Power BI vs Excel for Credit Data

Microsoft’s Excel has long reigned as the main data organizer in business. But since the launch of Microsoft’s Power BI in 2014, data organization has evolved. Power BI is an interactive data visualization platform software product for self-service and enterprise business intelligence (BI). With it, you can connect to and visualize any data, and infuse the visuals into any application. While both programs are efficient, credit professionals should be aware of their differences and how to use each in order to organize their data.

Excel has been a staple for many credit professionals due to its efficiency and simplicity. “If used correctly, it can save time and effort,” said Yazmin Miller , CBF, CCRA, CICP, credit supervisor at Mitsubishi Electric Automation, Inc. (Vernon Hills, IL), who uses it to prepare for her month-end report. “I have saved formulas that are linked to graphs and pie charts, making it easier for executives to understand the condition of the receivables.” She also uses Excel for KPIs such as DSO, cash collected as % of beginning A/R, delinquency and DBT.

Excel Pros

• User-friendly for individuals and corporations
• Functions and formulas for financial calculations and analysis
• You can create charts, graphs and pivot tables for data visualization
• It can handle moderate-sized data sets

Excel Cons

• Limited capacity for interactive dashboards and real-time data analysis
• Manual input and updating of data
• Difficult to maintain single source of truth principle
• Limited capability for complex data modeling and analysis
• Prone to errors if data is not properly entered or incorrect formulas are applied

One of the drawbacks for credit professionals is the space limit in Excel, “which is difficult considering that in the credit industry, the data isn’t always clean and takes up a lot of space,” said Leon Zhang , credit operations manager at SRS Distribution Inc. (McKinney, TX). He finds it easier to transfer his data from a CSV file or Excel file to Power BI. “Not to say Excel doesn’t have its positives. It’s easy to pick up and gets the basics done without having to go too deeply.”

Power BI has increased in popularity with its capacity to handle larger data sets, a must in the credit profession. “Now with the dashboard, we look at everything past due and the summary level on all accounts,” said Lee Tompkins, RGCP , director of credit and collections at MPW Industrial Services, Inc. (Hebron, OH). He finds it especially helpful with weekly AR calls. “In the past, we downloaded reports from JD Edwards into Excel and the team would have to type comments in. It was time consuming. Power BI is more of a deep-dive and can be sorted by account, sales, region, business unit and cost.”

Power BI Pros

• Advanced data modeling and transformation capabilities
• Can handle large and diverse data sets
• Natural language querying and predictive analytics
• Interactive dashboards and real-time data analysis
• Connect to multiple data sources, including internet data and cloud-based data services

Power BI combines multiple data sets and creates a visual for a user is to view and understand what they need, Zhang said. This way, users don’t have to sort through multiple reports to pull out what they need. “We’re able to create a standard for what we consider a good level to be at, whether that be for our customers or credit managers.”

Power BI Cons

• Requires advanced training
• Requires a stable and reliable internet connection
• Can be costly for smaller businesses
• Limited capability for financial analysis and calculation functions

When it comes to training employees in Power BI, Excel is much easier to learn. “Excel is more user-friendly whereas there’s multiple canvases and stats you have to learn in Power BI,” Zhang said. “If you need to look at a quick table or a quick draft, Excel is the way to go.”

When to Use Power BI Over Excel

• Large or complex data sets: Power BI can connect multiple data to form relationships. Those visuals can then be used by other users.
• Advanced data visualization
• Predictive analytics
• Real-time data analysis
• Collaborative data analysis

When to Use Excel Over Power BI

• Quick calculations
• Small data analyzation
• Simple data visualization
• Ad-hoc analyses
• For individual or small team use