Start With a Dashboard Question

Before opening Power BI, decide which business decision the dashboard should support. A useful starting question is which products generated the most revenue last month and where returns reduced the result. That question determines the fields, date range, and comparisons you need. For a sales spreadsheet, a focused first version might track revenue, units sold, return rate, and revenue by region.

Identify who will use the report and what action they should take after viewing it. A store manager may need regional performance and low-stock products, while a marketing manager may need campaign cost and conversion data. Write these needs as three to five questions instead of collecting every available column. This keeps the first page useful and gives you a clear test for every visual you add.

Audit the Messy Spreadsheet

A messy spreadsheet often contains several tables on one sheet, decorative title rows, merged cells, blank lines, inconsistent names, and numbers stored as text. Start by checking the first few rows to find the true header row, then inspect column names and data types. For example, a date column may contain 2025-01-05, 05/01/2025, and the word Pending in the same field. A revenue column may also include currency symbols or spaces that prevent correct calculations.

Create a short data audit before making changes. Check whether each transaction has a unique order ID, whether product and region names use consistent spelling, and whether blank cells mean missing information or zero activity. If the same order ID appears twice with identical values, it may be a duplicate; if it appears on two product lines, it may be a valid multi-item order. Record these decisions because they will affect totals and make later troubleshooting much easier.

Clean Data with Power Query

Use Power Query to turn the raw worksheet into a consistent table without manually editing every cell. Import the file, remove title rows and empty rows, promote the correct row to headers, and assign explicit types to dates, whole numbers, decimals, and text. Then trim extra spaces, standardize values such as North, north, and N, and replace truly missing numeric values only when the business rule supports it. Keep the original spreadsheet unchanged so the cleaning process can be repeated when a new file arrives.

Name each transformation step clearly and inspect the preview after important changes. If monthly files have the same columns, combine them through a folder query instead of copying and pasting rows into one workbook. If a column contains City and Region in a single value such as Hanoi, North, split it into separate fields before loading the data. A repeatable query is more reliable than a one-time cleanup because it documents exactly how the source became report-ready.

Build a Reliable Data Model

After cleaning, load the data into a simple model that separates transactions from descriptive information. A central Sales table can contain OrderID, Date, ProductID, RegionID, Quantity, SalesAmount, and ReturnAmount. Separate Product, Region, and Date tables can hold product names, category labels, regional names, and calendar attributes. This structure prevents repeated labels from creating confusing calculations and makes filtering more predictable.

Create relationships using stable keys such as ProductID and RegionID rather than names that users may spell differently. The usual pattern is one product row related to many sales rows, with the Product table filtering the Sales table. Add a proper Date table if you need month, quarter, year, or year-to-date analysis, and mark it as the date table in Power BI. Test the model by selecting one product and confirming that the related revenue and transaction count change as expected.

Create Measures and KPIs

Use DAX measures for calculations that must respond to filters instead of hard-coding results in separate spreadsheet cells. A basic measure can be written as Revenue = SUM(Sales[SalesAmount]), while Returned Amount = SUM(Sales[ReturnAmount]) calculates the selected return value. A rate can use Return Rate = DIVIDE([Returned Amount], [Revenue]) so the report handles a zero denominator safely. These measures will recalculate when a user filters by month, product, or region.

Start with a small set of metrics whose definitions are easy to explain. For example, show total revenue, units sold, return rate, and average order value across the top of the report. Confirm whether average order value should divide revenue by distinct orders rather than by individual product rows. Give each measure a clear name and format currency, percentages, and decimal values consistently so the numbers can be interpreted without opening the source file.

Choose Effective Dashboard Visuals

Match each visual to the question it answers. Use a line chart to show revenue across months, a bar chart to compare products, and a table when someone needs exact order-level details. A card can display total revenue, but it cannot explain why revenue changed, so pair it with a trend or comparison visual. Add slicers for date, region, and product category only when they help users investigate a decision.

Keep the page easy to scan by placing key metrics at the top, the main trend in the center, and supporting detail below. Sort product bars by revenue when ranking performance, and use the same color for the same category across related visuals. Avoid putting several charts on one page when they repeat the same comparison or introduce unrelated questions. Check the page at normal browser size and verify that axis labels, legends, tooltips, and table columns remain readable.

Validate, Publish, and Improve

Validate the dashboard against the source before sharing it. Filter the report to one month and one region, then compare the revenue and order count with a manually checked spreadsheet sample. Investigate any difference caused by duplicate rows, excluded blank dates, return logic, or an incorrect relationship rather than changing the number until it looks familiar. Also test slicer combinations, empty selections, and products with no returned items to make sure the visuals behave sensibly.

Build the report pages in Power BI Desktop, publish them to the Power BI service, and pin selected visuals to a dashboard when a single monitoring view is useful. Confirm that data source credentials, refresh settings, and workspace permissions are configured for the intended audience. Add a short definition for each KPI, including its numerator, denominator, and date rule, so future users do not interpret return rate differently. After people use the dashboard, review which filters and visuals support real decisions and remove elements that add noise without providing evidence.

Further Reading

Power BI

Power Query

DAX