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Data Wrangling

It is no secret that many businesses rely on public data to make various strategic decisions. The challenge is, however, extracting valuable insights from them. Typically, public data collected by businesses is unprocessed. When this is the case, data wrangling becomes necessary.

To properly evaluate data, a certain kind of structure is necessary. For this to work, it is imperative to know what types of data are required by what processes. Let’s take a deeper look at data wrangling and explain why it is so crucial.

What Is Data Wrangling?

The act of cleansing and integrating chaotic and complicated data sets for easy access and analysis is known as data wrangling. Since the amount of data and sources is increasing and developing continuously, it has become more and more essential to organize vast amounts of data for analysis. 

This procedure generally entails manually translating and mapping data from one raw form to another to facilitate data consumption and organization. According to Forbes, data experts spend around 80% of their time preparing and maintaining data for analysis.

Excel spreadsheets are the most fundamental structuring tool used by data analysts for data wrangling. Of course, more advanced tools exist, such as OpenRefine or Tabula. For data wrangling, data analysts often utilize the open-source programming languages R and Python. These languages, in particular, provide useful open-source libraries for the data munging process.

Data Wrangling’s Objectives

It is critical to realize that if the essential data is missing or inaccurate, the subsequent data analysis process might get muddled. It simply implies that all of the insights might be incorrect, costing firms time and money. Data wrangling mitigates this risk by ensuring that information is in a trustworthy state.

Here are some of the most important goals of data wrangling.

  • Gather data from many sources to provide more in-depth intelligence.
  • Deliver reliable, actionable data to business analysts on time.
  • Reduce the effort required to gather, organize, and make use of disorderly data.
  • Allow data scientists and analysts to concentrate on data analysis rather than data wrangling.
  • Improve the decision-making abilities of an organization’s senior leaders.

Stages of Data Wrangling

Each project requires a distinct strategy to ensure that the final data is reliable and accessible. In general, however, several steps are involved. These are typically known as data wrangling stages or activities.

Data wrangling can be time-consuming when done manually. Companies generally develop best practices to assist data analysts in simplifying the entire process. Because of this, it is crucial to understand the phases of the data wrangling process since it helps determine which aspects can be improved.


Discovering how to use data is the process of becoming familiar with it so that you can see how you can benefit from it. It is analogous to checking your pantry before making dinner to see what ingredients you have on hand. During the discovery process, there may appear to be errors in the data, such as missing or incomplete numbers, that need to be corrected. These errors will affect all subsequent activities.


In its raw form, data is usually useless because it is either missing information or incorrectly structured. Data structuring aims to make data more readily accessible. Your analytical model will determine the format of your data.


Data cleaning is the act of eliminating inherent flaws in data that may distort or diminish the value of your study. There are several types of cleaning, including deleting empty cells or rows and removing outliers. Data cleaning is the process of making sure your final analysis is error-free or at least reduced to the bare minimum.


As soon as you have analyzed your current data and turned it into something more usable, you must evaluate if you have all the data you need for the project. You can augment or expand your data by including values from additional databases. As a result, it is critical to determine what other data is accessible for usage. If you determine that the data requires enrichment, you must repeat the preceding procedures for any new data.


Data validation is the process of ensuring that your data is both consistent and of sufficient quality. It is possible to discover flaws during validation or to determine that your data is ready for analysis. You can automate the validation process, which, in general, requires programming.


Once you have verified your data, you can post it. You should make it available for review by others in your organization. Your data and the business’s aims determine how you offer the information, such as a printed report or an electronic file.

Data Mining

Data mining is the process of sifting through data to uncover anything valuable. To be more specific, mining is the catalyst for the idea of “working smarter, not harder.” On a lesser scale, mining is any process that involves collecting data in one place.

With data being the fuel in today’s digital environment, site scraping is becoming increasingly important. However, the growing usage of online scraping has resulted in websites employing scrape detection technologies. The role of proxy servers here is crucial.

Proxy servers, in a nutshell, enable you to scrape the web safely and secretly. Web scraping is legal. However, it may place a load on the target website. Websites rely on scraping detection technologies to prevent this accumulation of requests. You can evade these detection methods by using a proxy.


Data will ultimately limit any analysis that a company may do. A lack of adequate, inaccurate, or erroneous data will compromise any analysis, which will reduce the use of any results. As a result, it is critical to grasp the phases of the data wrangling process as well as the negative consequences of inaccurate or defective data.

You must use data wrangling to structure the information for your project or business. However, before that, you should use a scraper API or a proxy to collect the data.