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From Queries to Conversations: Humanizing Data Analysis in Higher Ed

By Anna Kourouniotis posted 12 days ago

  

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 InsightRecommendationExpected 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:
    • Why do we need this new Tableau dashboard?

    • Why is that decision unclear without it?

    • Why is that process confusing now?

    • Why does that confusion keep happening?

    • 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!


#DataAnalysis #highereducation #BusinessAnalyst #Design_Thinking

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6 days ago

@Ernesto Crucet ,thanks for mentioning the momentum factor! I can definitely relate to what happens after delivering a dashboard or report. Often, the people we build these tools for use them once or twice when they first receive them, but then rarely go back—even though the tool was designed to meet a recurring, long-term need.

It makes me wonder why that happens. We keep refreshing the data, maintaining dashboards, and tracking analytics on usage—but at what cost? Is the product still making an impact? Is it still serving its original purpose?

Once a year, I reach out to our Duke server dashboard users to ask whether the dashboards are still useful or if their needs or roles have changed. Sometimes that quick check-in reveals that what used to be valuable no longer is. How do you approach maintaining engagement and keeping that momentum going?

6 days ago

This is absolutely spot on! Thank you for highlighting this, Anna.

Too many times I've worked hard on presenting data, only for the momentum to stop right after the meeting. The real hurdle is inspiring stakeholders to look past the dashboard and find the stories within the numbers. Moving from mere reporting to active conversation and storytelling is definitely where we make our biggest impact and get them truly invested.

12 days ago

@Jeffrie Brooks, I often refer to Green's book, Effective Data Visualization, when I get stuck in my own head. Highly recommended read. That along with Luptons's Design Is Storytelling and Knaflic's Storytelling with Data

Love your reference to the "Big Eyeball Brains!" What an impact that has on one's mindset and approach to designing digital products in general.  Thanks for engaging with me! 

12 days ago

Great read! Thanks for providing this.

Knowing your audience is so key to any successful data-visualization tool. I am a big fan of Stephanie Evergreen who refers to people as "Big Eyeball Brains" and explains how it important to gauge folks' 1) knowledge of the data, and 2) interest in the data, in any tool/chart that you design.

Anna, thank you for reminding us that the human piece is the most important one when it comes to delivering data... and really just in general.