What are the most common mistakes when working with data?

1 Wrong data sources

One of the first steps in any data project is to select the data sources that are relevant, reliable, and accurate for your purpose. However, many marketers either use too many or too few data sources, or choose the wrong ones. Using too many data sources can lead to duplication, inconsistency, and complexity. Using too few data sources can result in missing important information, bias, and oversimplification. Choosing the wrong data sources can result in irrelevant, outdated, or inaccurate data. To avoid these mistakes, you should carefully evaluate the quality, validity, and relevance of your data sources, and use only the ones that are necessary and appropriate for your analysis.

2 Poor data cleaning

Data cleaning is the process of identifying and correcting errors, inconsistencies, and outliers in your data. It is a crucial step to ensure the accuracy and reliability of your analysis and results. However, many marketers either skip or rush this step, or do it incorrectly. Skipping or rushing data cleaning can result in misleading or inaccurate results, as well as wasted time and resources. Doing it incorrectly can result in losing or altering valuable information, or introducing new errors or biases. To avoid these mistakes, you should follow a systematic and rigorous data cleaning process, using appropriate tools and methods, and documenting your actions and decisions.

4 Bad data visualization

Data visualization is the process of presenting your data and results in graphical or visual forms, such as charts, graphs, tables, or maps. It is an effective way to communicate your data and results to your audience, as it can make them more understandable, engaging, and memorable. However, many marketers either use the wrong or too many data visualization types, or do not use any data visualization types at all. Using the wrong or too many data visualization types can result in confusing or misleading your audience, as well as distracting or overwhelming them. Not using any data visualization types can result in boring or unclear your audience, as well as losing their attention or interest. To avoid these mistakes, you should use the data visualization types that are appropriate and sufficient for your data type, level, and purpose, and use them effectively and aesthetically.

5 Poor data communication

Data communication is the process of conveying your data and results to your audience, using verbal or written forms, such as reports, presentations, or stories. It is the final and most important step of your data project, as it allows you to share your insights and recommendations with your audience, and persuade them to take action or change their behavior. However, many marketers either use the wrong or too much data communication styles, or do not use any data communication styles at all. Using the wrong or too much data communication styles can result in confusing or alienating your audience, as well as losing their trust or respect. Not using any data communication styles can result in ignoring or disappointing your audience, as well as wasting your efforts or potential. To avoid these mistakes, you should use the data communication styles that are suitable and effective for your audience, context, and goal, and use them clearly and concisely.

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?