Data transformation: A guide to what, why, and how

Data transformation allows raw data to be harnessed and used in data analytics and data science. This essential step enhances the quality, reliability, and accessibility of data, making it suitable for analysis, visualization, and training machine learning models.

There are numerous data transformation techniques available, each catering to different project requirements and dataset characteristics. To unify, standardize, and refine raw data, the data transformation process includes cleansing, formatting, and removing unnecessary data.

In this article, we will explore the benefits and challenges of data transformation, highlight best practices, and outline common data transformation techniques.

What is the data transformation process?

Data transformation is the process of converting data structures, formats, and/or types to make it more accessible and usable. For example, a business might extract raw data from various systems, transform it using a method such as normalization, and then load it into a centralized repository for storage.

Individual projects will use different data transformation steps depending on their goals. However, these five steps are typically used in a standard data transformation process:

1. Data discovery: The data transformation process starts with data experts using data profiling tools to identify the original data sources' structures, formats, and characteristics. This helps them choose the best data transformation techniques to use for the next steps.
2. Data mapping: The experts then map out how to transform the data. They define the relationships between data elements from different sources and construct a plan for aggregating and transforming them. Data mapping also helps professionals maintain a clear record of changes through metadata.
3. Code generation: Data analysts use the data map to guide the generation of transformation code. The generation of these codes is often automated by using scripts, algorithms, and data transformation platforms.
4. Code execution: The data transformation is now executed. During this phase, the data is gathered from sources, transformed according to the rules defined in the mapping stage, and sent to a storage location. Batch or real-time processing pipelines are used for this step.
5. Review: The last step is reviewing the transformed data to ensure it is accurate, complete, and meets the previously defined transformation requirements. If there are any discrepancies, anomalies, or errors, data experts will use further data transformations to correct or remove them.

Types of data transformation

There are four main data transformation types. It is important to understand when each one should be used.

Constructive Transformations

Constructive transformations create new data attributes or features within the dataset. They enhance existing features or generate new ones to improve the quality and effectiveness of data analysis or machine learning models. Examples include feature engineering, aggregating data points, or deriving new metrics. These transformations add value to the dataset by generating additional information or providing a better representation of existing data, making it more suitable for analysis.

Destructive Transformations

Destructive transformations remove unnecessary or irrelevant data from the dataset. This streamlines the information, making it more focused and efficient. Destructive data transformation types include data cleaning (removing duplicates or correcting errors), dealing with missing values (imputation or deletion), and feature selection (eliminating redundant or irrelevant features). By reducing noise and distractions, destructive transformations contribute to more accurate insights and improved model performance.

Formatting transformations

Formatting transformations deal with the presentation and organization of data, ensuring it adheres to a common format. These transformations include data standardization (converting data to a common format), sorting, and formatting. While formatting transformations may not directly affect the analytical or predictive power of the data, they play a vital role in facilitating efficient data exploration, visualization, and effective communication of insights.

Structural Transformations

Structural transformations involve modifying the overall structure and organization of the dataset, making it more suitable for analysis or machine learning models. This includes reshaping data, normalizing or denormalizing databases, and integrating data from multiple sources. These transformations are useful for time series analysis, multi-source data integration, preparing data for machine learning, data warehousing, and data visualization.

Data transformation methods

There are many data transformation techniques available within these four categories. In this section, we will explore some of these techniques, looking at the problems they solve, when they are used, and how they work.

Manipulation (Data Cleaning):

Problem solved: Data manipulation addresses data quality issues, such as errors, inconsistencies, and inaccuracies, within a dataset.

Use case scenarios: Data manipulation is crucial in almost every data analysis project.

How it works: Techniques include removing duplicate records, filling in missing values, correcting typos or data entry errors, and standardizing formats. Data manipulation ensures that the dataset is reliable and accurate for analysis or machine learning models.

Normalization:

Problem solved: Data normalization scales numerical features to a standard range, typically [0, 1] or [-1, 1]. This prevents features with larger scales from dominating the model and causing biased results.

Use case scenarios: Normalization is particularly important when working with machine learning algorithms that are sensitive to the scale of input features.

How it works: Techniques include min-max scaling and z-score standardization, which transform the original feature values to a standard range or distribution, making them more suitable for analysis and modeling.

Attribute Construction (Feature Engineering):

Problem solved: Attribute construction creates new features or modifies existing ones to improve the performance of machine learning models.

Use case scenarios: Feature engineering can be useful in various scenarios, such as combining or aggregating features to capture higher-level patterns, applying mathematical transformations (e.g., log, square root) to address skewed distributions, or extracting new information from existing features (e.g., creating a day of the week from a timestamp).

How it works: Feature engineering can be accomplished through various methods, such as mathematical transformations, aggregation, binning, and dimensionality reduction techniques. The goal is to create new data attributes that are more representative of the underlying patterns in the data and that help to improve the performance of the machine learning model.

