iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
A JCars Logistics Power BI case study by Victoria Ndei describes a full path from a raw vehicle-sales CSV to an interactive report: inspect and clean the data, define business rules, build a star-schema model, create reusable DAX measures, and present comparisons that support investigation. The workflow is useful as a practical example, but its calculations and observations are author-reported project work—not independently audited results about JCars Logistics.
What the JCars Logistics dashboard project describes
Ndei’s walkthrough starts with a CSV containing vehicle sales, customer, financial, operational, and location-related information. It follows the work through data investigation, transformation, modelling, DAX, dashboard design, analysis, and recommendations. The account explains how an analyst can structure a business-intelligence project; it does not establish company-wide performance or independently verify the dataset’s provenance.
The order matters: a dashboard can make data easier to explore, but it cannot correct a mistaken transaction count, currency interpretation, or revenue rule on its own. The project therefore treats source inspection and documented transformations as part of the analysis, not as preliminary chores to hide from report users.
Why data investigation comes before visualization
The article describes checking for missing and blank values, nulls, duplicate or repeated records, invalid or negative numbers, text inside numerical fields, inconsistent currency symbols and labels, “K” and “M” suffixes, inconsistent dates, placeholders, out-of-range ratings, discount formats, and inconsistent category values.
#1 Best Overall
These checks identify questions, not automatic deletion rules. A negative value could reflect a refund, adjustment, or other legitimate business event; an unusual value could also be a data-entry problem. The analyst needs to understand the field and its business context before correcting, excluding, or reclassifying a record. Otherwise, a seemingly tidy dataset may produce misleading totals.
Cleaning choices affect the conclusions
Ndei describes setting data types, cleaning text, handling blanks and placeholders, standardizing monetary and numerical fields, normalizing discounts and ratings, standardizing dates, and applying documented business rules. Each transformation can affect later KPIs. For example, treating a text-formatted amount as missing is not equivalent to converting it correctly, and removing a repeated-looking row is not safe until the transaction grain is understood.
A useful practice is to preserve the original source, record each transformation, and make assumptions visible in the report or supporting documentation. That gives another analyst a way to trace a KPI back to its inputs and distinguish a data correction from a business-policy decision.
How the project defines revenue and missing values
For this project, the author reports using Kenyan Shillings as the reporting currency and defines revenue as:
Rank #2
Revenue = Units Sold × Unit Selling Price × (1 − Discount) + Delivery Fee
This is the project’s stated calculation, not a confirmed JCars accounting policy. It relies on the fields representing compatible units and currency, and on the discount being expressed in a form that can be used in the equation. Analysts applying a similar formula should confirm whether delivery belongs in the revenue measure they need and whether the source values use consistent definitions.
The article also reports these missing-value assumptions:
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Missing discounts were treated as zero.
- Missing delivery and logistics fees were treated as zero.
- Missing ratings were assigned a neutral value of 3.
These choices make the project’s measures computable, but they are assumptions rather than verified company policy. Filling a missing discount with zero, for instance, treats “unknown” as “no discount”; assigning a rating of 3 changes the rating distribution. A report should disclose such treatments so readers can judge how they affect results.
The author says foreign-currency labels were not converted when a defensible exchange rate could not be established. That is a cautious choice: mixing currencies without a justified conversion would make monetary comparisons unreliable. The article does not establish that every source amount was independently reconciled to accounting records.
How the data model supports analysis
The walkthrough describes a star schema with a central FactSales table and descriptive dimensions for vehicle, customer, location, and date. Transaction-level measures sit in the fact table; descriptive attributes used to filter and group those measures sit in dimensions. This structure is intended to make filtering and maintenance clearer than a single table combining every attribute and measure.
The author describes reusable DAX measures for revenue, units, orders, average order value, costs, gross profit, profit margin, profit per vehicle, customer activity, ratings, delivery and logistics, returns, cancellations, discounts, payment, rankings, and time analysis. The value of a measure library is consistency: the same definition can be reused across report pages rather than recreated slightly differently in each visual.
Measures still depend on correct definitions and grain. “Orders,” for example, needs a clear rule for what counts as one order, while revenue and margin depend on the project’s treatment of discounts, costs, currencies, and exceptions. A well-organized model makes those rules easier to apply; it does not prove they match an organization’s official reporting policy.
Rank #4
How the report moves from overview to investigation
The report is described as progressing from an executive overview to more detailed analysis. Its pages cover sales; profitability and branch comparisons; branch drill-through; operations and customer experience; investigations and exceptions; and management insights. Slicers include year, month, branch, location, vehicle type, make, model, and payment method. Page navigation, a branch tooltip, interactive filtering, and drill-through let users move from a high-level view toward a particular slice of the data.
For branch, vehicle, period, or payment-method comparisons, the report’s measures should be read together rather than in isolation. Revenue and units or orders show scale; cost, gross profit, and margin add profitability context; logistics costs, delivery status, returns, and cancellations can help explain operational differences. Comparisons are meaningful only when filters, units, transaction grain, and exception handling are consistent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions the dashboard is designed to investigate
The author frames the analysis around questions such as these. They describe what the report is meant to explore, not findings that can be treated as established company results.
Recommended Free Tools
How does profitability vary across branches?
Compare branches using costs, gross profit, margin, and profit per vehicle alongside revenue. A branch with greater sales may also have higher costs or a different transaction mix; revenue alone cannot establish which branch is more profitable.
How do discounts relate to profitability?
Examine discount levels alongside margin and profit, while checking that discount values were normalized consistently and that other relevant costs are represented. A relationship in the report is an analytical pattern to investigate, not proof that discounts caused a change in profit.
How does revenue vary by payment method?
Use a consistent revenue definition and compare payment-method groups across the same periods and filters. Differences in transaction mix or missing classifications can affect the apparent pattern.
What operational patterns exist around returns and cancellations?
Review returns and cancellations with delivery, logistics, and sales measures. Verify how those events are represented in the source data before interpreting their effect on revenue or customer experience.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →How does performance change over time?
Use the date dimension and consistent time filters to compare periods. Confirm that dates were standardized and that periods are comparable before attributing a rise or fall to a business change.
What the project can—and cannot—support
The walkthrough supports a practical lesson: investigate source quality, document calculation and missing-value rules, model the data around the questions, and pair financial measures with operational context. It also describes project-level analysis and recommendations by the author. Those are not independently audited findings about JCars Logistics, and the article does not cite an organization-published statistic or an official company statement validating the results.
A separate public GitHub repository describes a different project with 276 rows and 32 columns, and discusses currency ambiguity, conflicting revenue fields, missing data, spelling variants, and questionable transactions. Its author says their own gross-margin interpretation changed after excluding two suspicious transactions. Those details belong to that repository’s project; they should not be combined with Ndei’s dataset or presented as JCars’ verified records or performance.
In either project, an anomaly is a reason to validate a record against source documents and business context—not an automatic reason to delete it. The distinction between a project calculation and a verified company result is essential when a dashboard is used to inform management decisions.
Free tools Windows power users keep installed
One-click scans. No signup required.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
