What techniques can you use to ensure data accuracy when cleaning different types of data and visualizations?

1 Numerical data

Numerical data is data that can be measured or counted, such as quantities, prices, or percentages. However, numerical data can be affected by missing values, outliers, incorrect units, or human errors. To clean numerical data, you can impute missing values with the mean, median, mode, or a custom value depending on the distribution and context of the data. Additionally, you can identify and handle outliers using methods such as box plots, z-scores, or interquartile ranges. Outliers can be removed, replaced, or kept depending on their impact and relevance to the analysis. Furthermore, checking and converting units to ensure consistency is important to avoid confusion. For example, if you are working with currency data, make sure they are all in the same currency and exchange rate. Lastly, validating and correcting human errors using data validation rules, formulas, or functions is necessary. For instance, if you are working with date data, make sure they are in a valid format and range.

2 Categorical data

Categorical data, such as names, colors, or labels, is data that can be grouped into categories. Issues like spelling mistakes, inconsistent formatting, or redundant categories can affect categorical data. To clean it up, you can standardize spelling and formatting with methods such as lowercasing, trimming, or replacing characters. You can also merge or split categories by grouping, mapping, or concatenating. And empty or invalid values can be removed or replaced with filtering, dropping, or filling. For example, you may want to make sure country names are spelled and capitalized consistently or combine similar product categories. You may also want to remove blank survey responses or replace them with a default value.

3 Textual data

Textual data, such as reviews, comments, or descriptions, consists of words, sentences, or paragraphs and can be affected by noise, ambiguity, or irrelevance. To clean it up, you can strip punctuation, hashtags, or mentions from tweets to remove noise. You can also parse, tag, or disambiguate news articles to resolve ambiguity. And for customer reviews, you may want to query, summarize, or cluster the text to filter out relevance. These techniques will help you ensure that your textual data is accurate and useful.

4 Visual data

Visual data, such as photos, logos, or diagrams, can be affected by low quality, distortion, or duplication. To clean this data, you can enhance quality by resizing, cropping, or sharpening the images. You can correct distortion by rotating, flipping, or transforming the images. Additionally, you can detect duplication using methods such as hashing, comparing, or deduplicating. Data cleaning is essential for any data visualization project to improve the quality and clarity of your data and visualizations. Applying the right techniques to different types of data and visualizations will ensure accuracy and enhance your analysis and presentation.

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?