Data Wrangling

Wrangling data involves the systematic and iterative transformation of raw, unstructured, or messy data into a clean, structured, and usable format for data science and analytics. 

Here we describe the 6 key steps:

Step 1: Discover

Initially, your focus is on understanding and exploring the data you’ve gathered. This involves identifying data sources, assessing data quality, and gaining insights into the structure and format of the data. Your goal is to establish a foundation for the subsequent data preparation steps by recognizing potential challenges and opportunities in the data.

Step 2: Structure

In the data structuring step, you organize and format the raw data in a way that facilitates efficient analysis. The specific form your data will take depends on which analytical model you’re using, but structuring typically involves reshaping data, handling missing values, and converting data types. This ensures that the data is presented in a coherent and standardized manner, laying the groundwork for further manipulation and exploration.

Step 3: Clean

Data cleansing is a crucial step to address inconsistencies, errors, and outliers within the dataset. This involves removing or correcting inaccurate data, handling duplicates, and addressing any anomalies that could impact the reliability of analyses. By cleaning the data, your focus is on enhancing data accuracy and reliability for downstream processes.

Step 4: Enrich

Enriching your data involves enhancing it with additional information to provide more context or depth. This can include merging datasets, extracting relevant features, or incorporating external data sources. The goal is to augment the original dataset, making it more comprehensive and valuable for analysis. If you do add data, be sure to structure and clean that new data.

Step 5: Validate

Validation ensures the quality and reliability of your processed data. You’ll check for inconsistencies, verify data integrity, and confirm that the data adheres to predefined standards. Validation helps in building your confidence in the accuracy of the dataset and ensures that it meets the requirements for meaningful analysis.

Step 6: Publish

Now your curated and validated dataset is prepared for analysis or dissemination to business users. This involves documenting data lineage and the steps taken during the entire wrangling process, sharing metadata, and preparing the data for storage or integration into data science and analytics tools. Publishing facilitates collaboration and allows others to use the data for their analyses or decision-making processes.

If you’re like most data scientists or data analysts, you’ll spend most of your time as a wrangler. So you’ll want to be as efficient as possible in your data wrangling techniques.

The good news is that various aspects of the time-consuming process can be automated or made more efficient with the use of specialized tools. Here are some key aspects and the types of data management tools that can be employed:

Discovery: Data discovery tools and metadata management tools help automate the process of identifying and cataloging data sources such as spreadsheets, databases, excel files, APIs, or online platforms.

Structuring: Data cleaning and data transformation tools automate the process of reshaping and structuring data. These tools often provide graphical user interfaces for intuitive manipulation.

Cleaning: Data cleaning tools can automate the identification and cleaning of errors, duplicates, and missing values. Machine learning-based tools may also assist in outlier detection.

Enriching: Big data integration tools facilitate the merging of datasets and the incorporation of additional information. APIs and web scraping tools can also automate the retrieval of external data.

Validating: Data quality tools such as offer automated validation checks for ensuring data accuracy and integrity. These tools can help in setting and enforcing data quality standards.

Publishing: Data analytics and data visualization tools can help in preparing data for presentation and analysis. Data cataloging tools aid in documenting and sharing metadata, and version control tools ensure traceability of changes made during the wrangling process.

Automation not only saves you time and effort but also reduces the risk of human error. Still, it’s crucial for you to oversee things as a data wrangler, especially decisions about data quality, transformations, and enrichment strategies.

Overall, data wrangling plays a crucial role in your overall data management process, transforming raw data into a valuable asset for your organization, helping you extract meaningful insights and make informed business decisions based on high-quality data.

Improved Data Quality and Consistency

• Wrangling addresses issues like missing values, outliers, and inconsistencies, enhancing overall data quality. Standardizing formats, units, and structures ensures consistency, making your data integration more seamless.

Increased Analysis Efficiency and Support for EDA

• Systematic cleaning and transformation reduce time and effort for downstream analysis, allowing you to focus on deriving insights. Well-wrangled datasets support effective Exploratory Data Analysis (EDA) by facilitating pattern identification and relationship exploration.

