Data Wrangling

Data Wrangling Definition

Data wrangling, also known as data cleaning, data remediation, and data munging, is the process used to transform data from its raw form into a format that is easier to access and analyze. The goal is to prepare data for downstream analytics.

What is Data Wrangling?

FAQs

What is Data Wrangling?

Data wrangling is the process of cleaning, organizing, and unifying raw, complex, unorganized data sets so that they are more accessible for future data analysis. The wrangling process encompasses all the practices used to ensure that data is high quality and useful for analytics.

Tools for data wrangling provide a self-service model for data scientists to quickly process increasingly complex data sets with greater accuracy for improved decision making, and deeper, actionable insights.

Some data wrangling examples include: merging data from multiple data sources into a unified data set, identifying and rectifying gaps in data sets, identifying redundant or irrelevant data, and identifying outliers that need to be explained and/or removed.

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 & Techniques

Data wranglers use a wide variety of tools and languages for performing wrangling processes. Data wrangling using R and data wrangling with Python are popular statistical languages. Other common languages for performing wrangling include SQL, PHP, and Scala.

Some data wrangling using Python tools include Numpy, data wrangling with Pandas, Matplotlib, Plotly, and Theano. Some data wrangling in R tools include Dplyr, Purrr, Splitstackshape, JSOnline, and Magrittr. Other tools include data wrangling excel spreadsheets, OpenRefine, Tabula, and CSVKit.

With the increase in artificial intelligence in data science, it is important to hire data scientists who know how to data wrangle and are capable of manually imposing and monitoring strict checks and balances on the automated data wrangling process.

Does OmniSci Offer a Data Wrangling Solution?

OmniSci harnesses the parallel processing power of CPUs and GPUs to power seamless interactivity and analysis of big data without the latency of CPU-based solutions. As a result, ML inferencing results can be viewed real-time in OmniSci for instant interactive visual analysis and data wrangling at speeds never before possible.

On the OmniSci analytics platform, instantly cross-filter and visually interrogate all of your data in Immerse, regardless of scale, and leverage the blazing fast SQL and Python data science integrations of OmniSciDB for deeper analysis.