Data Wrangling: Process, Tools, Use Cases & Benefits

DATA WRANGLING TRANSFORMING RAW DATA INTO ANALYSIS-READY DATA Presented by: Rachit Joshi

WHAT IS DATA WRANGLING? • Data wrangling is the process of transforming and mapping raw data into a more useful format.

• It is also known as Data Munging.

• It makes data suitable for data analysis and other downstream purposes.

• It improves the quality, structure, and usability of data.

• Common activities include: •

Extracting data

•

Parsing data

•

Sorting data

•

Transforming data

•

Storing data

IMPORTANCE OF DATA WRANGLING • Makes raw data usable for downstream analysis.

• Brings data from different sources into a centralized location.

• Organizes raw data according to the required format and business context.

• Cleans and converts data into a standard format.

• Removes noise, flawed data, and missing elements.

• Acts as a preparation stage for data mining.

• Helps business users make concrete and timely decisions.

DATA WRANGLING PROCESS Discovery → Organization → Cleaning → Data Enrichment → Validation → Publishing Discovery • Define objectives.

• Understand what you want to achieve with the data.

Organization • Structure the collected raw data.

• Handle different data types and sources.

Cleaning • Remove outliers.

• Handle null values.

• Eliminate duplicate data.

Data Enrichment • Determine whether enough data is available.

• Ensure sufficient data for meaningful insights.

Validation • Apply validation rules.

• Check consistency, quality, and security.

Publishing • Prepare data for future use.

• Provide documentation and access to users/applications.

USE CASES OF DATA WRANGLING 1. Fraud Detection • Identify unusual behavior.

• Support data security.

• Standardize data for accurate and repeatable modeling.

• Help maintain industry and government compliance.

2. Customer Behavior Analysis • Reduce time spent preparing data.

• Understand the business value of customer data.

• Allow analytics teams to use customer behavior data.

• Discover trends through data discovery and visual profiling.

DATA WRANGLING TOOLS • Spreadsheets / Excel Power Query — Basic manual data wrangling.

• OpenRefine — Automated data cleaning.

• Tabula — Data wrangling for different data types.

• Google DataPrep — Explores, cleans, and prepares data.

• Data Wrangler — Data cleaning and transformation.

• Plotly — Useful for maps and chart data with Python.

• CSVKit — Converts data.

BENEFITS & DATA WRANGLING FORMATS Benefits •

Data Consistency

•

Improved Insights

•

Cost Efficiency

•

Improved data usability

•

Easier creation and automation of data flows

•

Integration of different information sources

•

Ability to process large volumes of data.

Data Wrangling Formats •

Transactional Data

•

Analytical Base Table (ABT)

•

Time-Series Data

•

Document Library

DATA WRANGLING EXAMPLES Examples: • Merge multiple data sources into one dataset.

• Identify missing or empty cells and fill or remove them.

• Delete irrelevant or unnecessary data.

• Identify severe outliers and explain or remove them.

• Detect corporate fraud.

• Support data security.

• Analyze customer behavior.

• Discover data trends.

CONCLUSION • Data wrangling is an important data-preparation process that transforms raw data into useful and reliable information.

• By organizing, cleaning, enriching, validating, and publishing data, organizations can improve data quality and make better use of their data for analytics and decision-making.