What is Data Wrangling? Process, Tools and Techniques.

Data wrangling is “the process of programmatically transforming data into a format that makes it easier to work with. This might mean modifying all of the values in a given column in a certain way, or merging multiple columns together. The necessity for data wrangling is often a by-product of poorly collected or presented data. Data that is entered manually by humans is typically fraught with errors; data collected from websites is often optimized to be displayed on websites, not to be sorted and aggregated.

Data Wrangling Steps

While each data project has its own unique requirements for its data, data wrangling methods generally consist of the same six data wrangling process steps:

Data Discovery: During discovery, the criteria by which data should be categorized is established with the application of advanced analytics techniques. The goal of this step is to navigate and better understand the data, detect patterns, gain insights, answer highly specific business questions, and derive value from business data.
Data Structuring: Data is extracted in all shapes and sizes. During structuring, raw, disparate unstructured data is processed and restructured according to different analytical requirements so that it is useful. This step is achieved with the use of machine learning algorithms, which perform analysis, classification, and categorization.
Data Cleaning: The cleaning step involves dealing with data that may distort analysis. During this process, errors and outliers that come with raw data are identified, corrected, and/or removed.
Data Enriching: After data is explored and processed, it needs to be enriched. Data enrichment is the process of enhancing, refining, and improving raw data. This is accomplished with the merging of third-party data from an external authoritative source.
Data Validating: Data consistency and quality are verified via programming during the validation step. Data validation can be performed with enterprise tools, open source tools, and scripting.
Data Publishing: Publishing is the delivery of the final output of wrangling efforts. This output is pushed downstream for analytics projects.

Data Wrangling Tools and Techniques:

It has been observed that about 80% of data analysts spend most of their time in data wrangling and not the actual analysis. Data wranglers are often hired for the job if they have one or more of the following skillsets: Knowledge in a statistical language such as R or Python, knowledge in other programming languages such as SQL, PHP, Scala, etc.

They use certain tools and techniques for data wrangling, as illustrated below:

Excel Spreadsheets: this is the most basic structuring tool for data munging
OpenRefine: a more sophisticated computer program than Excel
Tabula: often referred to as the “all-in-one” data wrangling solution
CSVKit: for conversion of data
Python: Numerical Python comes with many operational features. The Python library provides vectorization of mathematical operations on the NumPy array type, which speeds up performance and execution
Pandas: this one is designed for fast and easy data analysis operations.
Plotly: mostly used for interactive graphs like line and scatter plots, bar charts, heatmaps, etc

R tools

Dplyr: a “must-have” data wrangling R framing tool
Purrr: helpful in list function operations and checking for mistakes
Splitstackshape: very useful for shaping complex data sets and simplifying visualization
JSOnline: a useful parsing tool

Importance of Data Wrangling

Data wrangling software has become such an indispensable part of data processing. The primary importance of using data wrangling tools can be described as:

Making raw data usable. Accurately wrangled data guarantees that quality data is entered into the downstream analysis.
Getting all data from various sources into a centralized location so it can be used.
Piecing together raw data according to the required format and understanding the business context of data
Automated data integration tools are used as data wrangling techniques that clean and convert source data into a standard format that can be used repeatedly according to end requirements. Businesses use this standardized data to perform crucial, cross-data set analytics.
Cleansing the data from the noise or flawed, missing elements
Data wrangling acts as a preparation stage for the data mining process, which involves gathering data and making sense of it.
Helping business users make concrete, timely decisions

Benefits:

Data wrangling helps to improve data usability as it converts data into a compatible format for the end system.
It helps to quickly build data flows within an intuitive user interface and easily schedule and automate the data-flow process.
Integrates various types of information and their sources (like databases, web services, files, etc.)
Help users to process very large volumes of data easily and easily share data-flow techniques.

Applications:

Data wrangling techniques are used for various use-cases. The most commonly used examples of data wrangling are for:

Merging several data sources into one data-set for analysis
Identifying gaps or empty cells in data and either filling or removing them
Deleting irrelevant or unnecessary data
Identifying severe outliers in data and either explaining the inconsistencies or deleting them to facilitate analysis

Businesses also use data wrangling tools to