3 Ambitious Excel Charts to Boost Analytics and AI/ML Visualizations
Introduction
As artificial intelligence (AI) and machine learning (ML) become increasingly integral to business, the ability to effectively visualize and communicate data insights is more critical than ever. While many think of advanced programming languages and specialized tools for AI/ML, Excel remains a powerful asset in any data professional‘s toolkit.
Cutting-edge AI/ML projects still rely on many of the same fundamental data visualization principles as traditional analytics. Excel‘s charting capabilities can help bring those analyses to life in a way that‘s quick to create and easily digestible for stakeholders.
In this post, we‘ll dive into three advanced Excel charts that are particularly valuable for leveling up your AI/ML visualizations:
- Waterfall Charts
- Funnel Charts
- Pareto Charts
For each chart type, I‘ll provide an in-depth overview, step-by-step Excel instructions, and multiple use case examples relevant to AI/ML projects. I‘ll also share some expert tips on how to maximize the impact of these charts in your AI/ML initiatives.
As an AI/ML practitioner, being able to create compelling data visualizations directly in Excel will save you time and help you better engage business stakeholders. Let‘s level up your Excel charting game!
Waterfall Charts: Visualizing AI/ML Project Financials and Performance
Waterfall charts are excellent for showing the cumulative effect of sequentially introduced positive or negative values. In the context of AI/ML projects, they‘re commonly used for:
- Cost-benefit analysis of model development and deployment
- Visualizing model performance metrics over time
- Breaking down the components of ROI for AI/ML initiatives
Example: AI/ML Project Cost-Benefit Analysis
Consider an AI/ML project to build a predictive maintenance model for manufacturing equipment. A waterfall chart could clearly show the financial breakdown:
| Category | Value |
|---|---|
| Algorithm Development | ($50,000) |
| Data Collection | ($30,000) |
| Model Training/Testing | ($20,000) |
| Deployment | ($15,000) |
| Increased Uptime | $200,000 |
| Reduced Repair Costs | $100,000 |
| Productivity Gains | $50,000 |
| Net Benefit | $235,000 |
Creating a waterfall chart in Excel to visualize this:
- Organize the data into "base", "loss", and "gain" columns
- Insert a stacked column chart
- Reverse the category axis order
- Set "base" series to "no fill" and adjust colors
- Add data labels, clean up formatting, and add a total
The resulting waterfall chart provides a compelling visual story for the costs, benefits, and overall net impact of the AI/ML project. This enables data scientists to better communicate the value of their work to business stakeholders.
More Waterfall Chart Use Cases for AI/ML
- Comparing performance metrics (accuracy, precision, recall, etc.) of different model versions
- Showing the cumulative impact of hyperparameter tuning or feature engineering on model outcomes
- Visualizing the cost savings or profit gains from an AI/ML model over time compared to the baseline
Funnel Charts: Visualizing AI/ML Model Development Pipelines
Funnel charts are ideal for showing retention or drop-off rates through a multi-stage process. For AI/ML teams, funnel charts are a great way to visualize the model development pipeline from data preparation through to deployment.
Example: AI/ML Model Development Pipeline
Let‘s say an AI/ML team is building a customer churn prediction model. They could use a funnel chart to show the number of observations remaining at each stage:
| Pipeline Stage | Number of Observations |
|---|---|
| Initial Dataset | 500,000 |
| Data Cleaning/Validation | 450,000 |
| Feature Engineering | 375,000 |
| Model Training (70%) | 262,500 |
| Model Testing/Validation (30%) | 112,500 |
| Deployed to Production | 100,000 |
To build this funnel chart in Excel:
- Set up the data with "Pipeline Stage" and "Number of Observations" columns
- Calculate "percent of previous stage" and "percent of initial" for each row
- Add "extra space" columns on either side for the funnel effect
- Insert a stacked bar chart with stages on the Y-axis and reverse the order
- Make "extra space" series "no fill", adjust colors, and add data labels
The funnel chart makes it easy to see the retention rates between each stage of the model pipeline. AI/ML leaders can quickly pinpoint the stages with the largest drop-off and click into the details to identify opportunities for optimization.
