Published: 19 June 2024
Contributors: Molly Hayes, Amanda Downie
Data transformation is a critical part of the data integration process in which raw data is converted into a unified format or structure. Data transformation ensures compatibility with target systems and enhances data quality and usability. It is an essential aspect of data management practices including data wrangling, data analysis and data warehousing.
While specialists can manually achieve data transformation, the large swaths of data required to power modern enterprise applications typically require some level of automation. The tools and technologies deployed through the process of converting data can be simple or complex.
For example, a data transformation might be as straightforward as converting a date field (for example: MM/DD/YY) into another, or splitting a single Excel column into two. But complex data transformations, which clean and standardize data from multiple disparate sources and consist of multiple workflows, might involve advanced data science skills. These advanced data engineering functions include data normalization, which defines relationships between data points; and data enrichment, which supplements existing information with third-party datasets.
In today’s digital-first global economy, data transformations help organizations harness large volumes of data from different sources to improve service, train machine learning models and deploy big data analytics.
Access IBM's guide to AI and data management to learn how to make the optimal investment in the right open, trusted data foundation.
By standardizing datasets and preparing them for subsequent processing, data transformation makes several crucial enterprise data practices possible. Common reasons for data transformation in the business world include:
Organizations transform data for use in business intelligence applications like real-time dashboards and forecast reports, allowing for data-driven decision-making that takes vast amounts of information into account.
Data transformation prepares data for storage and management in a data warehouse or data lake, facilitating efficient querying and analysis.
Machine learning models require clean, organized data. Ensuring the data is trustworthy and in the correct format allows organizations to use it for training and tuning artificial intelligence (AI) tools.
Before big data can be analyzed for business intelligence, market research or other applications, it must be collated and formatted appropriately.
Moving data from older on-premises systems to modern platforms like a cloud data warehouse or data lakehouse often involves complex data transformations.
Data transformations typically follow a structured process to produce usable, valuable data from its raw form. Common steps in a data transformation process include:
During the discovery process, source data is gathered. This process might include scraping raw data from APIs, an SQL database or internal files in disparate formats. In identifying and extracting this information, data professionals ensure that the collected information is comprehensive and relevant to its eventual application. During discovery, engineers also begin to understand the data’s characteristics and structure in a process known as data profiling.
Data preparation and cleaning requires identifying and fixing errors, inconsistencies and inaccuracies in raw data. This step ensures data quality and reliability by removing duplicates and outliers or handling missing values.
Data mapping involves creating a schema or mapping process to guide the transformation process. During this process, data engineers define how the elements in the source system corresponds to specific elements in the target format.
Either using a third-party tool or by generating code internally, during this step an organization creates the code that will transform the data.
During this phase, the actual transformation takes place as code is applied to the raw data. Transformed data is loaded into its target system for further analysis or processing. The transformed data and data model are then validated to ensure consistency and correctness.
During the review process, data analysts, engineers or end users review the output data, confirming that it meets requirements.
ETL (extract, transform, load) and ELT (extract, load, transform) are two frequently used data transformation processes that deploy slightly different data pipeline techniques. Each has advantages and disadvantages depending on the size and complexity of the transformation.
In the ETL process, a predetermined subset of structured data is extracted from its source, where it is transformed in a staging area or secondary processing server before being loaded into its target system. ETL is better suited to on-premises storage and smaller data sets. However, ETL can be preferable in scenarios with specific data quality and consistency needs, as more rigorous data cleaning and validation steps can be introduced. ETL may also be necessary to protect sensitive data, like HIPAA-protected information, during migration.
In the ELT process, information is extracted from data sources and loaded into the cloud-based target system, where it is transformed. This approach, as it takes advantage of cloud computing power, typically allows for faster processing and more agile data management. It can also be used with unstructured data such as images. With the advantage of cloud-based computing and storage power, the ELT process benefits from increased scalability.
Data scientists and engineers use several distinct techniques throughout the data transformation process. Which tactics are deployed depends entirely on the project and intended use for the data, though several methods may be used in tangent as part of a complex process.
- Data cleaning: Data cleaning improves data quality by rectifying errors and inconsistencies, such as eliminating duplicate records.
- Data aggregation: Data aggregation summarizes data by combining multiple records into a single value or dataset.
- Data normalization: Data normalization standardizes data, bringing all values into a common scale or format such as numerical values from 1 to 10.
