Data Transformation - Function, Methods, Benefits and Software

Jan Schäfer

In 2018, the amount of data generated by companies was already around 33 zettabytes (ZB). For orientation: One zettabyte corresponds to one billion terabytes. The International Data Corporation assumes that companies will will generate 175 ZB in 2025. This means that they generate around 27 percent more data every year. These data volumes pose a challenge for companies. The raw data can be confusing, inconsistent and difficult to understand. This is where Data Transformation comes into play. It makes it possible to transform and restructure data in such a way that it can be easily analyzed and interpreted.

We show you which methods and techniques a data transformation uses so that companies can make sound business decisions based on their data. We also explain the challenges you face in data transformation and which software you can use to tackle them efficiently.

Data Transformation - Definition

Data Transformation is a key process in data processing that involves transforming raw data into an optimized form. This is done through a series of measures aimed at making the data more accessible for analysis, visualization, and other processing steps.

Data Transformation includes various steps depending on your needs. These include, for example, cleaning up erroneous or missing values, converting data types, aggregating data points, grouping data according to certain criteria and adding new calculated variables. In addition, data can be sorted, filtered and duplicated entries removed.

Data transformation serves to increase the quality of the data, eliminate inconsistencies and adapt the data structure to your own requirements.

It enables meaningful patterns, trends and insights to be gleaned from data that are not usually visible in their raw form. Data transformation is thus an essential step in extracting valuable information from raw data and making data-based decisions.

Data Transformation vs. Data Pipeline

Data Transformation refers to the transformation of data to change its format, structure or value. This is done to make data more suitable for analysis or processing. Data pipelines, on the other hand, are processes that transport data from a source to the target storage and include steps such as data transformation, filtering, and aggregation. While Data Transformation focuses on the data itself, Data Pipelines focus on the smooth flow of data between different stations. Data Transformation is thus a component of Data Pipelines, which orchestrate the entire process of data movement, processing and storage.

Data Transformation - Methods and Techniques 

To change the format, structure or value of data, Data Transformation uses these methods and techniques:

Revision (Revising)

The revision ensures that the data supports its intended use. To do this, companies must organize the data in a necessary and correct manner. To do this, they first normalize the data set. That is: They eliminate redundancies in the data set, making the data model more accurate and readable while taking up less space. This process requires a lot of critical thinking, investigation and reverse engineering.

They then use data cleansing to ensure that the data can be formatted. This also means that they ensure consistency and correctness of the data. With format conversion, they change the data types to ensure compatibility. Last but not least, companies remove duplicates in the data records and validate the data.


Manipulation involves creating new values from existing ones or changing existing data through calculations. Manipulation is also used to turn unstructured data into structured data that can be used by machine learning algorithms. There are these manifestations of data manipulation:

Derivation: These are calculations across multiple columns.

Summary (Summarization): This technique aggregates values.

Pivoting: This converts column values to rows and vice versa.

Sort, arrange and index: Data is sorted, ordered and indexed to improve search performance.

Scaling, normalization and standardization: These techniques help compare disparate numbers by putting them on a consistent scale.

Vectorization: This technique helps to convert non-numeric data into number arrays, which are often used for applications in machine learning and similar technology.


Separating breaks data values into multiple parts for detailed analysis. This includes splitting a single column with multiple values into separate columns with each of those values. This allows filtering based on specific values.

Combining/Integrating (Combining/Integrating)

This method of Data Transformation combines data sets from different tables and sources to provide a more comprehensive view of an organization's activities and functions. It combines data from multiple tables and data sets.

Data Smoothing (Data Smoothing)

Data Smoothing removes meaningless, inaccurate or biased data from the dataset. This way, the data is more consistent, making it easier for companies to identify trends.

Data aggregation

This Data Transformation technique collects raw data from multiple sources and combines it into a unified form that can be used for analysis. An example of this is raw data that provides statistics such as averages and totals.


Using this technique, companies create interval labels in continuous data to improve its efficiency and easier analysis. Decision tree algorithms are used to transform large data sets into categorical data.


Low-level data attributes are converted to high-level attributes using hierarchies to create clearer data snapshots.

Attribute Construction

This Data Transformation technique creates a new set of attributes from an existing set to facilitate the analysis process.

Data Transformation Process - Here's how 

In order to transform data into a new form and thus make it usable for analyses, the Data Transformation Process takes place in the following steps: 

  1. Data identification

    This first step is about identifying the data sources and types. Here, data experts use data profiling tools or scripts to understand the structure and characteristics of the data. This also determines how the data should be transformed. The data can come from various sources such as structured sources, streaming sources or log files from web applications. In this phase, the experts analyze the data to gain a better understanding of its structure and meaning.

