Data wrangling is the process of converting and organizing raw data into a structured and usable format. By turning raw data into valuable information, organizations can gain insights that drive business strategies, improve operational efficiency, and enhance customer satisfaction.
by Maricor Bunal
November 7, 2023
Data wrangling, also known as data munging or data blending, is the process of transforming and mapping raw data into a structured, clean, and usable format.
In this comprehensive guide, we will explore the importance of this fundamental process, the steps involved, the common tools used, and best practices for ensuring success.
Data wrangling is the process of converting and organizing raw data into a structured and usable format.
By turning raw data into valuable information, organizations can gain insights that drive business strategies, improve operational efficiency, and enhance customer satisfaction.
These three are processes used in data preparation, but they serve different purposes and are used at different stages of data analysis.
Data wrangling is a comprehensive process that includes various methods to transform raw data into a format useful for analysis. It involves several steps:
Discovering - Identifying the right data sources.
Structuring - Organizing data into a structured form.
Cleaning - Removing inaccuracies and fixing errors.
Enriching - Augmenting the data with new information or context.
Validating - Ensuring the data adheres to certain quality standards.
Publishing - Making the data available for use.
Data cleaning is a subset of data wrangling. It focuses specifically on correcting errors to ensure data accuracy and consistency. This includes:
Removing duplicates - Eliminating repeated entries.
Fixing structural errors - Correcting misaligned data or incorrect data formats.
Handling missing data - Filling in or deciding how to treat null or missing values.
Filtering outliers - Identifying and resolving anomalies in the data.
Data parsing is a technical process that often comes before data cleaning.
Done to structurally converting data from one format to another, and make the data readable by different software or processed in a particular way. It involves:
Interpreting - Analyzing a string of data and breaking it down into components that are easier to understand and use.
Converting - Changing data from one format to another, such as from JSON to CSV.
Extracting - Pulling out specific data from a larger dataset based on certain patterns or symbols.
In summary, data wrangling is the overarching process of preparing data for analysis, which includes data cleaning (focused on error correction) and data parsing (focused on format conversion). Each serves a distinct role in the journey from raw data to insights.
Data wrangling can ba complex and time-consuming.
Organizations may face common challenges such as:
Data Quality. Raw data often contains errors, inconsistencies, and missing values that can affect the accuracy of actionable insights derived from the data.
Solution: Implement data cleaning techniques to identify and correct errors, standardize values, and fill in missing data.
Data Integration. Raw data may come from multiple sources, each with its own format and structure, making it difficult to combine the data into a cohesive dataset.
Solution: Use data transformation tools to map and merge data from different sources into a single, unified format.
Data Volume. The sheer volume of raw data can be overwhelming, especially for organizations that lack the necessary resources and infrastructure to handle large datasets.
Solution: Leverage cloud-based platforms and distributed computing technologies to store and process large volumes of data efficiently.
Data Complexity. Raw data can be complex and unstructured, requiring specialized knowledge and skills to extract meaningful information.
Solution: Employ data wrangling tools and techniques, such as machine learning algorithms and natural language processing, to analyze and extract insights from complex data.
By addressing these challenges and implementing effective data wrangling practices, organizations can unlock the full potential of their raw data and gain actionable insights to drive business success.
Data wrangling is a complex process that involves several key steps that ensure high-quality data is available for accurate business decisions.
This involves identifying and collecting disparate data sources that are relevant to the business decisions at hand.
The discovery stage is a critical step in the analytics process as it sets the foundation for the entire process.
The next step is structuring the data into a standard format that can be easily analyzed.
This involves converting non-standard format file formats into a compatible format for analysts.
The structuring process may also involve organizing disparate data sources into a single dataset.
A critical step in data wrangling, cleaning involves identifying and correcting errors such as duplicate values, incomplete values, and structural errors.
The cleaning process also includes standardizing values and correcting errors caused by human errors or invalid outcomes.
After cleaning, the data is enriched with additional insights.
This involves adding data sources or complex data structures to the dataset to enhance its value for business decisions.
The enriching process may also involve the mining process and the munging process to extract additional insights from the data.
Validation rules are applied to the data to ensure that it is high-quality and meets the business rules and standards.
The validation process also includes checking the accuracy of fields and correcting any errors.
The final step in the data wrangling process is publishing the data in a usable form for analytics teams and business analysts.
This step involves creating business reports and analytical models that can be used to make accurate business decisions.
The process may also involve automated processes or manual data wrangling tools to ensure that the data is in the correct format for analysts.
The entire process of data wrangling requires a combination of analytical skills, exploratory analysis, and exact methods to ensure that the data is of the highest quality and accuracy.
By following these steps, organizations can ensure that they are making accurate business decisions based on high-quality data.
Data wrangling tools are required to ensure high-quality data available for accurate business decisions. Here are some common tools used in data wrangling:
One of the most basic and widely used data wrangling tools, spreadsheets are ideal for small data sets and simple data cleaning tasks such as removing duplicate values and correcting structural errors.
Excel and Google Sheets are two popular spreadsheet programs that offer a range of features for data wrangling.
