What is data transformation? Definition, types and benefits

Types of data transformation

There are numerous types of data transformation that can be applied as needed to meet specific business goals. Some of the most common data transformation techniques include the following:

• Integration. Integration unifies data elements from different data sets, such as combining two different databases. This ensures the indexes and values for every data element are the same, enabling easier, more accurate analytics.
• Deduplication. Deduplication removes redundant or duplicate data, such as duplicate customer entries, from the data set.
• Aggregation. Aggregation combines data in different ways to make it more manageable and easier to use. For example, daily data can be aggregated to represent weekly, monthly or quarterly averages.
• Normalization. Normalization guarantees that data elements use the same scale or format. For example, if one data set uses three digits of precision and another data set uses only two digits of precision, normalization requires that all data is processed to two or fewer digits of precision so that all data is represented the same way.
• Discretization. Discretization makes data more discrete, breaking it into finer or more detailed elements. This can make larger data sets easier to analyze.
• Feature engineering. Feature engineering, sometimes called data enrichment, creates new data elements or features from existing data elements and is frequently applied to enhance data used to train machine learning models.

ETL data transformation vs. ELT data transformation

Organizations with large, highly integrated data sets, often from multiple sources, must perform an enormous amount of data transformation to make data useful for business tasks. They use the following fundamental phases of this data pipeline:

• Extract uses data aggregation to collect raw data from disparate sources.
• Transform processes the data to produce accurate and integral data that can be combined.
• Load combines the transformed data in a database where it can be processed.

ETL data transformation employs this sequence of events, often applying detailed business rules to process data closer to its source before integrating it into a single set; in this case, more processing is performed upfront.

ELT data transformation holds off on the data transformation until the data has been combined. In effect, the raw data is collected and loaded first, and then the entire combined data set is transformed.

ELT is generally considered to be the default approach today. Combining raw data into a single data set can transform the data in various ways. This makes the ELT approach more flexible, allowing the raw database to be used in different business tasks by simply running different or customized transformations against the entire raw data set.

What are the key steps in data transformation?

The process of data transformation involves identifying data sources and types; determining the structure of transformations that need to occur; and defining how fields will be changed or aggregated. It includes extracting data from its original source, transforming it and sending it to the target destination, such as a database or data warehouse. Extractions can come from many locations, including structured sources, streaming sources or log files from web applications.

Data analysts, data engineers and data scientists are typically in charge of data transformation within an organization. They identify the source data, determine the required data formats and perform data mapping. They then execute the actual transformation process before moving the data into appropriate databases for storage and use.

Their work involves the following five main steps:

1. Data discovery. Data professionals use data profiling tools or profiling scripts to understand the data's structure and characteristics and determine how it should be transformed.
2. Data mapping. Data professionals connect or match data fields from one source to data fields in another.
3. Code generation. Data professionals use either data transformation tools or write script to create the software code required to transform the data.
4. Execution of the code. The data undergoes transformation.
5. Review. Data professionals or end users confirm that the output data meets the established transformation requirements. If not, they address and correct any anomalies and errors.

These steps fall within the ETL process for organizations that use on-premises warehouses. However, scalable cloud-based data warehouses have given rise to a slightly different process called ELT for extract, load, transform; in this process, organizations can load raw data into data warehouses and then transform data at the time of use.

What are the benefits and challenges of data transformation?

Organizations must analyze their data for various business operations, from customer service to supply chain management. They also need data to feed their enterprise's increasing number of automated and intelligent systems.

Benefits of data transformation

Organizations need high-quality data in formats compatible with the systems consuming it to gain insight into and improve these operations. Thus, data transformation is a critical component of an enterprise data program because it delivers the following benefits:

• Higher data quality.
• Reduced number of mistakes, such as missing values.
• Faster business queries and retrieval times.
• Less resources needed to manipulate data.
• Better data organization and management.
• More usable data, especially for advanced BI or analytics.

