How can you interpret a spreadsheet of transactions or entries peppered with typing errors or empty entries? How can you extract any useful data from dozens of emails? Even worse, what can you do with lots of files you store in your cloud? Business competition is more avid than ever, and each piece of data could make the difference between profitability and a declining market share. The answer is data preparation and data munging – two sets of concepts that turn your unusable data into high-quality, clear, and correct data ready for analysis. 

What Is Data Munging?

Data munging, also known as data wrangling, refers to different methodologies that consist of transforming raw data into a format that can be easily understood. The purpose of data munging is to create appropriate datasets that bring value to your company. Businesses need to prepare data in order to turn it into information that can be analyzed. 

Data munging is sometimes used as a synonym for “data wrangling”. The first concept refers to the initial process of preparing data that can be then consumed by users or systems. Wrangling refers to a process that contains more steps, such as cleaning, enriching, and transforming the data into the final datasets. You can also find out more about data munging.

Initially associated with software engineers, the two terms are now more generic and broadly used in the internet age. In simple terms, data munging and data wrangling can be described as the initial collection of data and preparation. 

Why is data preparation important?

Businesses need to prepare data for several reasons. When collecting real-world information, this is often unstructured and contains several unwanted components. For instance, many datasets collected by your organization via different channels may contain missing data – this could be the cause of a technical problem or mistakes in data entries. 

Next, data preparation helps us identify “noise”, or erroneous entries. These could be the result of human mistakes during the data entry process or a technical/technological problem if data is collected by devices (i.e., mobile phones). 

Finally, you need to prepare data to ensure consistency. This could mean that raw information may contain mistakes in names or codes (if applicable), duplication, and others.  

How to prepare data for data munging

When we collect data with the purpose of analyzing it – especially as machine-learning methods and deep learning are becoming more available, data comes from files, sensors, databases, and other sources of alternative data. These cannot be directly interpreted or analyzed as they come in different formats, may contain mistakes, or contain sampling biases. To ensure data quality, consistency, and usability, you need to prepare data for munging. 

Data preparation is conducted to turn the real-world data into usable information that can be used to extract insights and make educated business decisions. The next sections discuss the most common issues arising during data preparation methods and how you can solve them. 

Identify and Solve Missing Values

Most real-world datasets will contain missing values. To some extent and depending on the type of data you use, you could simply ignore the missing record. This is the most time-saving method to handle this problem; however, if your dataset is missing a large portion of entries, ignoring the missing values means that you do not have enough data to analyze. 

One option, in this case, is to file all your missing values manually. In turn, this is time-consuming, especially if the number of missing values is large. Alternatively, you can fill in your dataset using computed values – for instance, depending on your dataset and purposes, you can calculate the mean, median, or mode of your existing values and fill in the missing ones automatically. This could smoothen some or most of the insights in your data, an issue that can also be solved by using machine-learning tools and algorithms to predict values. 

Noisy Data

All real-world datasets are expected to contain noise. There are different methods of eliminating noise when you prepare data:

  • Clustering the data (grouping it by similar characteristics) helps you detect outliers and noise
  • Using algorithms to smoothen data (i.e., regression algorithms)
  • Manual removal, although it is the most time-consuming and requires knowledge of engineering tools and the ability to use external references to prepare data. 

Removing Inconsistencies

For a successful data preparation process, all your data must be consistent. In other words, you need to transform all of your data into one format once it is cleansed. At this stage, you may notice that not all of your information is useful or needed, so you can remove it from your dataset. This process can also be done automatically, especially if your data preparation needs are continuous (i.e., preparing sales or credit card transaction data each month). 

Five Tips for Data Preparation

Apart from the steps discussed above, there are a few other things you can keep in mind to ensure that you can prepare data with ease:

  1. Understand the purpose of your data (i.e., how you will use these data and what questions it needs to answer);
  2. Always save the raw data and keep it, even after processing, in case you need to retrieve it in the future; 
  3. Ensure that your data is consistent (i.e., your final data will produce the same results if analyzed multiple times);
  4. If possible, use machine-learning methods, software, and other tools to automate these processes and lower the chance of errors;
  5. Ensure your data management and storage are well-protected and compliant with current legislation. 


Data preparation is not an easy task, especially when we consider that the quality of your decisions will be only as good as your data. In most cases, data preparation will take the most time, but data munging is a crucial step to ensure the performance of your analysis model. 

Also Read: What Is A Snowflake Data Warehouse? 5 Benefits To Your Business