#datacleaning

📊 From Raw Data to Business Strategy: An End-to-End KNIME Sales Analysis Workflow

I recently developed a complete sales data analysis workflow using KNIME Analytics Platform, transforming raw sales records into structured insights for business decision-making.

The workflow is organised into four key phases:

🔍 Phase 1: Data Preparation and Hygiene The process begins by importing CSV or Excel data and inspecting it using the Data Explorer node. Missing values, duplicate records, irrelevant columns, outliers and inconsistent category names are identified and corrected.

⚙️ Phase 2: Transformation and Calculation Date and numerical fields are converted into the correct formats, while invalid transactions, such as negative quantities or prices, are isolated for investigation. Important financial measures are then calculated, including:

• Revenue • Profit • Profit margin • Quantity sold • Average order value

📈 Phase 3: Analysis and Insight Generation Using GroupBy, Pivoting and sorting nodes, the workflow analyses performance across:

• Products • Categories • Regions • Customers • Months and quarters

This makes it possible to identify top-performing products, weak-performing areas, seasonal demand patterns and sales with high revenue but low profitability.

📉 Phase 4: Visualisation and Reporting The final stage converts analytical results into clear visualisations, including category comparisons, monthly revenue trends and pricing-outlier analysis. The cleaned dataset and performance reports are then exported to Excel or CSV for management review.

What makes KNIME valuable is its visual and reusable workflow structure. Every stage—from data cleaning to insight generation—is transparent, repeatable and easy to update when new sales data becomes available.

This project strengthened my understanding of data preparation, business intelligence, financial KPI analysis, visualisation and data-driven decision-making.

Learn how to complete an end-to-end Sales Data Analysis Project in KNIME Analytics Platform. In this practical tutorial, you will transform raw sales transactions into clean, meaningful business insights using a visual, low-code workflow.

The video covers the complete process, starting with importing CSV or Excel data. You will learn how to remove unnecessary columns, identify duplicate records, handle missing values, standardise category and region names, convert dates and numeric fields, and validate sales records.

Next, we calculate essential sales measures such as total revenue, profit, profit margin, quantity sold and average order value. You will also learn how to analyse sales by product, category, region, customer and month using KNIME nodes.

The workflow includes: • CSV Reader and Excel Reader • Data Explorer and Column Filter • Duplicate Row Filter and Missing Value • String Manipulation and Rule Engine • String to Date&Time and String to Number • Math Formula, GroupBy, Pivoting and Sorter • Bar Chart, Line Plot and Excel Writer

You will see how to create visualisations for top-performing products, regional profitability, monthly sales trends and category contribution. These outputs can help businesses recognise sales patterns, identify weak areas, review discount strategies, improve inventory planning and make better data-driven decisions.

This project is ideal for beginners, students, business analysts and anyone learning KNIME for data cleaning, business intelligence or sales analytics. No advanced coding knowledge is required.

Build the workflow step by step and apply the same process to your own sales dataset. Begin your KNIME journey.

🚀 Completed an End-to-End Sales Data Analysis Project Using KNIME Blog: https://assignmentonclick.com/sales-data-analysis-project-in-knime I recently worked on a practical sales data analysis project in KNIME Analytics Platform, covering the complete journey from raw data to meaningful business insights. Podcast: https://open.spotify.com/episode/4VocUwqLwROW1TG9GjAjNr?si=SPi_k98gQnWKjM7ShlD4gw The workflow included:

✅ Importing sales data from CSV or Excel ✅ Inspecting data quality and structure ✅ Removing duplicate and irrelevant records ✅ Handling missing values ✅ Standardising categories and text fields ✅ Converting dates and numeric columns ✅ Calculating revenue, profit and profit margin ✅ Analysing performance by product, category, region and customer ✅ Identifying monthly sales trends ✅ Creating charts and visualisations ✅ Exporting cleaned data and analytical results

Some of the key KNIME nodes used in the project were:

🔹 CSV Reader / Excel Reader 🔹 Data Explorer 🔹 Column Filter 🔹 Duplicate Row Filter 🔹 Missing Value 🔹 String Manipulation 🔹 String to Date&Time 🔹 Math Formula 🔹 Rule Engine 🔹 GroupBy 🔹 Pivoting 🔹 Sorter 🔹 Bar Chart and Line Plot 🔹 Excel Writer

This project demonstrated how raw sales transactions can be transformed into valuable insights for better decision-making.

The analysis can help businesses identify:

📌 Top-performing products 📌 Most profitable categories 📌 Strong and weak sales regions 📌 High-value customers 📌 Seasonal sales patterns 📌 Products with high revenue but low profit 📌 Areas requiring pricing, discount or marketing improvements

One of the biggest advantages of KNIME is its visual, low-code workflow. Every data-cleaning, transformation and analysis step is clearly connected, making the process transparent, repeatable and easy to update.

Nobody celebrates clean data. But everyone benefits from it.

Better decisions. Faster systems. Stronger outcomes.

That’s the quiet power PiLog brings behind the scenes.