Data Preprocessing: Definition, Benefits, and Stages of Its Work

Data is an important thing for companies to help determine business plans, develop business directions, and understand business targets. However, both data obtained directly and obtained from the internet, cannot be directly processed and processed using a computer. But there is a process called data preprocessing which functions to convert raw data into a form that is easier to understand.

This work process can actually be found in every company that uses large amounts of data. It will simplify the process of data mining which is the process of collecting and processing data to extract important information in it.

In order to better understand it, the following article will explain more about what data preprocessing is, along with its benefits and working steps.

What is Data Preprocessing?

Data preprocessing is the process of converting raw data into a form that is easier to understand. This process is necessary to correct errors in raw data which is often incomplete and has an irregular format.

Preprocessing involves the validation and imputation of data. Validation aims to assess the level of completeness and accuracy of the filtered data. Meanwhile, imputation aims to correct errors and enter missing values, either manually or automatically through a business process automation (BPA) program.

Data quality does have a direct impact on the success of any project that involves data analysis. In machine learning, data preprocessing plays a role in ensuring that big data is formatted and the information in it can be understood by the company’s algorithm so that it can produce more accurate results.

Benefits of Data Preprocessing

Based on the above understanding, it can be understood that data preprocessing plays an important role in database. It can also be said that data preprocessing provides a number of benefits for projects or companies such as:

  1. Streamlining the data mining process
  2. Makes data easier to read
  3. Reduces the burden of representation in data
  4. Reduces data mining duration significantly
  5. Simplifies the process of data analysis in machine learning

Stages of Data Preprocessing Work

In order to run optimally, the data processing divided into four distinct stages, namely data cleaning, data integration, data transformation, and data reduction.

1. Data Cleaning

In the data cleaning, the raw data will be cleaned through several processes such as filling in missing values, smoothing noisy data, and resolving inconsistencies found.

Data can also be cleaned and organized using segments of similar size and then smoothed (binning), with a linear or multiple regression function (regression), or by grouping them into groups of similar data (grouping).

2. Data Integration

Data integration is a stage that combines data from various sources into a single data set (dataset). In the merging process, data with different formats must first be converted to the same format. Overall, this data integration process is aimed at unifying and making data smoother through the following efforts.

  • Ensure data has the same format and attributes
  • Remove unneeded attributes from all data sources
  • Detect conflicting data values

​​3. Data Transformation

At this stage, the data will be normalized and generalized. Data normalization was carried out to ensure that there was no redundant data, while data generalization was carried out to homogenize the data.

Data transformation allows you to change data structures, data formats, and data values ​​into a dataset that is suitable for the mining or the algorithm that has been designed.

There are at least five steps that can be taken in the data transformation process, namely:

  • Aggregation: The step to combine all data in a uniform format.
  • Normalization: Steps to convert data into a regular scale so that it can be compared more accurately.
  • Feature Selection: The step to determine which variables are most important for analysis, where these variables will also be used to train machine learning or artificial intelligence models.
  • Discreditization: Steps to collect data into smaller intervals. For example, when calculating your average daily workout, you can break it down into 0-15 minutes, 15-30 minutes, and so on, instead of using the minutes and seconds in detail.
  • Concept Hierarchy Generation: Steps to add a new hierarchy in the dataset.

4. Data Reduction

The last step that needs to be done is data reduction or reducing the amount of data. Data mining uses large amounts of data which is feared to cause the level of accuracy to be low. Therefore, the data sample needs to be reduced, but with due regard that the process will not change the results of data analysis.

There are three techniques that can be applied when reducing data, namely dimensionality reduction , numerosity reduction , and data compression (data compression). The three techniques can be adapted to the needs, such as whether the data being processed is large, medium, or needs to be compressed and poses a risk of harm.


Thus the discussion of data preprocessing which is an important process that facilitates the process of data analysis. This process will select data from various sources and uniform its format into a data set.

That way, businesses can get more accurate results and then process them into something that can help in determining business plans, developing business directions, and understanding business targets.

Businesses should also not forget about financial data related to income and expenses, all of which need to be recorded in as much detail and clarity as possible in the books. In this case, businesses can use business applications such as Accurate Online which will make the bookkeeping process faster, more accurate, and automated.