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Data analysis with AI

📖 Lesson content

What you'll learn

Estimated time: 50 minutes

By the end of this lesson you'll be able to:

  • Use the Delegation-Diligence loop to systematically validate AI's analytical capabilities for your specific work
  • Apply Description and Discernment to identify patterns in your data while recognizing AI's limitations
  • Build confidence in AI-assisted analysis by testing against data you already understand

Data analysis with AI

(7 minutes)

This video addresses a critical question that nonprofit professionals face when using AI for data analysis: How do I know I can trust the results? You'll follow Rio, a program director at Valley Veterans Services, as he uses the Delegation-Diligence loop to systematically validate AI's analytical capabilities using past data where he already knows the answers. The video demonstrates how to build confidence through testing, identify capability gaps, and develop an approach you can apply to new data with confidence.

Key takeaways

  • Test AI against data you already understand: Before trusting AI with new analysis, validate it using past data where you know the correct results. If AI can match your known results with the right guidance, you can confidently use it for similar future tasks
  • Use Discernment to identify gaps in AI's reasoning: As you test, note where AI misses important context and what additional Description you need to provide
  • Build validated approaches, not blind trust: Each testing round teaches you what AI does well and where it needs guidance. Document what works so you can replicate it
  • AI can help even if you're not data-savvy: If you're not comfortable with data analysis yourself, AI can help brainstorm solutions, write Excel formulas, and reformat messy data—just keep asking for clarifications so you understand the process
  • Validation builds confidence but doesn't eliminate responsibility: You're still accountable for checking that results make sense and being transparent about AI's role

Exercise 1: Messaging analysis

This exercise uses lower-stakes data (your own public communications) to practice the Description-Discernment loop for data analysis.

Part I: Gather your data

Collect 10-20 examples of your organization's communications—social media posts, email subject lines, newsletter headlines, or event announcements. Include a mix of what you consider high-performing and lower-performing content.

Part II: Analyze with AI

Share your dataset with AI and ask it to identify patterns:

  • What themes or topics appear in your higher-performing content?
  • What language, tone, or formatting patterns emerge?
  • Are there any gaps between what you communicate and what resonates?

Part III: Apply Discernment

Evaluate AI's analysis:

  • Do the identified patterns match your intuition about what works?
  • What context is AI missing about your audience or goals?
  • Are there patterns AI identified that surprise you?

Reflection:

  • What are you trying to learn from your dataset?
  • How does higher-performing content align with your authentic voice and organizational values?
  • Are you reaching the right audience?

Stretch goal: Use AI to audit how your messaging compares with your organization's stated mission and values, find discrepancies, and create a messaging guide from the analysis.

Exercise 2: Analyzing donor giving patterns

This exercise applies data analysis skills to high-stakes fundraising data, building on the data hygiene practices from Lesson 5.

Part I: Prepare your data

Use the sanitized donor dataset from Lesson 5, or prepare a new one by removing personally identifiable information. Ensure you have historical giving data across multiple time periods.

Part II: Analyze with AI

Ask AI to identify patterns in:

  • Donor retention rates over time
  • Recurring vs. one-time donation patterns
  • Campaign effectiveness comparisons
  • Giving trends by amount ranges

Part III: Apply Discernment

Critically evaluate AI's findings:

  • Do the trends match what you know about your donor base?
  • Is AI only focusing on monetary value, missing relationship factors?
  • What patterns would help strengthen donor relationships, not just maximize revenue?

Reflection:

  • What might the costs of implementing efficiency recommendations be? (e.g., If findings suggest focusing on major donors at the expense of small donors, what's the impact on community perception or long-term sustainability?)
  • What patterns would help strengthen relationships with donors beyond just giving amounts?

Exercise 3: Trend analysis to anticipate community needs (stretch goal)

This advanced exercise combines multiple data sources to practice predictive analysis—a highly requested capability.

Part I: Gather diverse sources

Collect information you already use to understand community needs:

  • Your own program data and service requests
  • External reports or datasets about your community
  • News or policy developments affecting your constituents

Part II: Analyze for emerging patterns

Ask AI to help you identify:

  • Trends in the types of support people are requesting
  • External factors that might increase or change demand
  • Gaps between current services and emerging needs

Part III: Apply rigorous Discernment

This analysis requires the highest level of critical evaluation:

  • How do AI's predictions compare with your direct community experience?
  • What systemic factors or local context might AI be missing?
  • What values do you need to keep in mind as you anticipate community needs with dignity and respect?

Reflection:

  • How can you approach this process responsibly?
  • What factors and systemic issues can explain or contextualize what AI cannot?

Lesson reflection

  • How did testing AI against data you already understood change your confidence in using it for new analysis?
  • What gaps or limitations did you identify that will shape how you delegate data analysis tasks in the future?

What's next

In the next lesson, we'll look at workflow automation—how to apply these same principles when AI handles routine tasks on your behalf, freeing up your time for higher-impact work.

Feedback

As you progress through the course, we'd love to hear from you about how you are using concepts from the course in your work and any feedback you may have. Share your feedback here.

