Lab 3: Data Visualization

Create visualizations that tell the story of your clean energy analysis.

In this lab, you'll build visualizations that communicate your findings to stakeholders. The goal is to create a dashboard that shows optimal times to run compute-intensive operations based on carbon intensity forecasts.

Prerequisites

• Completed Lab 1: Ingestion & Transformation
• Completed Lab 2: Orchestration & Automation

The Story You're Telling

Data is only as valuable as the the impact it can have on the business. Ultimately, all data is meant to be used to make decisions that impact the business. But to drive meaningful impact data must be presented in a way that is actionable and easy to understand.

If a picture is worth a thousand words, then well designed visualizations may be worth millions of dollars in business impact.

Today, we will create visualizations that help the business make decisions about when to run compute-intensive operations based on carbon intensity forecasts. We'll also create a clear, compelling executive summary of the findings to share with stakeholders -- because the data team definitely deserves credit for identifying ways to save the company millions of dollars in energy costs!

Step 1: Understanding your data pipeline

Before building visualizations, it's important to understand the data behind them. Understanding your pipeline structure helps you create more accurate and meaningful visualizations.

Open Otto (Ctrl+I) and paste the following prompt:

Hi Otto! Can you create a mermaid and ER diagram for the pipeline.

This will help you visualize the relationships between your data sources, transformations, and output tables, making it easier to identify which data points to visualize.

Step 2: Creating visualizations

Start by creating a simple visualization to understand how Otto works when generating visualizations.

Open Otto (Ctrl+I) and paste the following prompt:

Hi Otto! Can you create an interactive line chart showing the forecasted carbon intensity for the next 7 days, with highlighted regions indicating recommended operation windows? Please use real data only, not mock data.

How Otto creates visualizations

Let's examine how this prompt is executed using the Context, Tools, and Triggers framework:

1. Trigger: You manually trigger Otto with the prompt.
2. Context gathering: Otto gathers context using available tools:
   • Otto typically starts by listing files in the flow to understand the file structure.
   • Then it reads the code associated with tables to identify available columns and understand the data transformation logic.
   • Finally, it runs queries on the tables to retrieve the data needed for the visualization.
3. Tool execution: Otto uses available tools to create the visualization:
   • In this case, Otto uses the create_visualization tool.
   • The visualization is saved in the otto/visualizations folder.
4. Result: Otto returns the completed visualization.

Now that you've created a single visualization, create a dashboard with your findings from this flow.

Please create a dashboard with our findings from this flow. Your visualizations should answer these questions for stakeholders:

1. When should we run operations next week? Forecast based on historical patterns and produce a timeline of recommended operation windows for the next 7 days.
2. When is energy cleanest? Show patterns by weather category, hour, day, and month
3. How much can we save? Quantify the impact of optimal scheduling

Please use real data only, not mock data. Only run 1 query at a time.

Step 3: Create an executive summary

Create an executive summary of your findings to share with stakeholders.

Open Otto (Ctrl+I) and paste the following prompt:

Please create an artifact with a complete report of our findings for an executive summary. The summary should be a well-written, compelling, and actionable summary of the findings that can be shared with stakeholders. Include any of the visualizations you created in the previous step as well as the data that was used to create them.

Step 4: Refine your visualizations

Work with Otto to refine your visualizations:

• Adjust colors and styling
• Add clear labels and titles
• Include data sources and date ranges
• Ensure accessibility (color contrast, alt text)

tip

If you created the visualization style guide rule in Hands-On #2, Otto will follow your style standards! You can update the rule to impact all future visualizations you create.

Checkpoint

By the end of this lab, you should have:

• Created a dashboard with visualizations that answer the questions for stakeholders
• Created an executive summary of the findings to share with stakeholders
• Refined the visualizations to follow a style guide

Need help?

Ask a bootcamp instructor or reach out in the Ascend Community Slack.

Next steps

Congratulations on completing the labs! Head to Wrapping Up to submit your work and claim your reward!