Generalization:

Problem solved: Generalization reduces the complexity of data by replacing low-level attributes with high-level concepts.

Use case scenarios: Generalization can be useful in scenarios where the dataset is too complex to analyze, such as in image or speech recognition.

How it works: Techniques include abstraction, summarization, and clustering. The goal is to reduce the complexity of the data by identifying patterns and replacing low-level attributes with high-level concepts that are easier to understand and analyze.

Discretization:

Problem solved: Discretization converts continuous data or variables into discrete intervals, making them more suitable for analysis.

Use case scenarios: Discretization can be useful in scenarios where the data is continuous but needs to be analyzed using techniques that require categorical variables, such as decision trees or association rule mining.

How it works: Techniques include equal width, equal frequency, and clustering-based discretization. The goal is to convert continuous variables into categorical variables that are easier to understand and analyze.

Data Aggregation:

Problem solved: Aggregation combines data at different levels of granularity, making it easier to analyze and understand.

Use case scenarios: Aggregation can be useful in scenarios where data needs to be analyzed at different levels of detail, such as in financial analysis or sales forecasting.

How it works: Techniques include summarization, averaging, and grouping. The goal is to combine data at different levels of granularity, creating summaries or averages that are more representative of the underlying patterns in the data.

Data Smoothing:

Problem solved: Smoothing removes noise and fluctuations from data, making it easier to analyze and interpret.

Use case scenarios: Smoothing can be useful in scenarios where the data is noisy or contains fluctuations that obscure the underlying patterns.

How it works: Techniques include moving averages, exponential smoothing, and kernel smoothing. The goal is to reduce noise and fluctuations in the data, making it easier to analyze and interpret.

An example of data transformation: ETL and ELT

Commonly used in data warehousing and data management, the ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) data integration pipelines are good data transformation examples.

These data pipelines involve extracting data from various sources (such as a CRM, web page, ERP, or SQL server), transforming it using the methods described above, and loading it into a target repository (such as a data warehouse or data lake).

Choosing between ETL and ELT for data transformations will depend on a number of factors, such as the complexity or type of transformations you are performing. We’ll go over some general guidelines for choosing the best transformation techniques next.

How to choose the best data transformation technique

Choosing the best data transformation technique depends on various factors, such as the nature of the data, the objectives of the project, and the requirements of the target analysis or machine learning models. Here are some general guidelines that will allow you to choose the best data transformation techniques:

• Understand the data: Analyze the data thoroughly to identify its characteristics, such as its scale, distribution, and outliers. This will help to determine which data transformation techniques will be best for the dataset.
• Identify the objective: Determine the project's objectives and what insights need to be gained from the data. This will help you decide how to transform the data to achieve your goals, for example, by making data suitable for specific algorithms.
• Consider the downstream techniques: Review the analysis or modeling techniques that will be applied to the data after transformation. This will help identify data transformation techniques that are compatible with downstream analysis or modeling techniques.
• Evaluate the impact: Evaluate the impact of each data transformation technique on the data and downstream analysis or modeling techniques. Choose techniques that have a positive impact and avoid those that negatively impact the data or downstream tools.
• Experiment and iterate: Experiment with different data transformation techniques to determine which ones work best for the data and objectives of the project. Iterate as needed to refine the data transformation process and improve the data quality for downstream analysis or modeling.

By following these guidelines, you can choose the best data transformation techniques for your project and ensure that your data is suitable for downstream analysis or modeling.

Benefits of data transformation

Transforming data increases its usability, results in more effective data analysis, and offers a host of other benefits.

1. Improved data quality: Data transformation helps identify and correct inconsistencies, errors, and missing values, leading to cleaner and more accurate data for analysis.

2. Enhanced data integration: Data transformation converts data into a standardized format, enabling seamless integration from multiple sources. This allows for greater collaboration and data sharing across different systems.

3. Better decision-making and business intelligence: Data transformation cleans and integrates data, providing a solid foundation for analysis and the generation of business insights. Organizations improve efficiency and competitiveness by using these insights to inform decisions.

4. Easier scalability: Data transformation helps teams manage increasing volumes of data, allowing organizations to scale their data processing and analytics capabilities as needed.

5. Stronger data privacy: Data transformation techniques like anonymization, pseudonymization, or encryption help businesses protect data privacy and comply with data protection regulations.

6. Improved data visualization: Data transformation makes it easier to create engaging and insightful data visualizations by supplying data in appropriate formats or aggregating it.

7. Easier machine learning: Data transformation prepares data for machine learning algorithms by converting it into a suitable format and addressing issues like missing values, outliers, or class imbalance.

8. Greater time and cost savings: Data transformation automation helps organizations reduce the time and effort needed for data preparation, allowing data scientists and analysts to focus on higher-value tasks.

