Explain Data Wrangling with a neat diagram.

7 b] Explain Data Wrangling with a neat diagram.

Data wrangling is the process of transforming raw data into a suitable representation for various tasks. It is the discipline of augmenting, cleaning, filtering, standardizing, and enriching data in a way that allows it to be used in a downstream task, which in our case is data visualization.

Look at the following data wrangling process flow diagram to understand how accurate
and actionable data can be obtained for business analysts to work on. The following
steps explain the flow of the data wrangling process:

1. First, the Employee Engagement data is in its raw form.
2. Then, the data gets imported as a DataFrame and is later cleaned.
3. The cleaned data is then transformed into graphs, from which findings can
   be derived.
4. Finally, we analyze this data to communicate the final results.

For example, employee engagement can be measured based on raw data gathered from feedback surveys, employee tenure, exit interviews, one-on-one meetings, and so on. This data is cleaned and made into graphs based on parameters such as referrals, faith in leadership, and scope of promotions. The percentages, that is, information derived from the graphs, help us reach our result, which is to determine the measure of employee engagement:

In the context of data visualization, the significance of data wrangling lies in its ability to:

1. Ensure Data Quality: Raw data often contains inconsistencies, missing values, or errors. Data wrangling cleans the data, ensuring accuracy and reliability, which is essential for producing meaningful visualizations.
2. Prepare Data for Visualization: Before creating visualizations, data needs to be structured in a way that aligns with the chosen visualization technique (e.g., converting categorical data to numerical, normalizing values). Wrangling helps prepare the data for these visualizations.
3. Enhance Clarity and Insight: Well-wrangled data reduces noise and emphasizes key patterns, trends, and insights, enabling clearer and more effective visualizations.
4. Enable Complex Visualizations: Some visualizations, such as multi-dimensional plots or interactive dashboards, require the integration of multiple data sources or the transformation of data formats. Data wrangling is crucial in combining and transforming data to support these complex visual representations.