Exploring Data Wrangling vs Data Cleaning Techniques

What is Data Wrangling?

Data wrangling, or data munging, is a process of data transformation and mapping from one raw pattern or format to another. Data wrangling is widely used to convert complex or uneven data into effective forms easily accessible to all. This approach enables fast processes in data accession and taking valuable insights to make data-driven decisions. The process includes cleaning, restructuring, and enriching the raw data into the desired format for further analysis. 

An engineering team can perform data wrangling both manually and automatically. It is crucial in larger organizations dealing with massive datasets. 

Benefits of Data Wrangling

Let us explore how data wrangling is crucial in providing efficient data for further use.

Data Quality: Improves data quality of unprocessed or raw data by working on missing data, errors, and inconsistencies to fix them.

Data Efficiency: Incorporating the data wrangling process into the dataset makes extracting necessary information and insights easier and more convenient in less time.

Saves Time and Resources: Utilizing various tools for automating data wrangling reduces the time and resources needed to process raw data, resulting in significant cost and effort savings.

Consistent Data: The data wrangling provides structure to raw data to make it available in an accessible format. This makes our data more consistent. Businesses and companies that deal with customers and depend on the inputs of customers require the data wrangling process to improve the massive data to bring consistency.

Better Insights and Decision Making: Information and in-depth insights can be easily taken from well-processed data as they provide accuracy in data analysis. This makes decision-making more productive and less time-consuming.

What is Data Cleaning?

Data cleaning, also called data cleansing, refers to identifying and correcting inaccurate data from a specific data source or data set. The process provides no mislabeled or duplicate data so that one can easily provide valuable insights. The process helps fix or eliminate inconsistencies without removing crucial, corrupted, inappropriately formatted, or incomplete data. Data cleaning enhances the dataset's validation by including various steps like filing empty regions, finding duplicate information or records, and rectifying structural errors. 

However, no specific steps are prescribed for data cleaning. This is because the data-cleaning process may vary with distinct datasets. Thus, it is crucial to determine the template for your data cleaning to perform it correctly based on the requirement.

Benefits of Data Cleaning

Data cleaning enhances the operational efficiency of the dataset. Thus, the process of data cleaning is beneficial in a broad spectrum, such as

Error Elimination: It helps remove errors that occur during data extraction from multiple sources. The lesser the error, the less frustrated the employee dealing with the dataset.

Cost Reduction: Tools that automate the data cleaning process reduce time consumption, effort, and resources, further saving costs in handling the data.

Enhanced Integrity: The data cleaning process ensures the fixing or removal of inconsistent or inaccurate data. Thus, it improves data quality and offers accurate, reliable, and consistent data for extracting valuable insights and decision-making processes.

Differences: Data Wrangling vs. Data Cleaning 

Data Wrangling and Data Cleaning Processes

There are certain series of steps in both data wrangling and data cleaning that need to be followed to get the desired result. Let us explore the processes of data wrangling vs. data cleaning below.

Data Wrangling Process

Step 1: Data Acquisition

The step involves the collection of data from multiple sources and storing them in a particular location.

Step 2: Data Cleaning

The step includes the cleaning of unnecessary, inappropriate, incorrect, corrupt, or misleading data to make the entire dataset consistent and accurate.

Step 3: Data Exploration

The step is performed to review the content and the structure of the dataset.

Step 4: Data Transformation

The step is necessary to transform the raw or unstructured dataset into a format required to perform data analysis.

Step 5: Data Loading

The step involves uploading or entering transformed data in a designated analysis platform or tool for further evaluation, processing, and extracting valuable insights.

Data Cleaning Process

Step 1: Data Inspection

It is the first step of the process involving the evaluation of data to find the inconsistencies, errors, corrupted and/or missing information in the dataset.

Step 2: Data Validation

The step involves assessing the data to qualify the standard rules to ensure the accuracy of the dataset.

Step 3: Data Correction

The step involves fixing or eliminating incomplete information, duplicate datasets, or misleading data.

Step 4: Data Standardization 

The step involves checking whether the data is in a consistent format and follows the standard guidelines. 

Step 5: Data Transformation 

The step involves the conversion of data into the desired first to proceed with further processing and analysis to provide valuable insights.

Note: The sequence of steps of both processes varies with distinct organizational workings, data, and the analysis to be done. Both processes need additional improvements continuously with time. In addition, multiple tools and technologies are available for data wrangling and data cleaning to ease the process and maintain accuracy in the dataset.

Data Wrangling and Data Cleaning Tools

Let us explore some well-known tools and technologies for data wrangling vs. data cleaning processes.

Data Visualization Tools

Tools like QlikView, Tableau, and Looker help explore and understand data structure and content. They also help generate graphs, maps, and charts to find the patterns and trends in the dataset.

Programming Languages

The language offers libraries, packages, and frameworks to wrangle, clean, and manipulate the dataset. The programming languages that are popularly used are SQL, R, Python, and Java.

Data Cleaning Software

Tools like Trifacta and Data Ladder are used in the data-cleaning process. These software programs are specifically designed to identify and fix missing information, inconsistencies, and errors in the dataset.

Data Wrangling Tools

Popular tools like Trificat and OpenRefine are used to convert, manipulate, and map data from one format to another as desired for further processing and analysis.

ETL Tools (Extract, Transform, Load)

Popular tools like Microsoft SSIS, Informatica, and Talend are particularly designed to extract data from diverse sources, collect them into specific locations, convert them into the desired format for analysis, and extract valuable insights.

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Conclusion 

Data wrangling vs. data cleaning are two major operations required in handling the dataset and delivering the desired outcome. Both involve certain steps in the processing, which can differ based on the organization, analysis, and data. It is important to incorporate the right approach to retrieving, storing, managing, and processing data to avoid inconsistency or misleading information. 

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FAQs on Data Wrangling vs. Data Cleaning

1. Can data wrangling and data cleaning overlap?

The presence of a data cleaning process within data wrangling acts as the overlapping point of the two methods. 

2. Are data wrangling and cleaning necessary for all types of data?

Both data wrangling and data cleaning are crucial to preparing accurate data before analysis. Data wrangling helps in manipulating records to transform them into the desired format. In contrast, data cleaning helps eliminate and fix inconsistencies in the data to make it reliable and consistent for analysis.

3. what skills are needed for effective data wrangling and cleaning?

Knowledge of programming language, ability to utilize advanced tools and techniques, understanding of data, attention to detail, and problem-solving skills are important to perform the two processes efficiently.