More Funnel Chart Use Cases for AI/ML
- Visualizing the efficiency of different data preparation techniques by comparing the number of observations retained
- Showing model accuracy rates across different customer segments to identify areas for improvement
- Comparing conversion rates of champion vs. challenger models in A/B tests
- Illustrating the "funnel" of data from edge devices through processing and into model inference
Pareto Charts: Identifying Key Drivers of AI/ML Model Outcomes
Pareto charts combine a bar chart and line graph to highlight the factors that are most important to a particular outcome. They‘re based on the Pareto Principle (also known as the 80/20 rule) which states that 80% of outcomes come from 20% of causes.
For AI/ML models, Pareto charts are a valuable way to identify the key drivers of model performance and focus improvement efforts.
Example: Key Features Driving Customer Churn Model
Going back to the customer churn model example – once the model is built, the data scientists could create a Pareto chart to determine which features have the biggest impact on the predictions:
| Feature | Importance | Cumulative % |
|---|---|---|
| Days Since Last Purchase | 0.30 | 30% |
| Number of Returns | 0.25 | 55% |
| Total Lifetime Value | 0.20 | 75% |
| Number of Customer Service Calls | 0.10 | 85% |
| Account Tenure | 0.08 | 93% |
| Geographic Region | 0.07 | 100% |
Creating the Pareto chart in Excel is straightforward:
- Order the data descending by importance
- Add a cumulative percentage column
- Insert a clustered column chart of feature importance
- Add a line chart of cumulative percentage on the secondary axis
- Clean up the formatting and add data labels
The Pareto chart makes it immediately clear that the first two features account for over half of the model‘s predictive power. The team can use this insight to collect more data on those features, try different engineering techniques, or implement business processes to impact those drivers.
More Pareto Chart Use Cases for AI/ML
- Analyzing the distribution of errors across classes in a classification model
- Identifying the most common root causes of false positives/negatives
- Determining the most significant hyperparameters to tune for model optimization
Best Practices for Using Excel Charts in AI/ML Projects
Now that we‘ve walked through three powerful Excel charts and multiple AI/ML use cases for each, I want to share some tips for maximizing their impact:
-
Context is key
No chart can tell the full story on its own. Be sure to provide enough context about the data, methodology, and key takeaways in the surrounding text or presentation. -
Pair with more advanced visualizations
These Excel charts are a great starting point, but AI/ML projects often benefit from more specialized visualizations like confusion matrices, ROC curves, partial dependence plots, etc. Use Excel charts to provide a high-level visual while diving deeper with advanced plots. -
Interactivity drives insights
Allow users to interact with your charts by adding filters, dropdowns, or slicers in Excel. This lets them explore different segments or scenarios and uncover additional insights. -
Static to tell a story, animated to show changes
Static charts are best for providing a snapshot view or telling a specific story. But if you want to show how model performance or pipeline metrics have changed over time, consider using Excel‘s animation tools to bring your charts to life.
Conclusion
Excel charts are a powerful way for AI/ML practitioners to visualize and communicate data insights to technical and non-technical audiences alike. Waterfall charts, funnel charts, and Pareto charts are three essential tools to have in your arsenal.
Waterfall charts are excellent for cost-benefit analyses, visualizing model performance over time, and showing overall financial impact. Funnel charts are best for illustrating the retention rates through each stage of the model development pipeline. And Pareto charts are a quick way to identify the key drivers or inputs of AI/ML model outcomes.
By adding these Excel charts to their toolkit, data scientists and ML engineers can spend less time trying to explain their work and more time actually doing it. And they‘ll be able to drive better business decisions by making their AI/ML insights accessible and compelling to stakeholders.
I encourage you to incorporate these charts into your own AI/ML projects and see how they can level up your visual communication. Experiment with different applications, get creative with your data and formatting, and most importantly – use them to tell a story.
Excel has been a staple of data visualization for decades and it‘s not going anywhere anytime soon. As AI and ML power the next generation of advanced analytics, let‘s ensure that everyone can understand and participate in the data conversation. I believe mastering these critical Excel charts is an important part of making that vision a reality.
