What Is Data Wrangling?
Data Wrangling Defined
Data Wrangling Steps
- Discovering data: In the discovery phase, data wranglers work to learn what their data is about and how it can be further explored and analyzed. For example, does it include shoppers’ browsing and purchasing history, or a detailed history of social media users’ past likes?
- Structuring data: Data comes in all shapes and sizes. Data structuring (or restructuring) is the process of merging numerous datasets into a singular unified format.
- Cleansing data: Datasets are often incomplete. Consequently, data is manually cleansed before it is analyzed. This may involve deleting and replacing inaccurate or corrupt records.
- Enriching raw data: Data enrichment involves enhancing existing data with relevant data from other sources. For example, online shopping platforms can link shoppers’ purchase history with their IP addresses to make more targeted, specific product recommendations.
- Validating data: Data validation ensures the accuracy of data before it’s analyzed. For example, if a company wants to analyze past fourth-quarter inventory levels against prior fourth-quarter purchase orders, data validation will verify that information from other quarters isn’t included in the dataset.
- Publishing data: This is the final step in data wrangling. Data publishing is when a cleansed, organized dataset is sent for analysis.
Work Settings for Data Wranglers and Data Analysts
Data Analytics Job Growth Projections
Skills Data Wrangling Specialists Need to Succeed
- Analytical skills: Data wranglers must review and evaluate various datasets to identify relevant information. Strong analytical skills are required to evaluate large, unstructured data sources.
- Programming skills: Data wranglers need to have a strong background in computer programming languages such as SQL and Python. These languages help analysts clean datasets using automated processes.
- Logical thinking skills: Unstructured data comes in various sizes and forms. Data wranglers with refined logical thinking skills understand how to combine and restructure multiple datasets into an analysis-friendly format.
- Problem-solving skills: If data analytics is like solving a puzzle, data wrangling is the process of restructuring wood fibers, paint, and glue to create puzzle pieces. Problem-solving skills help data wranglers decide which datasets are relevant and determine the transformations to perform before the data is published.