Understanding Error Trapping: A Quick Guide

Have you ever imported a date series file into Power BI and then faced an error when trying to manipulate the data?

In this scenario, I will show you how I manage error trapping using different platforms.

Tools Used for this Project: MS Excel and Power BI

The dataset can be found and downloaded for your reference on Kaggle.

Importing Data

Upon importing data, we can see that it has 100% Validity based on it’s column quality

We have two columns: DueDate and NextDueDate.
In this part, I'm trying to format the column into a date format. However, when I did this, I encountered an error.

To examine the data, I load it into MS Excel. One of the best practices to ensure the data has been imported correctly is to do some experimentation. This is especially important for date series, but it applies to any data type.

The DueDate column is currently in Date format. To check if the column was imported correctly, change the format to General. If the date changes from a date to a number, we can conclude that it was imported correctly.

We can see that it did not change into a number. Therefore, we can assume that the entire dataset was not imported correctly.

Fortunately, MS Excel offers a way to fix this issue. We can use the Text to Columns tool.

We will apply the same process to the NextDueDate column. Once completed, we can see that the date has shifted its alignment to the right, and the formatting has been updated. This indicates that we have formatted the data type correctly.

Performing data formatting in Power Query would also fix this issue since the source file has been updated, as shown below:

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Written by

Jogleen Calipon

👋 Welcome to My Profile! I'm a Data Analyst with over four years of experience turning data into meaningful insights that drive smart business decisions. Whether it's building automated reports, uncovering operational inefficiencies, or creating interactive dashboards that tell a clear story—I'm passionate about using data to solve real-world problems. 💡 What I Do Best Data Preparation: Cleaning, shaping, and enriching messy datasets Data Analysis: Extracting insights to inform decisions Automation: Streamlining recurring reports and building data entry forms Business Reporting: Creating reports tailored to decision-makers Visualization: Designing dashboards that make data easy to understand Collaboration: Translating technical findings for non-technical audiences 🛠️ Tools & Technologies Spreadsheets & Data Processing Microsoft Excel: Power Query, Power Pivot, DAX, advanced lookup functions, custom automation workflows Business Intelligence Power BI: Interactive dashboards and visual storytelling Databases & SQL Foundational knowledge of MS SQL Server, MySQL, BigQuery, and MS Access Experience writing basic to intermediate SQL queries Programming Python: Foundational experience with Pandas, NumPy, SciPy, Seaborn, and Matplotlib for data analysis and visualization R: Working knowledge of data wrangling, ggplot2, and statistical modeling 🚀 Let's Connect I'm currently open to short-term projects and part-time roles where I can contribute to: Optimizing processes Unlocking insights hidden in data Building scalable, automated solutions Thanks for visiting my profile! Feel free to explore my projects and reach out for collaboration or just to connect. 😊