How do you tell a story with clean and organized data?

1 Why clean and organize data?

Data cleaning and preparation are the processes of transforming raw data into a consistent, accurate, and ready-to-analyze format. This involves identifying and fixing errors, missing values, outliers, duplicates, and inconsistencies in the data, as well as reshaping, merging, filtering, aggregating, and sorting the data according to your analysis goals and questions. Cleaning and organizing data can be beneficial in a variety of ways; it can improve the quality and reliability of your data, reduce the risk of errors and biases in your analysis and interpretation, simplify and speed up your data analysis and visualization, highlight the most relevant aspects of your data, and enhance the clarity and impact of your data story.

2 How to clean and organize data?

Data cleaning and preparation is not a one-size-fits-all process; different data sources, types, and formats may require different methods and tools. However, there are general steps and guidelines that can assist you. Firstly, define your data story objective and scope to determine the main question or message you want to convey with your data, as well as the data needed to answer or support it. Secondly, explore and understand your data by looking at its characteristics, dimensions, variables, distributions, and relationships. Thirdly, clean and validate your data by detecting and correcting errors, missing values, outliers, duplicates, and inconsistencies. Fourthly, organize and transform your data by reshaping, merging, filtering, aggregating, sorting it to make it easier to analyze and visualize. Finally, document and save your data so that you can keep track of the changes made during the data cleaning process.

3 What tools can you use for data cleaning and preparation?

Data cleaning and preparation tools vary depending on the size, complexity, and format of your data. Spreadsheets, such as Excel or Google Sheets, are easy to use and familiar to most people; they can help with basic tasks such as filtering, sorting, formatting, and calculating. However, they may not be suitable for large or complex data sets due to their slow speed and susceptibility to errors. Programming languages like Python or R are powerful and flexible tools for data cleaning and preparation; they can help automate, document, and reproduce processes while also creating custom functions. Data cleaning and preparation software such as Trifacta or Alteryx provide a fast, easy, and intuitive way to prepare data using graphical user interfaces; however, they may be expensive or limited in terms of compatibility.

4 How to check and evaluate your data cleaning and preparation results?

After you have cleaned and organized your data, it is important to check and evaluate your results to ensure the data is ready for analysis and visualization. To do this, review and summarize your data to get an idea of its main features, statistics, and patterns. Additionally, compare and contrast your data to the original or raw data, as well as other data sources or sets. Lastly, test and validate your data to verify that it is accurate, complete, and consistent. This will help you identify and resolve any remaining errors, missing values, outliers, duplicates, or inconsistencies in your data. Ultimately, this will ensure that your data meets the standards and requirements of your analysis and visualization tools and techniques.

5 How to tell a story with clean and organized data?

Once you have cleaned and organized your data, you are ready to tell a story with it. Data storytelling is an art and science of relaying insights and recommendations from data in a clear, engaging, and persuasive manner. It helps to connect with the audience and capture their attention, as well as simplify complex or technical data in an understandable way. Furthermore, it emphasizes key findings and messages from your data while inspiring action and decision making. To tell a story with clean and organized data, you need to combine three elements: data, visuals, and narrative. Data is the foundation of your story providing evidence and facts, visuals are the tools to present your data using charts or graphs, and narrative is the way to structure and deliver your story using words, voice, tone, and style. To create a compelling data story, you need to balance these three elements while considering your audience, purpose, and context.

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?