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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Antonina Wambui’s JCars Logistics Power BI project shows how vehicle-sale records can be organized into views of revenue, profitability, customers, regional performance and operational exceptions. Her DEV Community article reports 276 transaction records and KSh 1.48 billion in revenue, but presents a project analysis—not an independently audited measure of the company’s performance. Its most useful lesson is that a dashboard’s conclusions depend on the transaction data, cleanup decisions and financial fields behind it.
The project’s results and methods below are attributed to Wambui’s DEV Community article. The article’s search result shows September 30 but not a year.
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Start with the transaction grain—and the data’s limits
Wambui describes an original dataset of 276 rows and 32 columns, with each row representing one vehicle sale transaction. The fields covered orders, customers, vehicles, geography, sales, finance, logistics, payment and customer experience. Establishing this grain matters: sales, costs, delivery status and customer ratings can be compared at the transaction level, while totals and averages should be interpreted according to which fields are populated and reliable.
The project article reports a range of quality problems: inconsistent order IDs and dates; implausible ages, including 0, 5, 121 and -5; mixed currencies; inconsistent category labels; questionable zero values; invalid vehicle years; inconsistent discount formats; invalid ratings; varying branch or yard names; and spelling differences in sales-representative names. Each issue can affect grouping, filtering or calculations. For example, spelling variants can split one representative across categories, while inconsistent dates can undermine time-based comparisons.
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Cleanup choices affect what the dashboard can claim
Wambui says the project standardized IDs and categories, converted valid dates, and set unrecoverable dates to null. Unreliable ages and discounts were also set to null; vehicle years were corrected only when supporting evidence existed. These are consequential choices: nulling a suspect value avoids treating it as true, but also means analyses relying on that field may omit records or have less coverage.
For currency normalization, the article lists USD/KES 129.54, EUR/KES 147.84 and ZAR/KES 7.93 as rates used in the project. It does not establish when those rates applied or provide an independent source for them, so they should be understood as project assumptions—not current exchange rates. The author also says some values bearing a corrupted “?” currency symbol were interpreted as USD when surrounding financial fields supported that reading. Such assumptions can materially affect converted revenue and profit.
Use a star schema to make analysis navigable
Rather than keep the data as one flat table, the project reshaped it into a star schema. Its central FactSales table holds transaction-level activity; surrounding dimensions provide descriptive context for slicing that activity.
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| Model component | Role in the analysis |
|---|---|
| FactSales | Transaction-level sales facts used for measures and comparisons. |
| Date | Time context for analyzing sales over dates. |
| Customer | Customer attributes for customer-level views. |
| Vehicle | Vehicle attributes, including categories used to compare performance. |
| Location | Geographic and branch context. |
| Sales Rep | Sales-representative context. |
| Payment | Payment information and status. |
| Lead Source | Channel or source context for sales. |
| Delivery Status | Delivery information and status. |
The described relationship pattern is one dimension row to many FactSales rows. That gives a consistent structure for filtering transaction measures by date, customer, vehicle, location, representative, payment, lead source or delivery status. It does not, by itself, make inconsistent source records accurate.
Read revenue alongside costs and margin
The article reports DAX measures for total revenue, gross profit, gross profit margin, return rate and logistics cost as a share of revenue. Gross profit is described as recorded revenue less units multiplied by unit cost. These measures are useful only to the extent that their underlying revenue, units, costs and currency conversions are dependable. Wambui notes that some unit cost and unit selling price values could not reliably be recovered, so margin results merit reconciliation rather than unqualified acceptance.
The project reports KSh 1.48 billion in total revenue and KSh 845 million in SUV revenue. The vehicle-type margins it reports are sharply different:
| Vehicle type | Reported gross margin |
|---|---|
| SUV | 8% |
| Sedan | -9% |
| Crossover | -6% |
| Van | -28% |
| Truck | -66% |
These are outputs reported by Wambui’s article, not verified company-wide performance indicators. The contrast illustrates why a high-revenue category is not automatically a high-profit category: revenue and gross margin answer different questions. The negative reported margins are a signal to examine unit costs, selling prices, discounts, currency conversion and missing financial values before drawing a business conclusion.
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Compare regions and branches on more than sales
The Regional & Branch Analysis page is described as comparing revenue and operating performance across locations. Wambui reports an average delivery time of 26.29 days for Nairobi, compared with 15.18 days overall. That gap can help identify a location for follow-up, but the article does not establish why delivery takes longer there. A useful regional view should therefore put revenue beside delivery time and logistics cost, rather than treating sales volume alone as a measure of performance.
Look for customer concentration without overstating identity
Wambui reports that the top 10 customers generated KSh 299.3 million, approximately 20% of reported revenue. This suggests a concentration question for management: how much business depends on a small group of buyers, and how should that exposure be monitored?
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The customer analysis has an important constraint: the source dataset had no unique customer identifier. The project’s Customer dimension was constructed from customer name, type and age. Consequently, apparent customer-level totals may not reliably distinguish people with similar details or connect the same customer across inconsistent records. The reported top-10 figure is a project result, not proof of a complete or deduplicated customer ranking.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Treat status conflicts as operational exceptions
The Operations & Exceptions page is described as bringing together delivery time, logistics costs, ratings and payment/delivery status. Wambui reports 14 status inconsistencies: 10 transactions marked paid while delivery was cancelled, and four marked payment cancelled but delivered. Those records are exceptions to investigate, not a basis for silently changing payment or delivery data. The article says conflicting records were left flagged rather than corrected without evidence.
A useful exception workflow is to review the underlying transaction and supporting operational or payment records, resolve the status with an accountable source, and preserve the correction trail. Until then, avoid relying on those rows for conclusions about completed deliveries, collections or cancellations.
What the five report pages are designed to answer
The article describes five pages that organize the project’s business questions into distinct views:
- Business Overview: A high-level view of sales and financial performance.
- Product & Sales Performance: Comparisons such as revenue versus gross margin by vehicle type.
- Regional & Branch Analysis: Revenue alongside delivery and logistics performance by place.
- Customers & Sales Channels: Customer concentration and lead-source analysis, subject to customer identity limitations.
- Operations & Exceptions: Delivery and logistics measures plus payment/delivery consistency checks.
The article also frames questions about whether customer ratings relate to transaction value and which categories, regions or branches warrant attention. A visible relationship between ratings and transaction value would be an association in the records, not evidence that one causes the other. The available reported findings do not provide a quantified rating-value relationship.
Keep distinct JCars project accounts separate
A separate LinkedIn profile excerpt attributed to Young Odhiambo describes a JCars Logistics dashboard built with PostgreSQL and Power BI. It reports 254 orders, 417 vehicles sold, KSh 1.38 billion in revenue and a 21% gross margin. Those figures differ from Wambui’s account, which describes 276 transaction records and KSh 1.48 billion revenue. The available descriptions do not explain whether the difference reflects distinct data versions, scopes or projects, so the figures should not be combined or treated as a reconciliation.
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How to use the analysis responsibly
- Use category revenue and margin together; a large sales total alone does not establish profitability.
- Investigate negative margins against costs, prices, discounts, currency assumptions and missing values before acting on them.
- Compare locations on revenue and operational measures such as delivery time and logistics cost; the reported figures identify questions, not causes.
- Review payment/delivery conflicts as transaction exceptions and retain unresolved status rather than guessing.
- Interpret customer rankings cautiously because the described model lacks a unique customer identifier.
- Distinguish reported project outputs from audited financial or company-wide performance measures.
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