What is data wrangling in Workday Prism?

What is data wrangling in Workday Prism?

• A. A method of generating real-time alerts
• B. The process of cleaning and transforming raw data for analysis
• C. A technique for visualizing data trends
• D. A tool for storing original data sets

Answer: (B)

Data wrangling in Workday Prism refers to the process of cleaning and transforming raw data for analysis. This critical step involves taking data from various sources, which may not be structured or may contain inconsistencies, and preparing it to be useful for analytics and decision-making. During data wrangling, tasks might include standardizing data formats, handling missing values, filtering out irrelevant information, and enriching datasets by combining data from different sources or applying calculations. This ensures that the data used in analysis is accurate, consistent, and in a suitable format, enabling better insights and more informed decisions. The other choices describe different functionalities that are not the focus of data wrangling. For instance, generating real-time alerts pertains to monitoring data changes and notifying users, while visualizing data trends involves presenting data in graphical formats to identify patterns. Storing original data sets typically relates to data management rather than the transformation process involved in preparing data for analysis.

Data wrangling in Workday Prism refers to the process of cleaning and transforming raw data for analysis. This critical step involves taking data from various sources, which may not be structured or may contain inconsistencies, and preparing it to be useful for analytics and decision-making.

During data wrangling, tasks might include standardizing data formats, handling missing values, filtering out irrelevant information, and enriching datasets by combining data from different sources or applying calculations. This ensures that the data used in analysis is accurate, consistent, and in a suitable format, enabling better insights and more informed decisions.

The other choices describe different functionalities that are not the focus of data wrangling. For instance, generating real-time alerts pertains to monitoring data changes and notifying users, while visualizing data trends involves presenting data in graphical formats to identify patterns. Storing original data sets typically relates to data management rather than the transformation process involved in preparing data for analysis.