What a business intelligence case study actually tests
A business intelligence case study hands you a company, a decision and a pile of data — dashboards, extracts, maybe a survey — and asks what the company should do. The grading rarely centres on the tooling. What's being tested is a translation skill: can you turn a vague business question ("why are users dropping off?") into measurable drivers, then turn those drivers into actions a manager could actually approve?
That's why the most common failure mode isn't wrong analysis — it's recitation. A paper that says "sales fell 4% in Q3, website visits fell 6%, the NPS survey shows complaints about delivery" is a caption, not an insight. The numbers are connected or they aren't; your job is to say which, and prove it from the exhibit. If your assignment is really a general business analysis with a data exhibit attached, the broader method in how to write a business case study analysis applies, and this guide stacks on top of it.
Three moves separate strong BI papers from weak ones, and they run in order: decompose the goal into drivers (the KPI tree), interrogate the data's quality before trusting it, and land on a recommendation that names what would change and who would own it. Everything below follows that spine.
Start with a KPI tree, not a chart
A KPI tree (or driver tree) breaks one headline goal into the smaller metrics that mathematically or behaviourally feed it. A revenue goal splits into traffic × conversion × average order value; each of those splits again. The tree does three jobs at once: it structures your analysis, it tells you which exhibits matter, and it gives you a defence when a professor asks "why did you look at that metric?" — because every metric on the tree has a parent.
Build yours here. Give the goal, name two or three drivers, and list the sub-metrics under each, one per line. The tree rebuilds as you type, and the copy and download buttons give you a text version you can paste into your draft. Nothing you type leaves your device.
KPI tree builder
100% private — runs in your browserThe tree is a scaffold, not an answer: it organises which metrics you interrogate in the case exhibits.
Keep the tree shallow. Two levels of drivers are almost always enough for a coursework case; a third level belongs in an appendix, if anywhere. If a driver has no metric the case can actually evidence, that's a finding too — say so in the limitations paragraph instead of inventing a number you can't cite.
From dashboard exhibit to insight paragraph
Dashboards appear in these cases as screenshots of charts with a time axis, a few segments and one or two suspicious divergences. Read them in this order: axis and grain first (daily or weekly? months or quarters?), then the comparison (against what — last period, target, another segment?), then the anomaly. Only then write.
The rewrite below shows the difference between a caption and an insight, using an illustrative delivery-app dashboard for a fictional grocer. Same data, two very different grades. Tabs — click between them.
"As we can see from the dashboard, weekly active users have been declining for several weeks while average order value has remained relatively stable. This suggests that fewer people are using the app. Delivery complaints have also increased according to the survey data."
Every sentence repeats a line on the chart. Nothing is explained, compared or connected — a reader learns nothing the exhibit didn't already say.
"The decline is concentrated in repeat users, not new ones: weekly actives fell while first-order volume held flat. Because average order value stayed stable, the revenue problem is a frequency problem — users aren't leaving after signing up, they're ordering less often. The timing matches the complaint spike about late deliveries in the survey exhibit, which makes delivery reliability the first hypothesis to test, ahead of price or assortment."
Three things changed. One: the strong version segments the decline (repeat vs new) instead of averaging it. Two: it uses a second metric as a control (stable order value rules out basket-size explanations). Three: it ends in a testable next step. Compare, control, conclude — that's the pattern worth copying into your own paragraphs.
Notice the strong version never says "the data shows that…" as filler. If your draft is full of that phrase, run it through the readability checker — filler clauses are usually hiding inside your longest sentences, and the tool finds them fast.
An illustrative KPI table for a delivery operation
When the case gives you freedom to choose metrics, a small table like this one earns structure marks fast — four to six KPIs, each with a definition and a watch-out, all tied to the tree above. This example is illustrative (Northline Groceries is fictional), so adapt the rows to whatever your exhibits actually support.
| KPI | What it measures | Watch-out when analysing |
|---|---|---|
| Weekly active users | How many customers used the app in a week | Definition matters: opened the app, or placed an order? Cases love this ambiguity |
| Orders per active user | Frequency of use among active customers | Skewed by promotion spikes; check for a promo week before declaring a trend |
| Average order value | Size of the typical basket | Rises when frequency falls — the two often move together, not independently |
| On-time delivery rate | Share of orders arriving inside the promised window | Check whether the clock starts at payment or at kitchen handoff |
| Cost per delivery | Fulfilment economics per order | Batching effects: dense hours look cheap, sparse hours look terrible |
| Complaint rate post-delivery | Issues reported after the order lands | Only survey respondents — late-night and occasional users are underrepresented |
Illustrative rows — the watch-outs are the kind of thing a marker looks for, whatever the case's actual metrics are.
The watch-out column is the one that scores. Anyone can define a KPI; showing you know how each metric can mislead is the analytics maturity the rubric usually hides under words like "critical evaluation".
Data quality checks before you trust the numbers
BI cases frequently hide a data quality problem on purpose: two sources disagree, a segment goes missing, a definition changed mid-year. Spotting it is often worth more marks than any chart gymnastics. Run every case through this six-point check before you build an argument on the numbers — tick them off as you go.
- Trace one KPI back to where it's captured — app event, till system, survey?
- Check for missing segments: new customers, refunds, a closed region?
- If two sources report the same metric, compare them and note the gap
- Note the time grain — daily data supports claims weekly data cannot
- Flag definitions that differ between departments in the case
- Write one sentence on a decision the data cannot support (honest limits)
The standard quality dimensions behind this list are the ones data teams use in practice: accuracy (is it correct), completeness (is anything missing), consistency (do sources agree), and timeliness (is it fresh enough for the decision). You don't need to recite them — showing one real instance from the case is worth more than the vocabulary.
Worked example: Northline Groceries, delivery diagnosis
Here's how the pieces assemble. Illustrative scenario, fictional company: Northline Groceries runs a delivery app for its regional supermarket chain. The board sees weekly active users declining for two months and asks whether to spend more on acquisition. The case exhibit includes an app-analytics dashboard, an order-management extract and a post-delivery survey.
The KPI tree
Goal: revenue from the app. Drivers: orders per active user, number of active users, margin per order. The tree immediately reframes the board's question — acquisition (growing actives) is only one of three levers, and the exhibits suggest the leak is elsewhere.
The data quality finding
The analytics dashboard and the order system disagree on order counts. Tracing one cancelled order through both shows why: the app analytics counts checkout events, while the order system counts completed orders after cancellations and payment failures. The "decline" partly evaporates once the two sources are reconciled — a finding worth a paragraph on its own.
The insight and the recommendation
Among reconciled orders, frequency fell among repeat users while new-customer volume held. The survey's late-delivery complaints cluster in the same weeks. Recommendation: fix delivery reliability before buying more installs — starting with the two postcodes with the worst on-time rates — and re-measure frequency after eight weeks. The board's original plan (more acquisition spend) would pour water into the same leak.
The full paper version of this example would follow the structure in how to write a case study — executive summary, situation, analysis, recommendations — with the KPI tree as the backbone of the analysis section. If the situation side needs a classic framework instead, the SWOT analysis generator covers the internal and external scan.
Structure, mistakes and the final pass
BI papers sink under weight: too many KPIs, too many charts, recommendations that arrive exhausted on page twelve. Hold the line at six metrics in the main body, one exhibit reproduced (if the brief allows) and one recommendation per problem you actually diagnosed. A recommendation should name the action, the metric it moves and how you'd know it worked — that last part is what "measurable outcomes" in a rubric means.
Three mistakes to hunt in your own draft: captions masquerading as insights (fix: compare, control, conclude); unexamined data (fix: the six-point check above); and recommendations that ignore the constraint the case planted — budget, headcount or regulation. And keep the words inside budget: the word counter tracks your section budget while you draft, so analysis doesn't starve the conclusion.
Stuck on the analysis section?
If the dashboard exhibit refuses to turn into an argument, a quoted writer with analytics coursework experience can draft or restructure the analysis against your brief and deadline — or coach you through it, your call.
Frequently asked questions
What does a business intelligence case study look like?
Typically a company scenario plus data exhibits: dashboard screenshots, spreadsheet extracts, sometimes survey results. The questions ask you to diagnose what the data implies and recommend actions. Some courses add a practical component — building a small dashboard — but the written diagnosis is where most marks sit.
How many KPIs should I include in the analysis?
Four to six in the main body, organised under two or three drivers from your tree. More than that and each metric gets a sentence of attention instead of a paragraph of analysis. Extra metrics can go in an appendix where they cost no words and show diligence.
How do I analyse a dashboard exhibit without just describing it?
Use the compare–control–conclude pattern: segment the change (which users, which region), control it against a second metric that should move if the explanation is right, and finish with the most plausible cause and how you'd test it. If a sentence could be read off the chart without thinking, it doesn't belong in the analysis.
What if the case data contradicts itself?
Say so, precisely. Name the two sources, the direction of the disagreement and the most likely reason (definition, timing, cancellations). A short paragraph on why you trust one source for a given decision is a high-scoring move — it shows the marker you'd survive a real analytics job.
Which frameworks fit a BI case study?
The KPI tree is the backbone. Around it: SWOT for the situation scan, a simple funnel for user journeys, and a cost–benefit list for the recommendation. Avoid forcing PESTLE or Porter's Five Forces unless the case genuinely turns on macro or industry structure — framework-forcing is easy for graders to spot.
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