Data processing is a high-priority task because of the exponential rise in data consumption. Data is analyzed by being manipulated to get insights and useful information according to one’s requirement, beginning with collecting or scraping data, conducting analysis, and producing dashboards. Retaining raw data into information and using it to perform further business predictions drives enterprises toward data-driven decisions. For this, the industry of data science and analytics is booming. Although data management and data modeling are crucial aspects of data analysis, data wrangling in data science has been the core emphasis since the beginning. Data wrangling is a collection of processes that transform raw data and can be labeled as the prerequisite to a successful data analysis. Let’s see what data wrangling is in data science, its importance, the steps for data wrangling, and the skills for data wrangling.
What is Data Wrangling?
Data wrangling, also known as data munging, or data remediation, is a collection of processes, including cleaning, organizing, structuring, and enriching raw data to transform it into a readily usable format. The methods for data wrangling vary greatly depending on the dataset and the objective of the project. This is an important prior step to data analysis to provide data quality.
Though new technologies have empowered shortcuts to ease heavy workloads, it is not so much in data wrangling. For which, the whole implementation of data wrangling remains manual. The process being manual consumes a lot of time, and according to Forbes, data scientists and analysts spend 80% of their time on data wrangling. And for a fact, data wrangling is not the most enjoyable part of the role. The reason why data wrangling is time-consuming is the whole process is fluid, and the steps to begin and end are vague or not definite for all datasets. However, there are six steps to data wrangling that give a general idea of what one must look for data quality and data reliability. Also, data wrangling methods need to adapt to a particular dataset, which is iterative, making data wrangling a labor-intensive process. Overall, the data wrangling process depends on factors like the source of data, the quality of data, the data architecture of the firm, and the aim of data analysis.
Importance of Data Wrangling
Data wrangling is necessary for the data science process as it delivers information through analysis. Any analysis eventually brings helpful insight into information or trends in a business, be its data analysis for modeling and prediction, building dashboards, or making reports. The process serves as an initial step to remove the risk of errors ensuring the data is reliable for further analysis. Alike laying a foundation initially goes long for a strong establishment, data wrangling enables the transformation of data into the desired format, which then produces valuable outputs. And if data wrangling is avoided, it may lead to significant downfalls, missed opportunities, and erroneous models, costing you time, money, resources, and the firm’s reputation.
The primary tasks that data wrangling tools help with are –
- Increasing data usability: Making raw data usable by transforming it into another format and securing data quality.
- Ease of data collection: Gathering data from various sources into a single centralized location.
- Clean data: Detection of noise or flaws and missing observations is simpler when sorting data of the preferred format.
- Business-oriented approach: Gathering raw data in one place and converting it to the required format eases the tasks of identifying the business’s best interest and improves the targeting of audience.
- Quick decision-making: As most errors and mistakes are eliminated already in data wrangling, further data processing is smoother to provide rapid data-driven decisions or models.
- Visualization of data: Visualization is the key to understanding anything at first glance. Many data analysts and scientists prefer to include a visual representation in data wrangling and exploratory analysis, ensuring the best aspects of the data are reflected. Once the data is wrangled, export it to a visual analytics platform that will summarize, sort, and analyze the data.
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Six Steps in Data Wrangling
Each step in data wrangling offers to manipulate data to understand better and extract information hidden in the data.
The first step of data wrangling is to discover. As simple as it sounds, discovering data means getting to know the data and conceptualizing how you can use it. Manually getting familiar with the dataset is crucial in ultimately catching patterns and pushing the limits of what one can do with it. In discovery, the easiest errors to find is missing or incomplete values, and plan to structure the data in an organized manner.
As the collection of data may come from more than one source, it may contain numerous data formats and sizes. The data required to be restructured and organized to make it more manageable for the analytical model. This step includes general standardization like string for names, integers for salary, date format for date, and so on.
Data cleaning consists of tasks dealing with errors, including duplicate entries, invalid values, and null values. Many people think data wrangling and data cleaning are the same, but that is not true. Data cleaning is included in the step of gaining wrangled data. It includes tasks like making corrections, removing errors, handling outliers, eliminating unnecessary data points, etc. Data cleaning can be performed swiftly with programming languages like Python, R, and SQL.
This step determines if the data need to consider external data for better performance. This especially helps data miners address certain labels not included in the dataset beforehand but prove to bring out relevant information in the dataset. Here, the goal is to fill the gaps in the data (if any) to derive meaningful information and, in the end improve the analysis. Enriching is optional in data wrangling but holds great significance if the current data cannot provide better insights.
As data is continuously manipulated and edited in data wrangling, this step checks the quality of the wrangled data. The process is to verify whether or not the data has quality, consistency, accuracy, security, and authenticity. The validation is extensively thorough, using some automated techniques in programming. And if the data doesn’t fit the requirement, the issues are resolved using different techniques, and the whole process is iterative until you reach the desired or best possible outcome.
Publishing is the final step in data wrangling, where the wrangled data output is ready for analytics. The data is to be published in an easily accessible location for the team to work on it, such as a new data architecture or database server. The output dataset here is a standardized version of itself, without the errors, sorted and categorized.
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Data Wrangling Skills
Good data wrangling skills are one of the most essential skills of a data scientist and analyst. Knowing the dataset entirely allows you to enrich the data by integrating information from multiple sources and solving common transformation problems and data quality issues. To promote data wrangling skills for the job, companies prefer to train interns and freshers with skills like data annotations, web scraping, and data transformation, including merging, ordering, aggregation, and so on. This training helps to induce the mindset to find and fix errors by knowing where the errors could come. Though the source of errors is vague, the idea is to eliminate errors by seeing through the raw data.
The tools used for data wrangling include programming languages, software, and open-source data analytics platform. Some tools are MS Excel Power Query, Python and R, Alteryx APA, and more. Some visual data wrangling tools like OpenRefine, Trifacta, and Tableau are also designed for beginners and non-programmers. Each tool has its specifications, such Trifacta features cloud integration, standardization, and easy flow, MS Excel features broad connectivity with data sources and combines tables, Tableau features visual appeal, high security and real-time sharing, and so on. There is no best or all-rounder tool for data wrangling yet in the market, as the use depends on the requirement and goal for analysis using a dataset.
As data wrangling consumes a lot of time, new automated solutions are developed that use machine learning algorithms. Yet the development of automated solutions for data wrangling is tough as the process requires intelligence and not only a repeated process of work. These automated tools aim to validate data mapping and inspect data samples thoroughly at each step of transformation. There are few automated software available today using end-to-end machine learning pipelines, performing the three domains of automation in data wrangling, cleaning, structural curation, and data labeling.
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