What techniques can you use to identify data errors during data cleaning for data storytelling?

In real world, there is no any full clean data and we face with errors in data undoubtedly. So we have to be familiar with data cleaning tools and techniques and be armed with new techniques repeatedly in order to identify and react the errors in data perfectly.

- Handle Missing Data: Decide on imputation or removal based on impact. Clearly show missing values. - Outlier Detection: Identify and treat outliers, considering removal or transformation. - Consistent Data Types: Ensure uniform data types, especially for categorical or date/time variables. - Data Aggregation: Aggregate data appropriately and use relevant functions (sum, average). - Normalization: Normalize data for consistent scales and units. - Duplicates: Address duplicates by aggregating or removing based on context. - Handling Skewness: Transform skewed distributions for better representation.

During the data cleaning process for data storytelling, techniques like outlier detection, consistency checks, and missing value analysis are crucial. Outliers can be identified through statistical methods, ensuring data consistency involves validating relationships between variables, and analyzing missing values helps maintain data integrity. Employing these techniques enhances the reliability of the dataset, laying a solid foundation for accurate and compelling data storytelling.

You may have heard "you're only as good as your data". And any errors within your dataset will skew the results. You cannot rely on those results if they contain errors. And you certainly do not want to have given the data as a final version to your boss or senior leadership. All data must be properly cleaned and validated.

Generally, you start data cleaning by scanning your data at a broad level. You review and diagnose issues systematically and then modify individual items based on standardized procedures. Your workflow might look like this: Apply data validation techniques to prevent dirty data entry. Screen your dataset for errors or inconsistencies. Diagnose your data entries. Develop codes for mapping your data into valid values. Transform or remove your data based on standardized procedures. Not all of these steps will be relevant to every dataset. You can carefully apply data cleaning techniques where necessary, with clear documentation of your processes for transparency.

Visualizations can be highly effective in revealing outliers in your data. When I work with data, I typically use three visualizations to identify outliers: Boxplot, Scatter Plot, and Histogram. Boxplot provides a clear and accurate representation of the data distribution and can easily identify outliers. Points that appear outside the "whiskers" of the boxplot are often considered outliers. Scatter Plot is another useful visualization tool to identify outliers. Outliers can be visually identified on a scatter plot as points that deviate significantly from the overall pattern or trend of the data. Histograms are generally used to visualize the overall distribution of data. but they can also reveal the presence of outliers.

You can start with the basic summary statistics like mean, median, mode, range, standard deviation, and count. if there are unusual values and extreme outliers, you can dive further to check for data errors. It is important to apply domain knowledge while dealing with potential data errors. You can also check for consistency in data by comparing related values. For example, the start date should be before the end date, or the order date should be before the shipping date. While checking for consistency keep in mind the format and language being used. For example, the date field might follow the American or British formats. Sometimes you might have mixed formats in the dataset which has to be cleaned before use.

To understand quickly the missing values in the data set, I always visualize them. - First, I make a column chart. If the number of missing values in a column is less than or equal to 5% of the total number of rows in the dataset, then I shade the column gray. However, if the missing values exceed 5% of the total number of rows in the dataset, I dye the column red. - Second, to determine if the missing data is random or exhibits a pattern, I create a missing data matrix.

Data errors can undermine the credibility and effectiveness of data storytelling. Therefore, identifying and correcting these errors is crucial for ensuring the accuracy and reliability of data-driven narratives. Several techniques can be employed to identify data errors, including data profiling, data validation, cross-referencing, range checks, duplicate detection, data visualization, domain expertise, statistical techniques, error reporting, and documentation. By implementing these techniques, data scientists and data storytellers can enhance the quality of their data and produce compelling and trustworthy data stories.