesn’t work, this is an opportunity to adapt and continue to pivot towards more effective programs.

Making informed decisions does not mean that the data makes the decision for you – it’s all context for discussion around decisions. Data is meant to supplement discussions with the team.

Each round, as you collect more data, you learn more. You can narrow down your goals and your strategies. You can refine your questions. You want to continue to adapt as you gather more information. In this way, it is an iterative process.

Sometimes we get surprises in the data. Perhaps we learn that our best-known program is not cost-effective. That does not mean you automatically have to stop doing that program. You might make an informed decision about how you will manage rising costs in upcoming years.