  2. Data mapping

    During data mapping, data experts link data fields from one source to data fields in another source. This mapping is critical to ensure that the data can be correctly transformed and put into the desired structure. Data experts determine which data fields should match and how they should be handled during the transformation process.

  3. Code generation

    This step generates the software code needed to process the data according to the transformation requirements. This code can either be generated automatically by data transformation tools or written by the data experts themselves. The generated code contains instructions to transform the data according to the specifications from the previous steps.

  4. Code execution

    During the execution of the code, the actual data transformations are performed. This step involves applying the previously generated code to the data to get it into the desired form. This is where the actual manipulation of the data takes place according to the specified rules and transformations.

  5. Review

    After the data has been transformed, verification takes place. Data experts or business/end users check whether the output data meets the specified transformation requirements. If not, anomalies and errors are detected and corrected. This verification is important to ensure that the transformed data is accurate and of high quality.

data transformation benefits

Data Transformation - Benefits

Companies benefit from data transformation in this way:

Improved data quality

Data Transformation enables companies to transform unstructured or erroneous data into structured and useful information. This helps to avoid errors and misinterpretations.

Example: A retailer collects sales data in which some product codes have been entered incorrectly. Automated Data Transformation corrects these codes and formats them consistently, enabling accurate analysis and reporting.

Data integration

Companies can harmonize and combine different data sets from different sources. In this way, they create a unified view of the data. This enables more comprehensive insights.

Example: An insurance company has customer data in several departments. With Data Transformation, this data from the departments can be merged to create a 360-degree customer profile.

Advanced analysis capabilities

Data Transformation prepares data so that it can be used in advanced analytics techniques such as machine learning, resulting in more accurate predictions and better insights.

Example: A healthcare company uses Data Transformation to prepare patient data for machine learning. In this way, it can develop a model that predicts the risk of certain diseases for different age groups.

Efficient data processing

By aggregating, filtering, and reducing data volumes using Data Transformation, companies can reduce processing time and resource utilization.

Example: A logistics company collects large amounts of location data from vehicles. Data transformation is used to select only relevant data such as stops and breaks in order to perform route optimization.

Adaptation to new requirements

Companies can adapt to changing requirements by transforming data into the required format or structure.

Example: A bank needs to produce reports for regulatory authorities that require specific data formats. Data Transformation can be used to convert the internal data into the required format.

Better decision making

Well-transformed data enables informed decisions based on reliable information, leading to better overall business performance.

Example: An e-commerce company analyzes purchasing behavior in order to optimize product offerings. Data transformation processes the data in such a way that clear trends in purchasing decisions become visible.

Fewer risks

By consolidating, cleansing and structuring data, companies can minimize errors and uncertainties, resulting in less risk in business decisions.

Example: A pharmaceutical company conducts clinical trials. Data transformation is used to format the study data in a uniform manner. In this way, the company reduces the error rate during analysis and generates more reliable results.

Lower costs

Data Transformation enables efficient processing of data, resulting in reduced resources and labor time.

Example: A utility company processes billing data. Data transformation allows the data to be aggregated more quickly, reducing the cost of data processing.

Competitive advantage 

Well-transformed data allows companies to respond faster to market trends and develop innovative solutions. This gives them a competitive advantage.

Example: A retailer analyzes sales data to identify trends in real time. Thanks to Data Transformation, the company can respond quickly to changing customer preferences and adapt its product offerings.

Data Transformation - Challenges

The benefits of a comprehensive data transformation are therefore obvious. So why aren't all companies using the process? This quickly becomes clear when we take a look at the challenges of data transformation:

Data quality

Companies face the challenge of ensuring that the data they use is of high quality. This means they must be free of errors, inconsistencies and incompleteness. As a rule, this is not possible without software. Here's a real-world example: a retailer analyzes sales data to identify trends. If the data contains inaccurate or missing entries, the derived insights can be misleading.

Data integration

Often, data comes from different sources and in different formats. The challenge is to combine these different data sources to get a comprehensive picture. A practical example: A financial company wants to have a 360-degree view of its customers. This requires bringing together data from different departments, such as sales, marketing and customer service.

Data security

During data transformation, companies need to ensure that sensitive information is protected. For example: A medical research company processes patient data for clinical trials. Here, protecting patient identity and other confidential information is of paramount importance.


