What's your data analysis workflow with data visualization?

1 Define your question and goal

Before you dive into the data, you need to have a clear idea of what you want to achieve and what you want to learn from the data. This will help you narrow down your scope, choose the right data sources, and design your analysis plan. A good question and goal should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, you might want to know how your website traffic and conversions changed in the last quarter compared to the previous one, and what factors influenced them.

2 Collect and prepare your data

Once you have your question and goal, you need to find and access the data that can help you answer them. Depending on your data sources, you might need to use different tools and methods to collect, store, and organize your data. For example, you might use web analytics tools, databases, spreadsheets, or APIs to get your data. You also need to prepare your data for analysis by cleaning, filtering, transforming, and integrating it. For example, you might remove outliers, missing values, duplicates, or irrelevant columns, or join data from different sources.

3 Explore and analyze your data

Now that you have your data ready, you can start exploring and analyzing it to find patterns, trends, relationships, or anomalies. You can use various techniques and tools to perform descriptive, inferential, or predictive analysis, depending on your question and goal. For example, you might use summary statistics, correlation, regression, or clustering to understand your data. You can also use data visualization to help you explore and analyze your data by creating charts, graphs, maps, or dashboards that show different aspects of your data. For example, you might use line charts to show trends over time, bar charts to compare categories, or scatter plots to show relationships.

4 Interpret and communicate your results

The final step of your data analysis workflow is to interpret and communicate your results to your audience. You need to explain what your data means, what insights you gained, what limitations you faced, and what recommendations you have. You also need to use data visualization to help you communicate your results in a clear, engaging, and persuasive way. You can use various types of data visualization to tell a story, highlight key points, or support your arguments. For example, you might use pie charts to show proportions, heat maps to show intensity, or infographics to show facts.

5 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?