- Data encoding: Data encoding converts categorical data into a numerical format, making it easier to analyze. For instance, data encoding might assign a unique number to each category of data.
- Data enrichment: Data enrichment enhances data by adding relevant information from external sources, such as third-party demographic data or relevant metadata.
- Data imputation: Data imputation replaces missing data with plausible values. For instance, it might replace missing values with the median or average value.
- Data splitting: Data splitting divides data into subsets for different purposes. For example, engineers might split a data set to use one for training and one for testing in machine learning.
- Data discretization: In data discretization, data is converted into discrete buckets or intervals in a process sometimes referred to as binning. As an example, discretization might be used in a healthcare setting to translate data like patient age into categories like “infant” or “adult.”
- Data generalization: Data generalization abstracts large data sets into a higher-level or summary form, reducing detail and making the data easier to understand.
- Data visualization: Data visualization represents data graphically, revealing patterns or insights that might not be immediately obvious.
While it’s possible to perform a data transformation by deploying only in-house engineers, many third-party services help facilitate the conversion and migration process. Some of the most common include:
- ETL and ELT tools: Tools such as Apache NiFi and Informatica facilitate a plug-and-play ETL or ELT process.
- Data integration platforms: A host of tools such as IBM Cloud Pak® for Data support data integration and real-time processing.
- Data preparation tools: These tools are specifically designed for data cleaning and transformation prior to a data transformation or migration.
- Programming languages: Programming languages like Python and R, with libraries such as the open-source pandas, provide robust capabilities for data transformation.
Data transformation is a crucial step in data processing. It improves an organization’s capacity for analysis, reporting, decision-making and storage. Key benefits include:
Data transformation includes processes like data cleaning, which improves the overall quality of a data set. With better data and well-defined data architectures, organizations improve operational efficiency in areas such as inventory management and order processing. Better data also improves the customer experience, providing a 360-degree view of current and potential consumers.
Data transformation standardizes data formats and structures, making it easier to integrate information into a cohesive data set. By breaking down data silos and unifying information from disparate departments or systems, an organization eliminates inconsistencies and gains a unified view of the business.
Typically, transformed data is more organized and structured, making it easier to create meaningful visualizations that communicate insights effectively. Visualizations help decision-makers identify trends or opportunities and can present crucial data, like sales or procurement pipelines, in near real-time.
Data transformation converts complex or unstructured data into formats that are easier to understand, access and analyze. Organizations use such data to create advanced market forecasts or identify areas for improvement.
Data transformation can include data anonymization and encryption, protecting sensitive information and complying with privacy regulations. Such security remains paramount for highly regulated industries, such as healthcare and finance, as well as for organizations operating in multiple geographies with varying privacy laws.
Transformed data is often more flexible and simpler to streamline, making it easier to adapt to new use cases or scale up data processing as the amount of data grows. Scalable data ensures that an organization grows without multiple costly reorganizations and IT implementations.
Uncover the value of enterprise data and build an insight-driven organization that delivers business advantage with IBM Consulting®.
With the IBM DataOps platform, organizations eliminate the distinction between data- and development-focused teams, increasing efficiency in everything from bug-fixing to goal setting.
IBM data and AI solutions empower organizations to use enterprise data to improve resiliency, realiability and cost-effectiveness without sacrificing data security or quality.
IBM watsonx.data™ enables organizations to scale AI and analytics with all their data, wherever it resides.
Modern data architectures—like data fabric—can help shape and unify a data-driven enterprise, guiding decision-making and increasing the quality of data governance and integration.
Explore why high-quality data is essential for the successful use of generative AI and learn how enterprise data can drive productivity gains.
Explore the watsonx.data trial experience by following Amelia the Data Engineer as she connects to her external data sources, accesses and queries data, and offloads data from her data warehouse for cost optimization.
Explore resources for data leadership using this guide, which will help you deploy the strategy, technologies and culture that are foundational to leading a data-driven organization fueled by AI.
Explore how IBM and the National Association of Boards of Pharmacy (NABP) centralized a digital hub to increase transparency and preserve the integrity of the drug supply chain.
Explore how data virtualization streamlines the merging of data from diverse sources and fuels machine learning by breaking down data silos.
Explore how IBM watsonx.data and Cogniware unified data from disparate sources to visualize connections and examine the activities of individuals under investigation.