The integration of IoT (Internet of Things) and other technologies is generating huge amounts of data. Since data is gathered from different sources, it can lead to prejudice and errors in the reporting process. Data scientists spend about 60% of their time cleaning and organizing data, 19% on gathering data sets, and 80% on data preparation and management for analysis (Forbes). . Data wrangling and data cleaning are the two most useful techniques to avoid inaccurate data and make well-informed data-driven decisions.
What is Data Wrangling and What are its Benefits?
It is the process of transforming and mapping data from one raw or unprocessed format to another to make it easily accessible and understandable. Data wrangling is also known as data munging and is primarily used to transform complex data into an easily accessible format for proper analysis. It comprises actions to organize the data, identify trends, and remove inconsistencies.
Data wrangling is important in big organizations with large datasets. The best thing is that this process can be done manually and automatically. Data scientists and engineers can easily adapt to different formats and requirements using this technique.
An organization can benefit from the technique of data wrangling in numerous ways including the following:
- Ensures enhanced quality of raw data by allowing quick extraction of meaningful information with no errors, inconsistencies, or missing data.
- Gaining meaningful insights from the dataset easily, quickly, and conveniently.
- Facilitate less time-consuming and data-driven decisions-making by providing detailed and accurate insights from well-processed data.
- Save time and other valuable raw data processing using multiple tools for automating data wrangling. The saved time can be used to come up with proven strategies.
- Allows preparing data into an analysis-ready format and prevents complications.
- Companies relying on customer data often need data-wrangling techniques to maintain consistency in large data.
What is Data Cleaning and What are its Benefits?
Data cleaning or cleansing is the subset of data wrangling and provides accurate data from a certain data set by eliminating errors and inconsistencies. It involves rectifying errors or inconsistencies to provide accurate insights for analysis with no duplicate, corrupted, incomplete, or mislabeled data. Since the data cleaning process may differ based on the datasets, it does not require certain steps. So, pay attention to the template for your data cleaning to do it appropriately.
Although the main objective of data cleaning is to ensure data accuracy and reliability with no inconsistencies or errors, it can benefit your organization in numerous ways, as listed below:
- Improves the operational efficiency of the dataset by detecting mistakes and eliminating or replacing them with crucial values.
- Boost productivity by eliminating employee frustration of dealing with errors encountered while extracting data from numerous sources.
- Training ML algorithms with clean, accurate data improves the model’s accuracy.
- Error and inaccurate and inconsistent data removal improves the data quality and helps organizations with informed decision-making by getting meaningful insights.
- Using advanced data cleaning tools provides cost-effective solutions in data management by automating the data cleaning process and saving effort and other resources. Improved efficiency of analytical tools facilitates better predictions and saves the cost related to repeated analysis.
Key Factors Differentiating Data Wrangling and Data Cleaning
| Factors | Data Wrangling | Data Cleaning |
| Purpose | Convert raw data into usable and algorithm-friendly format through data cleaning, structuring, and transformation. | Remove inconsistencies, outliers, missing values, bias, and errors from data to ensure data reliability and precision. |
| Outcomes | Organized and ready-to-analyze data. | Error-free, consistent, and clean data. |
| Processes | Data acquisition, data cleaning, data exploration, data transformation, data integration, and data loading. | Data inspection, data validation, data correction, data duplication, data standardization, data transformation, and handling missing data. |
| Scope | Wider scope. | Narrower scope with more focus on correcting errors. |
| Focus | Convert raw data and develop a basis for downstream analytics. | Ensure data accuracy and reliability by refining data. |
| Flexibility | Highly flexible to adapt to multiple data sources and formats. | Maintain strict data quality standards using a highly rigid method. |
| Tools | Trificat, Talend, Informatica, and programming languages, such as Python, SQL, Java, and R. | Trifacta, OpenRefine, Data Ladder, DataCleaner, and Talented Data Quality. |
| Tasks | Managing missing values, filtering, standardizing formats, and normalization. | Eliminating duplicates, identifying and handling outliers, and addressing inconsistencies. |
| Examples | Integrating several datasets to organize data. | Removing typos and eliminating duplicate records. |
| Team Collaboration | Teams work together for data wrangling and cleaning. | Encourage shared responsibility for data quality by aligning efforts. |
Conclusion
Organizations often need to extract insights from data and facilitate data-driven decisions-making because raw data is collected from multiple sources and is generally unstructured. To effectively manage and refine data for further usage, organizations rely on data scientists and data engineers. Thus, if you are seeking a career in data, learn the techniques of data wrangling and data cleaning, which play a significant role in data preparation.