637: How to Influence Others with Your Data — with Ann K. Emery (@AnnKEmery)​

Design Principles

Jon Krohn and Ann Emery discuss the fundamental principles of good data visualization design. They emphasize the importance of decluttering graphs, using dark colors that are legible to all, and including takeaway messages in titles 1. Ann highlights the value of adding annotations to charts to clarify significant changes, making the data more accessible to the audience 2.

It's so painfully obvious to the person making the graph why some sharp increase or decrease might have happened, but it's not painfully obvious to the audience who sees that graph among 50 million other graphs every single week.

--- Ann Emery

These principles help ensure clarity, simplicity, and accessibility in data presentations.

   

Tools & Techniques

Exploring various tools and techniques, Ann introduces the beta version of Excel's image function, which simplifies the process of adding images from URLs into spreadsheets 3. She also mentions the importance of practical spreadsheet skills, highlighting the work of Google Sheets expert Ben Collins 4.

You have to download, oh, what's the official name? It's a beta version of Excel, like a beta community tester. It takes less than 60 seconds to download it.

--- Ann Emery

These tools and techniques can significantly enhance the efficiency and effectiveness of data visualization tasks.

   

Challenges & Solutions

Ann and Jon address common challenges in data visualization, such as ensuring data accuracy and managing large volumes of data 5 6. Ann stresses the difficulty of maintaining data accuracy while also presenting the bigger picture, especially in high-stakes fields like public health 5.

There are real world implications if I make a mistake, like, I'm working on, just, like, child mortality and maternal, like, very heavy, heavy topics, Covid data, like, things that have huge real world implications.

--- Ann Emery

They suggest solutions such as taking breaks to review data with fresh eyes and leveraging electronic data collection methods to handle large datasets more efficiently.