Inspired by Alex Green and the HEUG community
Just yesterday, I found myself deep in conversation with my HEUG friend, @Alexandra Green, about the intersections of analytics, human relationships, and the daily grind inside PeopleSoft, Tableau, Power BI, BI Publisher, and Artificial Intelligence. Our takeaway? The best data work starts with people—not platforms. This post unpacks the five most common traps analysts fall into, and what to do about them, based on years of creating and reviewing reports across campuses and institutions.
1) Start with the Question, Not the Tool
It’s easy to get excited by Tableau’s latest feature or a slick Power BI integration. But the best work starts with a real question.
- Begin by asking what decision needs solving.
- Match the lowest-friction tool to the need—even if it’s just a PeopleSoft query and a quick Excel chart. Leverage what you have.
Research shows the greatest returns come when analysts prioritize business acumen and stakeholder co-creation rather than chasing platform capabilities.
2) Tailoring for the Audience
The front page of a dashboard presents the same data, but not everyone needs the same detail. VPs, directors, and front-line staff all approach the information differently.
If you want your analysis to land:
- For executive leaders, focus on risks, strategy, impact, and decision points.
- For mid-level managers, highlight KPIs, priorities, and trade-offs.
- For front-liners, offer week-to-week next steps in clear language.
Humanizing research emphasizes that context and audience framing are essential for insights to be both technically robust and practically impactful.
3) Why Technical Detail Isn’t Enough
Ever built the perfect query, cleaned fifteen years of data, and dazzled with a complex dashboard, only for stakeholders to shrug? That’s because most decision-makers want meaning, not mechanics. Research shows that translating technical analysis into actionable, contextual stories dramatically improves stakeholder engagement and confidence.
Try this instead: Anchor every report in the Insight → Recommendation → Expected Benefit framework. Always ask: “What changes if we act on this?” and “What stays the same if we ignore this?” Keep your SQL prowess as a bonus, not the feature.
4) Define and Reframe the Real Problem
Stakeholder requests (“give me a Tableau dashboard on enrollment”) can miss the true need.
Before building anything, probe deeper:
- Use “How might we...”
- How might we surface meaningful, timely insights for advisors without overwhelming them with dashboards?
- How might we involve non-technical stakeholders earlier so our PeopleSoft queries and BI reports actually match their mental model?
- How might we design our Tableau/Power BI/Oracle Analytics content so that conversations, not visuals, become the main driver of decisions?
- Use “5 Whys” interviews to get clarity. For Example:
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Why do we need this new Tableau dashboard?
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Why is that decision unclear without it?
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Why is that process confusing now?
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Why does that confusion keep happening?
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Why hasn’t that underlying issue been fixed yet?
- Run short discovery interviews with users to validate the real problem.
Pairing data with lived experience leads to more relevant questions and solutions than metrics alone.
5) Build Relationships, Not Just Reports
Dashboards and reports aren’t finish lines—they’re the start of two-way dialogue.
Shift to a relationship mindset:
- Check in after each release: “What helped, what didn’t?”
- Invite stakeholders to review and iterate.
- Return to completed projects to study real impact.
Ongoing dialogue is at the core of humanized data strategy and drives lasting change.
Call to Action: Be a Human Analyst
Analytics is powerful, but only when rooted in collaborative discovery, conversation, and care. This blog is an invitation for analysts in higher education:
- Step out from behind the dashboard.
- Have more interviews and informal chats.
- Invite feedback, and make your work visible as you build.
Want to share your own “human side” analytics story? Drop a comment or reach out for a data human-centered conversation. And don't forget to download the Humanizing Data Analysis infographic as your reference guide. Also, look out for a future webinar on the topic!
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