What Is Data Transformation?

Data-driven decision making is a smart strategy for companies that want to base their initiatives, growth moves, and product and service directions on facts rather than on intuition and guesswork. Today’s data-rich business environments provide ample information for them to do so. Yet, the quality of data is just as important as the quantity. That’s why data must be transformed from its raw state into a usable form.

Such data transformation increases the efficiency and effectiveness of data analytics, leading to better decisions, such as whether to use omnichannel customer care, where to open a new location or what product lines to develop. Before performing data transformation, data analysts determine the best structure for the data, depending on its intended use.

Here we examine the benefits and challenges of data transformation, data transformation types, methods, and rules, and the process used to perform data transformation. Read on to learn about the transformation process that will help you make more of one of today’s most critical assets: data.

Data Transformation Benefits

Data transformation creates the conditions for a company to get more from its data collection and analysis. For example, transformed data is easier to access and better organized because it has been moved to a secure, convenient location. Also, bad data can lead to bad decisions, while “clean” data provides a properly formatted and validated starting point from which to analyze information.

All these benefits lead to better decisions, which lead to less waste, better customer care, more revenue, and a competitive edge. The following video describes a data-driven framework for business:

Data Transformation Challenges

Data transformation also entails some challenges. It can use up a significant amount of company funds and resources, especially because data meant to be used for different purposes may need to be transformed more than once. Expenses include licensing, computing equipment, and hiring properly trained personnel, as well as computing power that may be required for other operations.

Also, data can become less usable through errors in the transformation process. These errors are especially common when those performing the data transformation are less experienced or have less familiarity with the specific subject matter involved (such as financial data).

Data Transformation Types, Methods, and Rules

Data transformation can be one of two types: batch data transformation or interactive data transformation. The first involves developers writing code that includes transformation rules and running it on large amounts of data. A subset of this type is “micro-batch,” which is used when data must be transformed with low latency.

Interactive data transformation includes an interface that enables users to interact with large datasets through a visual interface. They can simply view the data, or they can change it using a simple interface to select and alter specific data elements. This type of data transformation uses a less linear approach and requires less training on the part of users.

Data analysts have several methods for transforming data, as follows:

Scripting. This method uses Python or SQL to write the code that extracts and transforms data. While this method allows analysts to customize their approach, it can also take longer and result in more errors. Additionally, codes must be rewritten each time the process is needed.

On-premise ETL tools. Hosted on company servers, these tools automate the data transformation process. These tools are cost-effective and have the added benefit of generating visual representations of data flow and incorporating additional features and the ability to scale for larger projects.

Cloud-based ETL tools. Like on-premise ETL tools, cloud-based ETL tools automate the data transformation process. However, they are hosted in a cloud environment and allow analysts to collect data from cloud sources and load it into a data warehouse.

Data transformation rules are instructions that specify certain alterations to be performed to convert the structure and semantics of data from one source to another. Semantic principles provide definitions of data elements, such as what characterizes a complete transaction. Reshape rules define how to bring data elements from one source to another. And taxonomy rules associate data source values with values of the target data.

Data Transformation Process

The data transformation process includes 3 major steps: extract, load, and transform, also known as ETL. Here are the specific steps involved:

Data discovery. Analysts identify data by using data profiling tools and decide the next steps they’ll need to take to get the data into a format they determine.

Data mapping. Analysts define how individual data fields are modified, mapped, filtered, joined, and aggregated. This step may involve narrowing down data to make it more manageable, such as eliminating particular fields, columns, or records that aren’t needed.

Data extraction. In this step, analysts extract the data from its original source, such as databases, or customer log files from web applications.

Data encryption. In many fields in which privacy is an issue, personal data must be encrypted.

Code generation and execution. In this step, analysts use data transformation platforms or tools to generate codes to complete the transformation.

Review. Finally, analysts check for proper formatting.

In addition to these standard steps, analysts might also implement customized operations such as filtering the data by certain columns, adding more information, removing duplicate data, or joining sets of data together. Upon completion of this process, analysts send the transformed data to its target destination, such as a data warehouse or database.

Bad Data Prevents Good Decisions

With many complex systems involved in today’s business environments, data produced on one may not be usable on another. Data transformation solves this problem by converting data to be used in the target system. This process is necessary for companies that want to make the best use of the massive amounts of data generated by numerous sources.

Without data transformation, companies would be stuck with data that includes: