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What the JCars dashboard was built to show
The source dataset, named Jcars_data, combined customer, sales, delivery, and pricing information across 32 columns. According to Wainaina, it had accumulated inconsistent entries over time and lacked validation rules. The report was organized into six pages covering an executive view, products, geography and sales team, customers and payments, and logistics and delivery. The article groups some page themes in its narrative, so it does not provide a page-by-page specification.
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The project followed a useful analytical sequence: clean inconsistent values, structure the data into fact and dimension tables, create DAX calculations, then present the results in report pages. The article does not publish enough model detail to reproduce the relationship cardinalities or audit each measure, so the reported outputs should be read as the author’s dashboard results.
How the source data was cleaned
Normalize categories without silently dropping values
Customer-type entries included variants such as corp, CORPORATE, govt, n.g.o, and dealer. Wainaina mapped known variants to canonical labels and used Proper Case as a fallback for unmapped values rather than discarding them. The first row was promoted to headers, while blanks and placeholders such as N/A and unknown were converted to nulls.
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This approach improves grouping, but the mapping itself is an analytical decision: a misspelled or unfamiliar category can remain as a distinct Proper Case value. A production workflow should make those unmapped values easy to review instead of assuming the fallback makes them equivalent.
Standardize dates while preserving assumptions
The order and delivery date columns contained Excel serial numbers and multiple text formats. The project treated ambiguous slash-separated dates as day/month/year, all-digit strings as Excel serials, and two-digit years as 20xx; unparseable placeholders became null. Those rules can change which period a transaction appears in, particularly where day and month could both be valid, so date-based trends depend on these interpretations.
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Convert currencies using a fixed project assumption
The author standardized five money fields—unit selling price, unit cost, delivery fee, logistics cost, and recorded revenue—to KES. The project used fixed rates of KES 1.00 per KES, 129.20 per USD, 147.67 per EUR, and 7.84 per ZAR. It did not give a rate date or provider, and it applied one rate across all transactions regardless of transaction date. Values without a currency marker were assumed to be KES. These are the project’s conversion assumptions, not verified historical exchange rates.
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What the dashboard reported
All figures below are reported by the project author in the DEV Community article; they are not independently validated company accounts. The source does not establish whether returned or cancelled sales were excluded from headline revenue.
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| Measure or finding | Reported dashboard result |
|---|---|
| Units sold | 458 |
| Total revenue | Ksh 1.46bn |
| Gross profit and margin | Ksh 26.43M; 1.80% |
| SUV revenue and margin | Ksh 844.86M, described as 57.7% of revenue; 9.14% margin |
| Returns and cancellations | 149 returned units; 37 cancelled transactions |
| Logistics cost and delivery fees collected | Ksh 27.78M; Ksh 24.91M |
| Highest and lowest branch revenue | Thika highest; Nairobi lowest |
| Largest region by revenue | Rift Valley, Ksh 377.47M |
| Leading make by revenue and model by units | Toyota by revenue; Harrier by units |
The combination of Ksh 1.46bn in reported revenue and a 1.80% gross margin is the central management signal: high sales volume or revenue does not necessarily mean a strong profit buffer. It is a reason to investigate cost and transaction treatment, not proof of a particular operational failure.
Where reported margins were strong or weak
Vehicle categories
The author reported negative margins for sedans (-16.24%), trucks (-65.57%), vans (-27.67%), and crossovers (-9.84%). SUV revenue was reported at Ksh 844.86M with a 9.14% margin. These figures identify categories worth examining, but the source does not establish why the negative margins occurred. Purchase cost, discounts, and delivery costs are possible lines of investigation, not demonstrated causes.
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Regions
Reported regional margins were -4.13% for Central, -8.90% for Nyanza, -16.77% for Nairobi, and 4.58% for Rift Valley. The dashboard also ranked Rift Valley highest in regional revenue at Ksh 377.47M. Revenue leadership and margin leadership are different questions; neither result alone explains the other. The article does not establish whether mix, discounting, or cost caused the margin differences.
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Instagram (17.20%), WhatsApp (12.51%), and Facebook (10.09%) were reported with positive margins, while referral (-22.21%) and corporate tender (-18.02%) were negative. These are observed associations in the project data, not evidence that a channel caused a better or worse margin. Different channels may bring different vehicle or customer mixes.
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Customer groups and transaction status
Car dealers, government, NGOs, and corporates together accounted for about 83% of revenue in the dashboard. Wainaina also noted a cancelled order that still carried Ksh 4.38M revenue. The article does not establish whether that amount, or other returned and cancelled transactions, was included in headline revenue. That unresolved treatment matters when interpreting both revenue and margin.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to turn dashboard signals into a reliable investigation
- Reconcile transaction status first. Trace returned and cancelled records to the revenue and gross-profit calculations. Determine whether cancelled orders, returns, refunds, and replacement sales are included, reversed, or counted more than once.
- Inspect loss-making vehicle categories at the sale level. Compare selling price, purchase cost, discounts, and delivery costs for individual transactions. Check whether the category result is being driven by a few unusually costly or low-priced sales.
- Compare like with like across regions. Use comparable time periods and volumes, then examine vehicle mix, customer mix, pricing, and costs. A region’s total revenue ranking does not establish its profitability or explain its margin.
- Test channel results against product and customer mix. Compare the vehicles, customer types, sales values, and costs associated with each lead source before drawing conclusions about channel performance.
- Track margins over time. A trend view can help distinguish a persistent issue from a small number of unusual transactions. The source warns that small volumes can make make-level margin swings easy to over-read; unit counts should accompany comparisons.
- Make assumptions reviewable. Keep category mappings, null handling, date parsing rules, and currency conversions explicit. If the report is refreshed with new data, review unmapped categories and ambiguous dates rather than allowing silent changes to alter comparisons.
The project demonstrates how Power Query cleaning, a fact-and-dimension model, and DAX measures can turn inconsistent operational data into questions a business can investigate. Its most valuable output is not a causal explanation: it is a set of specific signals—thin overall margin, category and regional losses, returns, cancellations, and delivery economics—that call for transaction-level reconciliation.
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