How do you overcome data communication challenges at work?

1 Data complexity

One of the main challenges of data communication is dealing with the complexity and volume of data you have to analyze and present. You may have to deal with multiple sources, formats, and variables of data, which can make it hard to convey the key messages and insights. To overcome this challenge, simplify and prioritize your data, focusing on the most relevant and actionable information for your audience. Also use appropriate methods and tools to clean, transform, and organize your data, such as data wrangling, data pipelines, and data warehouses.

2 Data literacy

Data literacy refers to the ability to understand, interpret, and use data effectively. Depending on your audience, you may have to explain basic concepts and terms, or dive deeper into more advanced topics like machine learning, artificial intelligence, or natural language processing. It's important to know your audience well and tailor your language, tone, and style to their needs. Use clear and concise explanations, avoid jargon and acronyms, and provide examples to illustrate your points.

4 Data storytelling

Data stories are narratives that use data as evidence to support a message or recommendation. They can help you connect with your audience emotionally and logically, and influence their actions. However, data stories can be ineffective, irrelevant, or untrustworthy if they are not structured and delivered well. To overcome this challenge, follow some steps of data storytelling including defining your goal and audience, finding your angle, building your plot and characters, using data visualization, and ending with a call to action. Make sure to practice and refine your data storytelling skills with storytelling frameworks, styles, and tools.

5 Data ethics

Data ethics deals with the responsible and ethical use of data, especially in relation to privacy, security, ownership, and fairness. Data ethics can affect how you collect, analyze, interpret, and present data, and how your data impacts others. So it's important to be aware of the data ethics issues and implications of your data communication, such as data quality, bias, transparency, accountability, and impact. Also adhere to the data ethics standards and codes of conduct of your organization, industry, or profession.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?