Nowadays, the internet is perhaps the most valuable source of information. However, the main challenge is our ability to extract valuable and actionable insights from data. Before analyzing these data, we need to collect and prepare this information in order to make it easy to understand. In simple terms, this is what "data wrangling" means - the processes we need to go through to make sense of data. Let's check out what data wrangling is and how such software helps you with data processing and understanding.
Background of data wrangling
Data wrangling is defined as a concept that refers to the time we spend working with data to prepare it for analysis. The data wrangling process might include the time we spend to determine what data are available, which types are beneficial and which types are not necessary, and how to present the insights into an easy-to-understand format.
The concept of "data wrangling" first appeared in the 1990s and remained relatively unused until 2001, when CNN used the concept of "data wranglers". These were used to find information for news articles.
What is data wrangling?
Since the early 2000s, data wrangling has been increasingly used with a broader meaning, such as transforming data formats, statistical model training, and other activities related to data preparation. It is now defined as the process of cleaning, restructuring, and enriching data to make the information more useful, easy to analyze and consume. Data wrangling also refers to combining large datasets into searchable, indexed sets.
One popular use of data wrangling is when you extract information from the internet. Once you collect the data, wrangling comes as the next step, aimed at cleaning the complex datasets and turning them into actionable information presented in a user-friendly format. There are different actions involved in data wrangling, such as:
● Identifying and deleting unnecessary data;
● Correcting missing or irrelevant data entries;
● Merging data from different sources into one set.
Brief understanding of data wrangling software
Data wrangling can be manual or automated using a program. Companies that deal with impressive amounts of raw data may find manual data wrangling extremely expensive and time-consuming. This is why data wrangling software is widely accepted as the most efficient way - it does not only automate the process, but such software aims to eliminate human error, ensuring accurate and insightful analyses.
Undoubtedly, data wrangling software is a valuable component of accurate data processes. The main reasons behind using such programs include:
● Turning raw data into usable, insightful analysis;
● Accurate data-wrangling guarantees data quality and consistency;
● Easily combining raw data from different sources into a single format;
● Automated data-wrangling instantly cleans and converts data into a standard format that can be used by business professionals;
● Automatic data cleaning, eliminating errors, missing entries, and unnecessary information;
● Data wrangling software saves time, allowing businesses to make timely decisions.
Benefits of data wrangling software
Data wrangling has numerous benefits. First, it improves data usability as it turns raw data into a standard format. Second, it allows you to integrate many types of information into one set ready for analysis - such as databases, files, and web-scraped data. Data wrangling software then enables you to analyze large volumes of data and share the information for quick decision-making.
Use cases of data wrangling
Data wrangling is an important process for any business, regardless of its industry or activity. If your company collects online data, you can use software to curate it and organize it in a time-efficient manner.
For example, any business may collect competitors' prices for comparison. This data can be processed using data wrangling software, turning it into a large dataset that helps you correctly price your own products and services. This is also useful if you have a flexible pricing strategy or you want to act quickly based on market conditions or competitors' strategies.
Conclusion
If your company is not able to extract value from its data, it may easily fall behind competitors. Successful decision-making processes must be based on enriched, useful, and easy-to-use data, and data wrangling software helps you structure, clean, and reorganize your data automatically. This does not only help you save time, but also decrease your costs and remain competitive.
* This is a contributed article and this content does not necessarily represent the views of sciencetimes.com
![Earth's Quasi-Moon Kamo‘oalewa Could Originate From Lunar Surface Not Asteroid Belt [Study]](https://1721181113.rsc.cdn77.org/data/thumbs/full/53275/258/146/50/40/earths-quasi-moon-kamo-oalewa-could-originate-from-lunar-surface-not-asteroid-belt-study.png)