Challenges of data transformation

Alongside its numerous benefits, the data transformation process does present some challenges.

1. Complexity: Modern businesses collect huge volumes of raw data from multiple sources. The more data there is, the more complex the data transformation process becomes, especially for companies with convoluted data infrastructures.

2. Expertise: Data professionals must possess a deep understanding of data context, different types of data transformations, and the ability to use common automation tools. If businesses don’t retain employees with this high-level expertise, they will be at risk of critical misinterpretations and errors.

3. Resources: Data transformation demands extensive computing, processing, memory, and manual effort. For businesses with high data volumes and complexity, the widespread resource strain of transforming data can slow down operations.

4. Cost: The cost of the tools, infrastructure, and expertise needed for data transformation can be a significant budget constraint. This can be a particular difficulty for smaller companies.

The benefits of data transformation certainly outweigh the challenges. However, jumping into transformation processes without sufficient planning can result in operational and financial upset, as well as decreased data value. To mitigate these drawbacks, you should implement a robust data transformation strategy, proceed with caution, and follow data transformation best practices.

Incorporating data transformation best practices

To avoid the challenges we discussed earlier and extract the most value from your data, implement these best practices when building your data transformation strategy.

Conduct thorough data profiling

Any data transformation process should begin with meticulous profiling. Evaluate the quality and complexity of your data by examining characteristics like data types, volumes, value ranges, relationships, irregularities, and duplicates.

This profiling will provide a clear understanding of the integrity of your data, allowing you to accurately identify transformation needs and increase the efficiency and effectiveness of your data transformations.

Perform standardization across data types and naming conventions

Data from sources with different naming conventions, formats, and structures is harder to integrate, compare, and use.

Standardizing data involves applying uniform naming conventions and data types across sources and should be a data transformation priority. Data like phone numbers, dates, and timestamps should be standardized to match the format of your target destination. This will ensure correctness, reduce complexity, and enhance system interoperability.

Develop a process to handle missing or malformed data

Missing, malformed, and inaccurate data lowers overall data quality, reducing the reliability of insights and decisions.

Establish a process for effectively handling incomplete data by identifying its source and outlining the appropriate steps to fix it. Using data imputation allows you to fill in missing data, while data exclusion involves removing invalid data.

Understanding the impact that missing or malformed data will have on subsequent data analysis will allow you to decide whether data imputation or data exclusion is more appropriate.

Validate your data to ensure an effective process

Data validation is crucial for maintaining data accuracy and quality during the transformation process.

Before new data is ingested into your target repository, validate it by evaluating a sample test log directly from your target destination. Check that standards are adhered to across naming conventions and datatypes and look for missing, inaccurate, or duplicate data points. Any of these could indicate a flaw in your data transformation process and addressing them proactively will help you avoid costly and inconvenient corrections later.

What is a data transformation tool and should you use one?

Data transformation tools are specialized software solutions that automate, streamline, and optimize the data transformation process. These tools can handle numerous tasks, from cleansing data to modifying and enriching it, that help businesses quickly and reliably meet their data transformation objectives.

There are lots of data transformation solutions to choose from—but, does your business really need one? In short, yes. And here’s why:

• Improves the quality and accuracy of data: Data transformation solutions cleanse, standardize, and validate data more accurately than manual processes. Errors and inconsistencies are quickly identified and corrected, improving data quality and subsequent analysis. Removing the risk of human error results in more reliable and accurate data for analysis.
• Accelerates transformations: Manual data transformation is time-consuming and prone to error. Data transformation tools automate these manual, repetitive, and error-prone tasks. As well as speeding up processes and reducing the risk of errors, automation allows experts to concentrate on higher-value tasks, such as analysis.
• Simplifies scalability: Data transformation tools can manage large volumes of active data efficiently. Furthermore, they can scale limitlessly alongside businesses as they grow. This keeps the data transformation processes running effectively and increases ROI for your data transformation tech stack.

Next steps: Simplify data transformation with RudderStack

Data transformation turns raw, unstructured, and unusable data into clean, organized, high-quality data ready for use. From enriched business intelligence and decision-making to improved collaboration and scalability, data transformation accelerates business growth with high-quality data.

That said, it’s crucial to implement a data transformation strategy that includes thorough profiling, mapping, standardizing, and validation. Combine this with an experimental and iterative approach, and you can continuously refine your data transformation strategies to optimize processes and improve the accuracy of your results.

To achieve efficient and reliable data transformation, you need the right tools. With RudderStack, you can collect, unify, and monitor customer data across all of your data sources, leveraging sophisticated features like pre-defined tracking plans, schema violation management, and end-to-end proactive data monitoring.

Interested in how RudderStack can streamline and automate your data transformation process? Sign up to try RudderStack for free.