The ability to analyze data and turn it into business-relevant insights requires expertise. Example: A marketing team wants to evaluate the effectiveness of its campaigns. To do this, team members must be able to interpret data to determine which strategies have been successful.

Change in corporate culture

Companies may need to change their culture to encourage data-driven decisions. Example: A traditional manufacturing company wants to become more agile. It needs to change the culture to make decisions based more on data and analytics, rather than relying solely on experience.

Technology selection

Choosing the right data transformation software is critical. Example: An e-commerce company wants to develop personalized recommendations for customers. To do this, it must select the right data analytics platform.


During transformation, companies need to ensure that their infrastructure can handle growing volumes of data. Example: A social network is experiencing a rapid increase in new users. The database must be able to handle the increasing amount of user data.

Cost management

The changeover to data-driven decisions and processes can be cost-intensive. Example: A logistics company invests in IoT sensors to collect real-time data from deliveries. Here, the cost of technology and employee training must be considered.

Temporal aspects

Converting raw data into actionable insights usually takes a lot of time. Example: An online marketplace wants to understand customer buying behavior in order to adjust product offerings. Delays in data processing could affect the ability to adapt quickly.

Data Transformation - Practical Areas of Application

Data transformation is a critical process in many industries for turning raw data into actionable information and insights. Here are some real-world examples for the insurance, banking and public sector industries:


  • Risk Assessment: Transform customer data (age, gender, medical history, etc.) into a standardized risk profile to set insurance premiums.
  • Damage analyses: Claims data can be transformed to identify patterns and trends that can be used to predict future claims or detect fraud.
  • Customer interaction: Conversion of customer data from different channels (e.g., email, phone, online) into a consistent format to provide a 360-degree view of the customer.


  • Credit scoring: Converting data on income, expenses, previous loans, and payment history into a credit score that is used to make decisions about loan applications.
  • Trade Data Analysis: Transform raw data on stock market transactions into structured formats to perform market analysis or develop trading strategies.
  • Money Laundering Detection: Aggregate and transform transaction data to identify noticeable patterns that may indicate potential money laundering activity.

Public Sector

  • Urban Planning: Transforming transportation, population, and infrastructure data into models to plan urban development.
  • Social Services: Transform data on individuals receiving social services to better understand needs and allocate resources more efficiently.
  • Tax Data Processing: Collect and transform financial data from citizens and businesses to verify tax returns and calculate tax liability.

These examples illustrate that Data Transformation is a critical step in any industry to gain useful, actionable insights from a flood of data. It helps organizations not only to better understand their current situation, but also to predict future trends and developments.

With respect to the previously mentioned applications in the insurance and banking industries as well as the public sector, these applications could be classified into the following sub-areas of Data Transformation:


  • Risk assessment
    • Normalization of the data set: removal of redundant information
    • Data cleansing: ensuring consistent data
    • Data validation: Verification of the integrity of customer data
  • Damage analyses
    • Data aggregation: combination of data from different damage cases for pattern recognition
    • Data smoothing: remove irregular data for better trend analysis
  • Customer interaction
    • Combine/Integrate: Combining customer data from different channels
    • Data cleansing: ensuring consistency of data from different sources


  • Credit check
    • Normalization of the data set: conversion of financial data into standardized formats
    • Data validation: checking credit data for completeness
  • Trade Data Analysis
    • Data aggregation: combining data across multiple exchange transactions
    • Sort, arrange and index: Optimize data for fast analysis
  • Money Laundering Detection
    • Deduplication: identification and removal of duplicates
    • Data cleansing: ensuring the accuracy and consistency of transaction data

Public Sector

  • Urban planning
    • Combine/Integrate: Fusion of data from different city departments
    • Data aggregation: Combining data for holistic analysis
  • Social services
    • Data smoothing: removing outliers and anomalies from the data
    • Data aggregation: combining data from different service providers or regions
  • Tax data processing
    • Format conversion: customize financial data types for tax calculation
    • Data cleansing: Removal of incorrect or inconsistent entries

This classification serves only as a rough guideline as to how the applications described could fit into the methods and techniques of Data Transformation. Depending on the specific use case and context, the classifications may of course differ.

data transformation use cases

Data Transformation - Use Cases

To better understand the use of a Data Transformation and the resulting benefits in practice, let's take a look at classic use cases:

1. Banks: Optimization of the lending process

A classic use case in the banking sector looks like this:


A major bank faces the challenge of speeding up the process of granting loans. At the same time, it wants to ensure that it makes high-quality lending decisions.