Facilitates Data Integration and Adaptability

• Wrangling data enables the integration of information from diverse sources, providing you a comprehensive view of the data. The process is adaptable to changes in data sources or formats, accommodating evolving business needs and data requirements.

Preparation for Machine Learning and Enhanced Reproducibility

• Your machine learning models benefit from clean, structured input data. Clear documentation of the wrangling process enhances reproducibility, ensuring transparency and facilitating the replication of analyses.

Supports Informed Decision-Making and Reduces Errors

• Reliable, well-organized data contributes to more informed decision-making. The systematic addressing of data quality issues reduces errors in analysis and reporting, leading to more accurate and trustworthy results.

The following four examples provide real-world scenarios and use cases where wrangling data enhances data reliability for informed decision-making.

Merging Datasets. In marketing campaign analysis, merging datasets based on customer IDs is a common task. This use case involves combining data on customer demographics with information on marketing campaign responses. Wrangling facilitates this process, allowing marketers to gain insights into the effectiveness of campaigns among different customer segments. Another use case involves supply chain optimization, where merging datasets containing information on inventory levels and supplier performance is crucial. Wrangling helps you align data based on product codes or supplier IDs, enabling detailed analysis to optimize inventory levels and enhance overall supply chain efficiency.

Handling Missing Values. Wrangling is crucial in scenarios like Customer Relationship Management (CRM) databases, where incomplete customer records with missing contact information are common. In this use case, wrangling techniques, such as imputation based on existing data patterns or removal of records with extensive missing information, play a vital role. These methods ensure a more complete and accurate customer database, supporting effective customer engagement and communication. Additionally, in financial analysis, especially when tracking quarterly revenue, missing values due to reporting delays can be addressed. Techniques like forward-filling or interpolation based on historical trends help maintain the integrity of financial time series data, enabling accurate trend analysis and informed decision-making.

Standardizing Data Formats. Consider the use case of an e-commerce company integrating product data from various suppliers, each using different units for weight and dimensions. Wrangling becomes essential in this scenario by converting all measurements to a standardized unit, ensuring consistency across the product catalog. This standardized data facilitates a seamless online shopping experience for customers and streamlines internal operations. In healthcare data aggregation, where patient records from different sources may have diverse date formats for medical procedures, data wrangling tasks involve standardizing date formats. This enables the integration of patient history across multiple healthcare providers, supporting comprehensive analysis for improved patient care and treatment planning.

Creating Derived Features. Consider the use case of employee performance metrics in HR analytics. Here, wrangling plays a significant role in calculating a new feature such as "average project completion time" by aggregating historical project data. This derived feature contributes to a more nuanced evaluation of employee performance, aiding in strategic decision-making related to talent management and project allocation. In social media analytics, creating a derived feature like "engagement rate" is essential for understanding audience interactions. Wrangling facilitates the calculation of this metric by dividing the number of interactions (likes, comments, shares) by the number of followers, providing valuable insights into user engagement and content effectiveness.

What is data wrangling vs data cleaning?

Data cleaning focuses on identifying and rectifying errors, inconsistencies, and inaccuracies in a dataset to ensure accuracy and reliability. In contrast, wrangling data is a broader process that encompasses data cleaning but extends to tasks such as reshaping, merging, and enriching data to make it suitable for analysis. While data cleaning is a specific aspect within wrangling, the latter involves a more comprehensive workflow for preparing raw data for meaningful insights.

What is data wrangling vs ETL?

Data wrangling and ETL (Extract, Transform, Load) are related but distinct processes in data management. The former focuses on the cleaning, structuring, and preparation of raw data for analysis, emphasizing flexibility and iterative exploration. ETL, on the other hand, is a formalized process for extracting, transforming, and loading data from source systems to a target destination, often involving scheduled, batch-oriented workflows for data integration in a more structured and automated manner.

What is the difference between data mining and data wrangling?

Data mining and data wrangling are distinct stages in the data analysis process. Data mining is the actual process of discovering meaningful patterns, trends, or insights from the pre-processed data, involving the application of statistical and machine learning techniques to extract valuable knowledge from datasets. In contrast, wrangling involves the cleaning, organizing, and structuring of raw data to make it suitable for analysis, emphasizing the preparation of data for insights.