Business Intelligence tools are used for analyzing and visualizing complex data sets.
They allow business users to create interactive dashboards and reports that can be used to make informed business decisions.
Tableau and Qlik are business intelligence tools that offer a range of features for data wrangling.
Cloud platforms are used for storing and processing large data sets and offer a range of services for data wrangling such as data storage, data processing, and machine learning.
Google Cloud, Microsoft Azure, and Amazon Web Services are three popular cloud platforms that offer such features..
Used for automating the data wrangling process, data services provide data cleaning, data transformation, and data integration.
Talend, Paxata, and Alteryx are three popular data services.
Here are some tips to ensure high-quality results from data wrangling:
Know the structure and format of your data, as well as the business rules and requirements that govern it.
Understanding your data is a critical step in the preparation stage of the analytics process and ensures that you are able to make informed decisions based on accurate and reliable data.
As mentioned earlier, data wrangling tools range from manual tools to automated processes and machine learning algorithms.
The right tool will depend on the complexity of your data sets and the specific requirements of your analytics process.
Clean and validate your data to ensure that it is free from errors, duplicate values, and incomplete values.
Quality data must be in the correct format and meets the accuracy of fields and validation rules required for accurate business decisions.
Working closely with business analysts, marketing teams, and analytics teams ensures that your data is aligned with the business goals and objectives of your organization.
Collaboration also ensures that your data is used effectively and that valuable insights are derived from it.
The goal of data wrangling is to clean, structure, and enrich the data in such a way that it becomes a valuable asset for making informed decisions and driving strategic initiatives.
Data wrangling can be tailored to the specific types of data and the unique challenges they present, as show in these sectors:
In the banking sector, data wrangling might involve:
Transaction Data: Aggregating and organizing transaction records from various systems to detect fraudulent activity or to personalize customer experiences.
Customer Data: Integrating data from different banking channels (online, mobile, in-branch) to create a single view of the customer for better service and marketing.
Regulatory Data: Ensuring compliance with financial regulations by standardizing and validating data against regulatory requirements.
In healthcare, data wrangling might include:
Patient Records: Consolidating patient information from various electronic health record (EHR) systems to improve care coordination and patient outcomes.
Clinical Trial Data: Combining data from different phases and sources of clinical trials to analyze the efficacy and safety of new treatments.
Genomic Data: Structuring and cleaning vast amounts of genomic data to facilitate personalized medicine and genetic research.
In the insurance industry, data wrangling is used for:
Claims Data: Harmonizing data from claims submissions, adjuster reports, and third-party sources to streamline claims processing and detect fraud.
Policyholder Data: Merging information from various policy administration systems for a unified view of policyholders, risk assessment, and customer service.
Risk Data: Aggregating internal and external data sources to assess risk more accurately and set premiums accordingly.
In manufacturing, data wrangling might involve:
Supply Chain Data: Integrating data from various points in the supply chain to optimize inventory levels and predict maintenance needs.
Sensor Data: Processing and organizing data from IoT devices and sensors on the manufacturing floor to improve operational efficiency and product quality.
Quality Control Data: Analyzing data from quality checks to identify patterns in defects and to inform process improvements.
In the public sector, data wrangling could include:
Census Data: Combining demographic information from various surveys and censuses to inform policy decisions and resource allocation.
Public Records: Standardizing data from different public records databases for research, transparency, and governance purposes.
Social Services Data: Aggregating data across multiple social service programs to better understand community needs and the impact of services provided.
Poor data wrangling can lead to inaccurate analyses, misguided business decisions, and inefficient operations.
It can cause significant time and resource wastage as teams may need to reprocess data.
Additionally, it can result in compliance risks if erroneous data violates regulatory standards.
The difficulty of data wrangling depends on the complexity of the data sets and the skills of the individuals involved.
It can be challenging due to the variety of data formats, volume, and the need for attention to detail.
However, with the right tools and expertise, it can be managed effectively.
Data wrangling is a subset of data preparation.
While data preparation encompasses the entire process of getting data ready for analysis, including collection, integration, and cleaning, data wrangling specifically refers to the process of transforming and mapping raw data into a more usable format.
Data wrangling includes data transformation as one of its steps.
Data transformation is the process of converting data from one format or structure into another.
Data wrangling is broader and includes additional steps such as cleaning, validating, and structuring data.
Data wrangling is the transformative process that converts raw, unstructured data into a refined, structured format ready for insightful analysis and strategic decision-making.
This guide has navigated through the essential facets of data wrangling, from its definition to the nuanced steps, tools, and methodologies that ensure its successful execution.
With a firm grasp on data wrangling, organizations are better positioned to harness their data's full potential, fostering informed decision-making and fostering a competitive edge in their respective industries.
With the insights from this guide, you too can elevate your organization's data practices.
Begin by assessing your current data wrangling procedures, pinpointing opportunities for enhancement.
Equip your team with the appropriate tools to guarantee that your data is not only clean and organized but also primed for analytical endeavors.
The caliber of your data is a direct determinant of your decision-making precision, so don't delay. Refine your data wrangling skills now and transform your data into a powerful asset for your business.