🎬 Video transcript

Source video: Zzn-g8lvLMA

📜 Click to expand transcript (cleaned + AI-translated)

Building Trust in AI through the Delegation Diligence Loop

In our last lesson, we dealt with data privacy and security—what you absolutely need to protect and how to do it. Now, let's address the question that has likely stopped you from using AI for data analysis in the first place: How can I trust the results?

Today's lesson is about the Delegation Diligence Loop. Specifically, it is about building confidence in AI's analytical capabilities for your specific work by systematically testing it against data you already understand. By doing this, you can better understand how AI will support your specific circumstances.

The Process of Delegation

The process starts with delegation. Here is how this works:

  1. Identify a specific analytical task you do regularly that you want to delegate to AI.
  2. Find past data where you have already completed the analysis.
  3. Work with AI to reproduce what you did, evaluating what works and what doesn't.
  4. Refine your approach and test again.

If AI can match your known results, you know how to use it and trust it for similar future tasks. If not, you have learned that this specific task is something you should not delegate.

Case Study: Practical Application in Program Management

To see what this looks like in practice, let's look at Rio, the program director at Valley Veterans Services. Every quarter, he analyzes program attendance alongside employment outcomes, calculating participation rates, tracking monthly changes, and determining whether attendance correlates with job placement success. This analysis consistently takes him hours.

Setting Up the Test Case

Considering delegation, Rio knows he wants to continue using the results of this analysis to improve his program. He wants to interpret the results himself, but he could do without the data cleaning and formula mayhem he usually finds himself in.

To test whether AI is appropriate, he evaluates it using last quarter's data. He knows exactly what this data showed after he analyzed it without AI, and he has the raw, messy data from before he started. This is his test case.

Executing the Diligence Loop

Rio uploads the data and starts to work with AI, using description and discernment to perform his analysis. Each time the AI responds, Rio checks the results against what he knows to be true and jots down potential gaps in AI's reasoning.

  • Refining Description: Sometimes additional description helps AI get the outcome he is looking for. In these cases, Rio knows he must include that kind of information for future tasks.
  • Identifying Capability Gaps: Other times, Rio might find legitimate capability gaps. This diligence changes what he chooses to delegate in the future.

Iterative Testing and Refinement

Rio's first attempt might look like this: "I'm sharing attendance data and employment outcome data from our job training program last quarter. Please analyze the participation patterns across the three months and graph the correlations between attendance levels and employment success."

AI responds with a summary, but rather than assuming this is fact, Rio checks it against his records. He notices that while AI correctly identified the correlation, it missed a critical insight regarding the combined housing assistance and job placement program.

Rio refines his description, asking AI to try again but pay special attention to the program type. This time, AI catches its mistake. Rio notes that for future quarters, he will need to specifically request the AI to consider the program type.

Next, he tests something harder: "Can you also look at this based on when participants enrolled?" Rio observes that despite not having the enrollment data explicitly formatted, AI could help extract it. He makes a note to cross-reference these results later.

Validated Confidence vs. Guesswork

By going through this process, Rio has systematically validated what AI can and cannot do for his quarterly reporting. He has learned that with the right description, AI can accurately reproduce manual analysis. However, he has also identified clear limitations:

  • AI needs explicit enrollment dates to do cohort analysis; otherwise, it tries to infer them inaccurately.
  • He now has a tested approach and clear notes on what context he needs to add himself.

When Rio uses this validated approach with new data, his diligence continues. He checks whether numbers make sense, takes accountability for the final report, and remains transparent about AI's role. He is now working from validated confidence, not guesswork.

Framework for Data Delegation

If you want to apply this yourself, follow this framework:

  1. Identify a specific analytical task that you want to delegate. Be precise about what you need.
  2. Find past data where you already completed that analysis. You need the right answers to evaluate whether AI can arrive at them.
  3. Work with AI to reproduce your past analysis and systematically evaluate the results.
    • What did AI produce?
    • How did it approach the task?
    • How did it communicate findings?
  4. Identify gaps, refine your delegation, and test again.

If you can validate that AI produces correct results, you have built an approach you can use on new data. If you cannot get there after several refinements, you have learned that this is not a task you should delegate.

Support for Non-Data Experts

What if you are not comfortable with data to begin with? AI can still be a useful tool to brainstorm and implement solutions. Because AI models are uniquely good at coding, they can help with:

  • Writing Excel formulas.
  • Reformatting messy data.
  • Explaining complex data concepts.

In these cases, bring your question to AI and ask for help understanding what a solution could look like, just as you would work with a data analyst on your team. Ask for clarifications and explanations so you can follow the process and understand the final output.

Final Principles of AI Analysis

Validation builds confidence, but it does not eliminate responsibility. You are still accountable for checking that results make sense and being transparent about AI's role in your analysis process.

This testing works for any analytical task:

  • Donor analysis.
  • Budget forecasting.
  • Survey synthesis.
  • Outcome tracking.

Test first, validate what works, and then apply with confidence—or learn what you shouldn't delegate at all.

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