Introduction
When we asked IR offices what they report with, Tableau and Power BI were on the list, alongside Argos, Informer, SSRS, and Excel. That came from the interviews behind our data request workflows whitepaper: 50+ IR and IE professionals across 19 states. Plenty of IR teams have built years of good work in one of the two.
This is not a post about replacing them. BI tools are the right home for a specific kind of question, and they are very good at it. The problem IR offices describe is the other kind of question: the one that arrives once, from a dean or a cabinet member, and does not match any dashboard anyone built. We will be fair to both products, cite their own documentation for what they can do, and be specific about where the hand-off to IR happens.
What BI does well on campus
- Recurring, governed metrics. Census enrollment, fall-to-fall retention, credit hour production, and admissions funnels are defined once, modeled once, and published the same way every term. That consistency is exactly what boards and accreditors want.
- Row-level security. Power BI restricts rows for users through roles defined with DAX filters on the semantic model (Microsoft Learn). Tableau offers user filters, dynamic filters on a security field, data policies on virtual connections, and database-level security (Tableau Help). One published report can safely serve a provost, a dean, and a chair.
- Visual exploration by trained users. An analyst who knows the model can slice a well-built dashboard faster than any request queue.
- Institutional familiarity. Power BI often arrives through a campus Microsoft agreement, Tableau is a Salesforce product, and both have large user communities, so you can usually hire or train for them.
Natural language inside BI, as of late 2026
Both vendors have offered plain-language querying for years, and both have changed course recently. If you evaluated these features a few years ago, re-check them.
In Power BI, Microsoft is retiring the original Q&A feature. Its Q&A documentation says Q&A experiences are going away in February 2027 and recommends Copilot instead. Copilot for Power BI requires a paid Fabric capacity (F2 or higher) or Power BI Premium capacity (P1 or higher); a Pro or Premium Per User license on its own is not enough, and trial capacities are not supported. For institutions on Pro licenses, that is a budget conversation, not a toggle.
In Tableau, Ask Data and the earlier Metrics feature were retired in Tableau Cloud in February 2024 and in Tableau Server 2024.2. Tableau Pulse now carries metric monitoring, built on metric definitions, and its enhanced Q&A lets users ask questions in their own words; Tableau's documentation lists that AI feature as requiring a Tableau+ license or a Try AI trial.
In both products, the natural-language layer answers questions about the data model you already built. That is a reasonable design, and it is also the boundary the rest of this post is about.
Where BI stops
The question is not in the model
A dean asks how many students who changed majors out of engineering in their second year graduated within six years, and in what. If nobody modeled major-change history, no dashboard or Copilot prompt over that semantic model can answer it. Someone in IR writes the query.
The comparison is external
Peer benchmarking needs IPEDS, College Scorecard, or other federal files joined to a peer list. Most campus BI models hold internal data only, so "how does our six-year rate compare to our Carnegie peers" becomes a download, a cleanup, and a new workbook.
The definition is the question
Many requests boil down to "which number should I use?" A dashboard shows retention under one definition. It cannot explain why the provost's slide and College Navigator disagree, or which definition the accreditor wants.
The person will not open the dashboard
In our interviews, 64.7% of institutions reported problems with self-service dashboards, led by data literacy gaps and BI underutilization (32.4% each) and terminology confusion (23.5%). As one respondent put it: "We have dashboards, but users still email us because they don't know where to look or can't interpret what they find."
It only needs answering once
Building a governed dashboard for a question that will never be asked again is poor use of a developer's week. These are the requests that pile up, because they are too small for a BI project and too big to answer from memory.
Which questions go where
| Question type | Example | Best home |
|---|---|---|
| Recurring operational metric | Census enrollment by college, every term | BI dashboard |
| Governed KPI for leadership | First-year retention trend for the strategic plan | BI dashboard, with the definition documented |
| One-off internal question | Graduation outcomes for students who left a specific major | IR analyst or an AI assistant connected to the warehouse |
| External benchmark | Our Pell graduation rate against 15 named peers | Federal data tool or assistant with IPEDS and Scorecard loaded |
| Definition dispute | Why our retention rate differs from College Navigator | IR, backed by a governed data dictionary |
| Repeat ad hoc question | The same peer comparison asked by three deans this year | Promote it to a dashboard |
A test to run on your own inbox
Before anyone argues for more BI seats, a new dashboard project, or an AI tool, look at what arrives. Take the last 30 days of requests from the inbox, the ticket queue, and the hallway, and sort each one into the rows of the table above. It takes an afternoon and settles most of the debate.
If most requests are recurring metrics that already have a dashboard, the problem is adoption and training, and the fix is a short session with each dean's office plus a link to the right view in every reply. If most are recurring metrics with no dashboard, you have a BI backlog, and building those views is a good use of developer time. If most are one-off internal questions, external benchmarks, or definition disputes, no amount of dashboard work will empty the inbox, because the questions were never dashboard questions.
Write the result down with counts. A sentence like "We got 74 requests last month and 51 of them needed data that is not in our Power BI model" (with your own numbers) is something a provost can act on. "We are swamped" is not.
Making BI and ad hoc answers work together
The practical goal is a clean split: dashboards for the questions you can predict, a fast path for the ones you cannot, and a habit of promoting any ad hoc question that keeps coming back into a governed dashboard. That last step is how the BI layer improves over time instead of drifting away from what people ask.
Three habits help. Log every request with its category, so you can see which ones repeat. Publish the definition next to every dashboard metric, ideally from one data dictionary, so the "which number" questions shrink. And measure how much of the team's week goes to one-off pulls; in our interviews, ad hoc requests consumed 40 to 60 percent of IR capacity, which is the case for treating them as their own workflow rather than as interruptions to dashboard work.
That workflow is what our data request management approach is built around: conversational intake that clarifies the ask, answers drafted from your warehouse and federal data with the source attached, and a human in IR who reviews before anything goes out. It sits next to Tableau or Power BI rather than replacing either; the case for a higher ed native assistant explains why we built it for the questions BI was not designed to take. For a scenario version of the same split, see how an IE director bridges the dashboard and email gap, and for the requests your team cannot take on, how to say no to data requests without burning bridges.
Sources
- Microsoft Learn, Row-level security with Power BI.
- Microsoft Learn, Power BI Q&A introduction (deprecation notice) and Copilot for Power BI overview (capacity requirements).
- Tableau Help, Overview of row-level security options in Tableau.
- Tableau Help, Ask Data retirement notice and Tableau Pulse.
- Clema, Understanding data request workflows in US higher education: interview percentages and capacity figures in this post.
Keep your dashboards. Clear the questions they miss.
Clema connects to the warehouse behind your Tableau or Power BI reports and answers the one-off requests in minutes, with sources.
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