What is Data Wrangling?

Data wrangling is the process of converting and organizing raw enterprise data into a usable format. It is essential for enhancing the quality and suitability of the data so that it can be consumed for analytics. The process encompasses a variety of crucial tasks that involve addressing missing or inconsistent data and filling gaps or rectifying errors to maintain data integrity. Merging different datasets is another key part of wrangling, where data is combined from various sources to create a comprehensive dataset for exploration and modeling in analytics or machine learning projects.

A practical data wrangling example is where a dataset that was initially organized by categorical values is turned into a more analysis-friendly format based on numerical data. Data wrangling helps transition qualitative columns tagged “high”, “medium” and “low” into their equivalent numerical values like 3, 2 and 1, respectively. Through this transformation, aggregation and advanced analysis becomes possible. It makes sure that the dataset remains consistent and functional for machine learning algorithms, thereby extracting deeper insights.

How Data Wrangling Works

Data wrangling is a comprehensive process that involves major steps that will empower users or analysts to transform unstructured data into a format that is analytics ready. Here is a detailed breakdown of the six steps of how it works:

• Exploration: The first step of data wrangling is to dive deeper into raw datasets to gain an understanding of its structure and composition. Data wrangling entails appraising the data to identify patterns or potential issues that may include missing or incomplete data points. This forms the foundation for every subsequent step because the principal aim is to get acquainted with the data in a way that it becomes easier to decide on any necessary transformations.
• Cleaning: Cleaning the data is necessary to rectify any errors that may have originated from manual entries, automated sensor data or faulty equipment. This step addresses these issues by correcting inaccuracies, removing duplicate entries and handling outliers appropriately. Additionally, for any data missing in the database, the incomplete records are eliminated entirely or null values are included using statistical or conditional methods.
• Transformation: Transformation involves converting data types, normalizing values or restructuring the data to maintain consistency across the dataset. It enhances the dataset’s analytical potential by creating new variables and applying relevant mathematical functions. Data becomes well-organized and tailored to meet specific analytics needs of the business user, reducing the chances ofany further complications.
• Enrichment: Data enrichment enhances the quality of dataset by incorporating additional information from varied sources. Authoritative third-party data can be integrated, such as census or demographic information, to add depth and context to the primary dataset. The process is helpful in revealing new insights and patterns that might be overlooked if only the original information is relied on. It can also inspire strategies to capture and store more comprehensive data in future projects. By thoroughly considering the extra data that could contribute to reports, models, business processes, etc., organizations can improve the quality of their analysis.
• Validation: Ensuring the integrity and reliability of data requires validation of datasets. It includes applying specific rules, such as checking data formats and cross-referencing with trusted sources. Rigorously enforcing these validation rules helps identify and correct any inconsistencies so that there are minimum errors and discrepancies in further analysis.
• Storage: The last step of data wrangling is storing the final, refined dataset and this includes saving the cleaned and validated data and documenting all the transformations it underwent. Having appropriate storage allows easy access to users for future analysis and audits. It enables replication of transformations and facilitates collaboration. An organized system for storage ensures that data remains a valuable asset for any future projects.

Benefits of Data Wrangling

• Improved Insights: Data wrangling is responsible for transforming data in order to make it more consistent and accurate. It allows advanced analytical tools to process information more efficiently. Additionally, due to this integration of data, automated systems are able to operate without errors. For instance, when building predictive models for market performance, data wrangling ensures that the underlying data is clean and uniform to create a robust and reliable model that yields actionable insights.
• Cost Efficiency: With minimal errors in the data, organizations can prevent associated costly reworks. Data wrangling can thoroughly clean and organize data before it is integrated. This ensures that the analytical tools function smoothly and eliminates the need for constant adjustments and troubleshooting. In addition, this efficiency allows data scientists, analysts and developers to focus on deriving actionable insights rather than managing data quality. Consequently, it allows businesses to allocate their resources for better use which results in substantial cost and time savings.
• Scalability: Implementing automated and robust data wrangling processes will allow businesses to maintain data quality and integrity even as the dataset becomes large and complex. This scalability ensures that the performance of the analytics platforms does not decline and prevents errors. Therefore, data wrangling empowers enterprises to harness the full potential of their growing data assets without compromising on quality.

Data Wrangling Vs. Data Cleaning

While data wrangling and data cleaning can be used interchangeably, they have different purposes within the domain of data preprocessing. Data cleaning targets the identification and correction of errors and inconsistencies with a dataset. To ensure the data is completely accurate and reliable, it performs activities like removing duplicates, handling missing values and correcting erroneous data entries.

Data wrangling, on the other hand, includes data cleaning as part of its process but extends beyond that. It consists of a broader set of tasks aimed at converting raw data into the final dataset that is more structured and suitable for analysis. The entire process involves integrating datasets from various sources, transforming them and potentially enriching data with additional context or external information.