Challenges of data transformation

The data transformation process, however, can be complex and complicated. The challenges organizations face include the following:

• High cost of transformation tools and professional expertise.
• Significant compute resources, with the intensity of some on-premises transformation processes potentially slowing down other operations.
• Difficulty recruiting and retaining the skilled data professionals required for this work, with data professionals some of the most in-demand workers today.
• Difficulty of properly aligning data transformation activities to the business's data-related priorities and requirements.

Reasons to do data transformation

Organizations must be able to mine their data for insights to successfully compete in the digital marketplace, optimize operations, cut costs and boost productivity. They also require data to feed systems that use artificial intelligence, machine learning, natural language processing and other advanced technologies.

Data transformation has one simple goal -- to make data better and more useful for business tasks. When approached properly, a successful data transformation process can enhance various data attributes, including the following:

• Quality. Activities like removing errors, duplicates, missing entries or gaps and improper indexing or categorization can improve data accuracy and reliability for data analytics.
• Portability. Transformation uses formats and data types that make data consistent and more easily portable between disparate systems or platforms.
• Integration. Transformations can help integrate or combine different data sources into a single uniform data set.
• Usability. Proper transformations can make the data set more readable by humans and systems, making it easier to access, process and use for tasks such as analytics and visualization.
• Flexibility. Transformations can make data sets simpler and easier to scale or adapt to expanding use cases for the business.
• Security. Transformations can employ actions, such as data anonymization and encryption, to improve data security, data privacy and regulatory compliance issues for the business.

Data transformation tools

Data professionals have numerous tools to select from to support the ETL process. These technologies automate many of the steps within data transformation, replacing much, if not all, of the manual scripting and hand coding that was a major part of the process.

Both commercial and open source data transformation tools are available. Some offerings are designed for on-premises transformation processes and others for cloud-based transformation activities. Moreover, some data transformation tools focus on the process, handling the string of actions required to transform data. However, other ETL tools on the market are part of platforms that offer a broad range of capabilities for managing enterprise data. Examples of data transformation tools include the following:

1. Alteryx Transform.
2. Amazon Web Services Glue.
3. Apache Airflow.
4. Denodo Platform.
5. Google Cloud Dataform.
6. IBM InfoSphere DataStage.
7. Informatica Intelligent Data Platform.
8. Matillion.
9. Microsoft Azure Data Factory.
10. Oracle Cloud Infrastructure GoldenGate.
11. Pentaho Platform.
12. Qlik Replicate.
13. SAP Data Services.
14. SAS Data Management.
15. Talend Open Studio.

How to find the right data transformation tool for your organization

There are many data transformation tools available to assist organizations of all sizes and needs. But data transformation tools aren't all created equal, and each presents different features, functionalities, resource demands and staff requirements. Getting the best results from a data transformation tool takes careful consideration of important factors, including the following:

• Feature set. The tool should support the types of data transformation that the business needs, such as aggregation, cleaning, normalization and data enrichment. Different tools focus on different strengths, so consider any special focus or transformation use cases that the tool provides.
• Data sources. The tool should support the varied data sources the business requires, such as database files, cloud storage, flat files, or general or third-party formats such as Excel.
• Data management. The tool should provide data management capabilities such as tagging, indexing and other discoverability features that let the business easily correlate raw data with transformed and processed data.
• Automation. The tool should support a full range of automation, allowing transformation workflows to be handled with a high degree of autonomy and consistency.
• Testing and validation. The tool should provide some level of testing so that data passes through the workflow properly and provides valid, useful data for analysis.
• Coding. The tool will inevitably require some level of coding to detail transformation processes, workflows, interfaces and automation, but the coding requirements should be well-understood and within the capabilities present in the existing development team.
• Scalability. The tool should support current data volumes and workflow complexity and have the potential to handle additional data volumes and more complex workflows in the future.
• Support. The tool should have comprehensive and responsive vendor support. Open source tools should have an active, responsive and helpful user community.

As with any tool selection, it's worth planning a series of cross-departmental proof-of-concept (POC) tests to evaluate potential tools and get firsthand experience with potential products before making a final adoption decision.

Learn what a POC is and how an organization can create one to help lay out the criteria needed when purchasing a new product. Examine how to write a POC using these free templates.