Solution through Data Transformation

The bank collects data from various sources: Credit history, current financial situation of the applicant, work history and other relevant parameters. This data can be in different formats and needs a unified structure for efficient processing. A Data Transformation brings this heterogeneous data into a unified format that can be easily processed by credit scoring algorithms.


By using Data Transformation, the bank can significantly speed up the loan origination process and improve the accuracy of its decisions, while increasing customer satisfaction.

2. Insurance: Fraud detection

A classic use case in the insurance industry looks like this:


An insurance company wants to optimize its claims handling process and detect fraud at an early stage.

Solution through Data Transformation

Among other things, the company collects data on accidents, policies and the history of customer claims. As part of a data transformation, it processes the data and stores it in a centralized system equipped with fraud detection algorithms.


With the help of Data Transformation, the insurance company can identify patterns and anomalies in claims that indicate potential fraud. In this way, it can save a lot of money on bad claims.

3. Public Sector: Urban planning and traffic management

A classic use case in the public sector looks like this:


A major city wants to optimize traffic flow while planning urban development.

Solution through Data Transformation

The city collects data from traffic sensors, public transportation, citizen feedback, and urban planning projects. After transforming the data, it feeds it into an intelligent traffic management system that analyzes traffic flow in real time and makes predictions for urban planning decisions.


Through data transformation, the city can monitor traffic flow in real time, prevent congestion, and make informed decisions about future urban design projects.

Data Transformation as an Advantage or Part of a Process?

Data transformation on its own offers many benefits, especially when companies bring data from different sources into a consistent format for analysis or reporting. However, it usually realizes its full potential within a broader process - be it data analytics, machine learning or other business applications. Within such processes, Data Transformation enables the seamless integration and analysis of data. This ultimately leads to better decisions, optimization and innovation.

Data Transformation Tools: 5 established Providers 

To overcome the challenges associated with data transformation, companies must first and foremost choose the right data transformation software. This has a huge impact on how much work the transformation and preparation of their data actually entails. Here are 5 Data Transformation Services that take care of large parts of data processing in an automated way:


data transformation provider Talend

Talend is a widely used open source ETL tool. Customers can use it to capture data from various sources, transform it and convert it into different formats. Talend has a user-friendly graphical interface and supports even complex transformations through the use of scripts and custom code.


data transformation provider Konfuzio

Konfuzio is a leading provider of AI-powered document processing and transformation. The platform uses advanced machine learning algorithms to extract and transform information from unstructured documents. This is particularly useful for organizations that want to process and analyze large volumes of unstructured data such as from texts, reports and documents. Konfuzio enables the automation of all data capture and transformation steps. In this way, companies save a lot of time and resources.


data transformation provider Informatica

Informatica provides capabilities such as data quality validation, transformation, and migration. The Data Transformation Platform enables users to extract, cleanse, transform, and load data from various sources into different target formats. It also has advanced features such as data masking and anonymization for data security.

Microsoft Azure Data Factory


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Azure Data Factory is a cloud-based service from Microsoft that enables data extraction, transformation and loading. It integrates seamlessly with other Azure services and provides powerful data processing capabilities. Users can create data pipelines that extract, transform, and load data from multiple sources to multiple destinations. The platform also provides monitoring, logging and scalability for complex data transformation scenarios.

Apache NiFi

data transformation provider Apache NiFi

Apache NiFi is a data transformation open source platform that enables data integration, transformation and movement. It provides a user-friendly, web-based interface to create data flows that captures and transforms data from multiple sources. NiFi also supports real-time data processing and provides a variety of processors for data manipulation.


What is Data Transformation?

Data Transformation is the process of transforming and preparing data to make it suitable for analysis, processing and reporting. This includes changing data types, filtering, merging data from different sources, and rearranging columns or rows. Data Transformation is critical for digital transformation to transform raw data into a useful form that enables better insights and decision making.

How can the data transformation process benefit from machine learning?

Machine learning plays a central role in data transformation. It accelerates the process by automatically recognizing patterns in the data. This capability allows it to efficiently transform and optimize data. This leads to faster and more accurate results. In addition, Machine Learning enables informed decisions by automatically gaining valuable insights from the transformed data.

What data transformation software is available on the market?

Companies can choose from a variety of data transformation tools. The provider Konfuzio makes data processing particularly efficient. It uses artificial intelligence and machine learning so that companies can automatically extract and transform their data according to their own specifications and pass it on to subsequent workflows. In this way, the software solves important challenges of data transformation, such as ensuring data quality.

Would you like to learn more about efficient data processing with AI? Then talk to us now with one of our experts!

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