Data wrangling is known to be a process of removing errors and combining complex data sets to make them accessible and also easier to analyze. With the amount of data and data sources that are available today, storing and organizing large quantities of data analysis becomes necessary.
Data wrangling process called a data mangling process consists of reorganizing, transforming, and mapping data from one raw form into another to make it more usable and valuable for a variety of downstream uses including analytics. This can also be defined as a process of cleaning, organizing, and transforming raw data into the required format for analysts to use for prompt decision-making. It can also be known as data cleaning or data mangling. Data wrangling will activate the business to handle the complex data less in time, produce more accurate results and make better decisions. The methods may differ from projects depending on the goal that we are trying to achieve.
The importance of Data wrangling is:
Data wrangling software has such an indispensable part of data processing. The importance of using data wrangling tools:
- By making raw data usable. Wrangled data guarantees the quality data that is entered into the downstream analysis.
- By getting all the data from many sources into a centralized location as it can be used.
- There are automated data integration tools that are used as data wrangling techniques that will be cleaned and converted from the source of data into a standard format that can be used repeatedly according to end requirements.
- Cleansing the data from noise or flawed, missing elements.
- By helping business users make concrete, timely decisions.
Benefits of Data wrangling:
- Data wrangling will help us to improve the data usability and also converts the data into a compatible format for the end system.
- It will help us quickly to build the data flows within an intuitive user interface and easily schedule and automate the data flow process.
- It also integrates many types of information and their sources
- It also helps users to process very large volumes of data and easily share data flow techniques.
We have many tools for data wrangling which will be used for gathering, importing, structuring and also cleaning data before it will be fed into analytics and BI apps. We can use the automated tools for data wrangling, where the software will allow us to verify the data mappings and scrutinize data samples at each step of the transformation process. The errors can be detected and fixed in data mapping quickly. Automated data cleaning will be necessary for businesses dealing with exceptionally large data sets.
There are some examples of data-wrangling tools are:
- Spreadsheets/excel power query- This is the most fundamental data-wrangling tool.
- Open Refine- This is an automated data-cleaning tool that requires programming skills.
- Tabula-This will be a tool suited for all the data types.
- Google DataPrep- It will be the best data service that will explore, clean, and prepare data.
- Data wrangler- This will be used as a data cleaning and transforming tool.
Data wrangling examples
Data wrangling techniques will be used for many use cases. The commonly used examples are:
- Merging many data sources into the data set for analysis.
- Identifying the gaps or empty cells in data and either filling or removing them.
- By deleting many irrelevant or unnecessary data.
The business also uses data-wrangling tools for below purposes:
- To detect corporate fraud
- To support data security.
- To make sure that accurate and recurring data modeling results.
- To ensure business compliance with industry standards.
Questions:
- What is data wrangling?
- What are the uses of data wrangling?
