6 Data Transformation Methods & Techniques for Business Growth

Businesses worldwide produce and collect about 2.5 quintillion bytes of data each day. Much of that data is raw—without format, organization, structure, or context—and can't be analyzed right away. Raw data needs to be transformed to be of any business value.

In this article, we'll describe what data transformation is and explain why it's so important, as well as detail the techniques that make raw data ready to analyze. We'll also go over some considerations to help you choose the method that works best for your needs.

What is data transformation?

Data transformation is the process of cleansing, converting, and restructuring raw data into an optimized format for storage and processing. This ensures that the data being used for decision-making is ready for analysis.

Data transformation is typically carried out using two frameworks, ETL (extract, transform, load) and ELT (extract, load, transform):

• ETL: Raw data is first extracted from your sources, and the transformation is performed in-flight (midstream). Then, the transformed data is loaded to the destination.
• ELT: In this format, immediately after extraction, the raw data is loaded onto the destination before it's transformed.

Regardless of the framework used, data transformation is necessary for businesses to integrate data from multiple sources, standardize varying data formats, and decipher meaningful trends and patterns. It is a key process that empowers organizations to innovate and grow in a competitive and data-driven environment.

5 benefits of transforming data

• Enhanced data quality: Identifying inconsistencies, removing duplicates, and correcting errors ensures you maintain quality data. Enhancing data quality leads to consistent and reliable data that can be used for strategic decision-making and analysis.
• Improved data utilization: Converting raw data into a more usable format allows organizations to use the data effectively. Leaving raw data in unusable formats can lead to data not being utilized fully. Better data utilization can help organizations leverage the true potential of their data.
• Better data accessibility: After transforming your data, it becomes more accessible for users across different departments because it is in a standard format and/or a central repository. This reduces the barriers to unorganized data and can enhance collaboration across organizations.
• Streamlined integration: Transforming your data can assist with harmonizing your datasets to ensure the various data points can be merged and analyzed efficiently. With data coming from multiple sources in multiple formats, it is crucial to ensure your data can come together effectively.
• Scalability and Efficiency: Transformed data is typically enhanced for quicker processing and analysis. This can reduce your resource usage and help improve scalability and efficiency as your data volume grows.

6 data transformation strategies

Data smoothing

Smoothing is the statistical process of removing outliers or noise from datasets to expose patterns and insights that raw datasets could hide. The most common smoothing methods are moving averages, exponential smoothing, or binning.

Implementation tips:

• Choose an appropriate smoothing method based on the nature of your data characteristics.
• Avoid over-smoothing your data because this can obscure meaningful variations or trends. You can visualize your smoothed data alongside your raw data to ensure key trends remain intact.

Data aggregation

Aggregation takes data from multiple sources and combines it into a summary format, such as calculating totals, averages, or other measurements that may be useful. Data aggregation is most common in big data environments to help improve efficiency.

Implementation tips:

• Standardize and document your aggregation rules to maintain consistency across datasets in the future.
• Ensure you choose a meaningful aggregation level for your dataset, such as using weekly versus daily sales numbers.
• Maintain access to your raw data so critical details are not lost during aggregation.

Data normalization

Normalization transforms your data to a uniform range or standard deviation, ensuring consistency across datasets. This process eliminates redundancy and removes the influence of varying scales, making the data more suitable and comparable for modeling and analysis. Data normalization is especially important for machine learning algorithms that are sensitive to scale, such as gradient descent. The most common normalization techniques are min-max scaling, z-score normalization, and decimal scaling.

Implementation tips:

• Outliers can skew normalization results if left unhandled, so it is best to handle outliers before normalizing a dataset.
• Ensure you understand the characteristics of your dataset so that the correct normalization method is used. For example, you should use min-max scaling for bounded data and z-score scaling for unbounded or normally distributed data.
• Keep your normalization parameters consistent across all datasets and ensure they are documented for transparency.

Data generalization

Generalization is the process of reducing the complexity of your data to make your datasets easier to manage for analysis. It takes low-level attributes and replaces them with higher-level summaries that have a concept of hierarchy. For example, this could include grouping ages into levels, like “18-25.”

Implementation tips:

• Ensure your generalizations are meaningful by leveraging domain knowledge and insights from subject matter expertise.
• Avoid overgeneralization, as it can lead to the loss of significant details or insights. Test your generalizations by reviewing their effect on the results and insights from your analysis to ensure patterns remain clear and accurate.

Data discretization

Discretization divides continuous data into sets of discrete categories or intervals. While it may seem similar to generalization, discretization focuses on continuous data, whereas generalization is typically applied to simplify categorical data. Discretization can help simplify complex data for better interpretation and visualization that enhances the communication of results and trends to non-technical colleagues.

Implementation tips:

• Ensure that you choose a discretization technique that matches your dataset. Such as equal-width binning for even distributions, equal-frequency binning for skewed distributions, and clustering-based binning for data with natural groupings already in place.
• Properly document the boundaries and rationale for each bin or interval to ensure consistency and transparency.

Data encoding

Encoding is the process of converting categorical data into numerical values or symbols that algorithms can process properly. This allows data to be compatible and readable for machine learning models (MLMs) and other processes that can only use numerical data. Some common types of data encoding include label encoding, one-hot encoding, frequency encoding, target encoding, and binary encoding.

Implementation tips:

• Understand the nature of your data so that the right encoding method is selected. For example, using one-hot encoding for colors and label encoding for sizes.
• Ensure your encoding is consistent across datasets and that your original categorical data is available alongside your encoded data for interpretability.

Learn more about other data optimization techniques: Data Wrangling vs Data Cleaning

How to choose the most suitable data transformation technique

Now that you know the benefits of transforming your data and the different strategies, you will need to choose the technique that works best for your data to ensure high-quality analysis and insights. Below are a few things to keep in mind when selecting a data transformation technique:

• Understand your data: It is vitally important to analyze and understand the characteristics of any dataset before transformation is performed. Identifying characteristics such as distribution, outliers, missing data, or any underlying patterns can help point you toward a transformation technique that suits the dataset.
• Experiment and iterate: Sometimes, it is not 100% clear which techniques should be used for your dataset. It can be beneficial to test multiple data transformation methods and iterate through those to identify which approach is most effective.
• Consider downstream analysis: While reviewing data transformation strategies, you should aim to tailor the transformation to the requirements of the downstream processes that are applied after the transformation. In many instances, the analysis and modeling that is done after the data is transformed can perform better with more compatible data formats.
• Evaluate the impact: Test your dataset to determine if the selected transformation strategy improves outcomes by reviewing metrics such as model accuracy, goodness-of-fit, and interpretability. As new data and insights become available, you can revisit transformations to validate any previous assumptions about the dataset.
• Define the analytical goal: Ensure that your chosen transformation technique aligns with the overall objective of your project. Outlining the purpose of your transformation can help narrow down which strategy fits best for your dataset.
• Plan for scalability and automation: For any workflow that involves real-time analysis or automation, you should ensure your data transformation techniques prioritize computational efficiency and consistently work across different batches of data. This is particularly important in production environments where your data transformation strategies are reused across large datasets.

Robust in-flight ETL and post-job ELT transformations with CData Sync

With your data transformation method chosen, you can enhance your data-driven decision-making by leveraging CData Sync. With CData Sync you can schedule and monitor data pipelines and add transformations and filtering before, during, or after data is moved to its destination to effectively drive organizational growth.

Take your data transformations to the next level by downloading a free, 30-day trial of CData Sync to start optimizing your organization's data for analysis and see how seamless transformations can help drive your business forward.