• Blog
  • August 24, 2022

The purpose of Data transformation

The purpose of Data transformation
The purpose of Data transformation
  • Blog
  • August 24, 2022

The purpose of Data transformation

Data is the most powerful avatar today. It is used by most people in businesses and industries to analyze, store and retrieve information. Data transformation, on other hand, is a process through which data can be changed according to a user’s need without changing its format or content. But little do many people understand what actually data transformation is about and how to approach it for effective delivery.

The key role of Data Transformation

Data transformation is frequently needed for applications of data processing. Data transformations are the processes of transferring one form of data into another form and converting data from one format to another format. There are various methods involved in data transformation, such as- raw data filtering, normalization, reformatting, relabelling, etc. These processes help improve performance, reduce costs and make reporting simpler to do.

The purpose of data transformation is to improve the quality and usefulness of data by removing noise and inconsistencies. It involves activities such as:

  • Joining tables and various datasets to perform analysis
  • Removing duplicate columns, rows with missing or null values
  • Converting units for measures, categorical variables into quantitative variables
  • Generating new attributes
  • Identifying the source of data
  • Cleaning the data
  • Modifying the structure of the dataset
  • Transforming the values in a given column
  • Storing transformed data as a new dataset or overwriting the existing dataset

These activities allow businesses to create better features for their machine learning models and also simplify the data so that it is easier to work with and analyse them.

Types of Data Transformations-

Depending upon the complexity of data and methods used to perform the transformation, the types are categorized into four segments. These include Simple, Complex, Manual, and Automated segments.

1. Simple Transformations-

Simple transformations are generally those that don’t require coding or scripting to perform. For example, some common simple transformations include:

  • Concatenation: Joining multiple fields into a single field
  • Splitting: Dividing a field into multiple fields
  • Sorting and Ranking: Sorting records in ascending or descending order and ranking them based on criteria like values in fields or aggregate calculations
  • Filtering Duplicates: Removing duplicate records from a dataset
  • Changing Data Types: The format of data types can also be changed using functions.
  • Handling Missing Values: Missing values in a dataset must be handled properly before any analysis can be done. There are two ways to handle missing values- deleting them, or filling them with some value (0, mean etc). 

2. Complex Transformations-

Data that require complex transformations usually have inconsistent formats and types, such as free-form text fields. Free-form text fields can contain any type of information but are difficult for analytics applications to analyse. For example, an email field could also contain phone numbers or other information. To solve this problem, the data must be cleaned and parsed into a format structured so that it will work with the target application. To clean up this type of data, you will need to set up business rules to define exactly how each field should look when it is standardized. Using these rules, you can then create a program to parse the information appropriately and create a consistent output file. 

3. Manual Transformations-

Manual transformations are carried out by domain experts or analysts who possess domain knowledge and organizational skills. These transformations do not require any technical expertise, but they need time and effort, which can increase the overall project costs. Manual transformations are usually done by data analysts or scientists working with a small batch of data for a particular business goal. A good example would be an analyst working on expense reports for the month. The analyst will use Microsoft Excel to manually clean up and organize the expense report data into a worksheet so they can generate graphs and charts that show how much was spent on gas, food, etc by month. 

4. Automated Transformations-

Automated transformations are used when simple manipulations need to be performed on large quantities of data without having to write many lines of code. The automated transformation is often used when there is a large amount of data (such as millions or billions of rows) that needs to be transformed on a regular basis such as daily, weekly or monthly updates to an existing database table.

Common Methods used in Data Transformation-


The process of transforming data from raw to cleaned and structured can be done by using several methods. The most common ones are:

1. Filtering:

Filtering is used to remove the unwanted values from a dataset. For example, filtering out rare categorical values that do not occur frequently enough in the dataset to be useful for predictive models.

2. Aggregation:

Aggregation refers to the grouping of variables. It helps to keep similar data together and make it easier to visualize and analyse such groups. For example, a company might want to group employees into departments based on their job functions.

3. Normalization:

Normalization is used to transform values in multiple columns so that they can be compared with each other. For example, normalizing all sales records so that they can be compared across stores, regions or countries.

4. Generalization:

Generalization is the process of representing an entity in a more summarized manner. Data is represented in higher-level classes or groupings to reduce its granularity. However, the information loss resulting from generalization is minimal as compared to other data reduction techniques.

5. Discretization:

Discretization is a technique used for converting continuous attributes into nominal (discrete) attributes. It involves partitioning a continuous range of values into intervals and then replacing the original values with interval labels.

6. Manipulation:

This technique is used when we want to change the content of certain attributes in existing tuples.

7. Integration:

It combines data residing in different sources and provides users with a unified view of these data. The sources can be relational databases, spreadsheets, flat files and so on.

8. Binning:

Binning is used to reduce the number of bins by grouping observations together based on their numerical value ranges.

ETL tools for transforming data-

ETL (Extract, Transform and Load) is a type of data integration that involves extracting data from outside sources, transforming it to fit operational needs, then loading it into end target databases. ETL tools are usually used in data warehouses and other business intelligence platforms. There are basically 3 types of tools available for ETL: Scripting tools, On-premises ETL tools and cloud based ETL tools.

  • Scripting Tools: This is the most inexpensive option. With scripting you can use any programming language like python which has good libraries for data manipulation. Use of scripting also helps you to build complex logic in your transformation process. The main downside of using scripting is the time involved in writing and maintaining the code.
  • On-premises ETL Tools: These tools provide a GUI which help you to manipulate data easily without writing much code but it requires a lot of hardware maintenance and configuration to run these applications.
  • Cloud based ETL Tools: These are cloud subscription-based services which run on cloud infrastructure like amazon web services, google cloud platform, etc. So, there is no need for hardware maintenance as everything will be handled by the company providing these services.

Benefits of Transforming Data-

Data scientists often spend a majority of their time preparing data for analysis. After all, it is hard to find insights in raw data, that is hard to understand and isn’t organized. However, once the data is in the right format and shape, it can be analysed and used to make business decisions. Let us look at some benefits of data transformation:

  • Easier to work with: With data transformation, you get your data into a format that is easier to work with. It can be as simple as applying a filter on a table or as complicated as combining multiple tables together into one.
  • Faster analysis: When your data is clean and all the columns and rows are properly labelled, analysis becomes easier and faster because you don’t need to spend time figuring out what the various fields mean.
  • Better understanding: Data transformation helps you get a better understanding of your data so you know exactly what information is available for analysis. You also get a sense of the quality of your data – whether it has missing values or outliers which may impact your results later on during modeling. 
  • Faster queries: Data transformation also helps perform faster queries on structured and unstructured data. This way you can easily access records for reports, dashboards, or other important functions within your business. 
  • Improved data quality: It is another benefit that comes with data transformation. When source systems are integrated into one platform, the single version of truth becomes available for easier collaboration between multiple parties.
  • More accurate models: Data transformation leads to deployment of better machine learning models for accurate data science predictions and better decision-making.

As you can see above, there are a number of different sources from which businesses can extract and gather data for transformation, many methods and steps involved and numerous benefits of data transformation. Factors such as how current their business needs are, the type of connections these businesses have with one another, the size that these businesses have, the kind of information they wish to access to extract data and other factors could determine the method through which they might choose to extract and transform data.

Since technology is constantly expanding and the pace of change is increasing, businesses are handling more data than before. This data comes in different formats, large amounts, and are collected from various sources. So, it is quite difficult to process it and view it on a large scale. Data Transformation helps in converting data from one form to a different format to fulfil business requirements. Thus, data transformation platform has risen to prominence among organizations in a short span of time.