Data transformation

What is data transformation?

Data transformation is changing data from one format, structure, or type to another to make it suitable for analysis. Think of this as prepping ingredients before starting to cook. What happens during data transformation is the data gets cleansed, filtered, structured, and formatted, de-duplicated, and organized, just like how you wash, peel, and cut vegetables before making soup. The transformed data is often moved to a storage destination, like a data warehouse or a Lakehouse.

Example of data transformation is conversion of a spreadsheet with messy data into a visual chart. This involves the removal of cells with no or erroneous values, sorting out values in order, and formatting fields for consistency.

Data transformation techniques  

Some commonly used data transformation techniques used while handling simple datasets include:

Data cleansing: data cleansing is a data transformation process to perform basic cleansing of data, like removing errors or typos or dealing with missing values or duplicates. 

Encoding: encoding is the process of converting non-numerical data into a binary format, so other systems can understand and process it. 

Sorting: sorting is about organizing data in a particular format, like arranging a sequence in ascending order or alphabetically.

Merging: combining multiple datasets into a single dataset. Merging is done to form a complete, consolidated dataset, handle distributed datasets, and support machine learning models with more aligned features.

Splitting: splitting is breaking down one single dataset into multiple sets 

Pivoting: pivoting is a data transformation function in Excel and other tables, where you turn rows into columns or vice versa to identify hidden patterns. 

Applying functions: this is when you apply mathematical functions and logic to datasets, for example, converting temperature in Celsius to Fahrenheit or kg into tons. 

Some advanced data transformation techniques in data science and AI model building are as follows:

Feature engineering: Feature engineering in ML/AI model building is about building new features or modifying existing ones to get the desired output. Feature engineering involves data transformation like polynomial conversions, log transformations, etc.  

Binning: a data transformation technique that sorts continuous numerical variables and groups them into buckets. Binning helps with improving model performance, handling outliers (as the high-range values don’t influence model’s output).

Dimensionality reduction: machine learning and AI models often deal with complex, multi-dimensional datasets. You use dimensionality reduction, a data transformation technique, to reduce its number of variables, while retaining original essence.  

Imputation: imputation technique is used to handle missing values in datasets, by replacing them with imputed values. One way to do this is to impute the mean value of the overall fields. 

Data synthesis: this transformation technique involves creating artificial data sets to balance data sets.

Data masking and anonymization: these are tokenization approaches, where you mask sensitive data with random or defined variables to ensure data usability for less-privileged users.

Data transformation tools

Some popular tools used by data professionals to process, clean, and transform data are as follows.

Apache Spark - open source framework used for large-scale data processing, which supports both real-time and streaming data.

Microsoft Power Query - A self-service tool integrated with Power BI and Excel, suitable for small to mid-level data sets. It is used to remove duplicates, split columns, and reshape datasets.

Dbt (data build tool) - data transformation tool for data teams, which requires version control and automation of transformation together. Very useful for SQL-based data modelling in data warehouses.

Azure data factory - cloud based ETL tool for data transformation and integration. 

FiveTran - a cloud-based tool to automate data extraction and transformation and loading into any cloud-based data warehouse.

There are many other data transformation tools, as well as AWS Glue, Databricks, Alteryx, Talend, Matillion, etc. One could also use Excel for mid and low-level transformation, including combining or splitting columns, and other basic actions like formatting and structuring.

📖 Recommended read: ETL definition, tools, and best practices explained

Data transformation use cases

Data transformation is a common process involved in many use cases. 

Customer data integration: solutions like customer 360 require data transformation, where you transform data extracted from sources like ERP, CRM, customer support systems, etc., to suit your analysis. 

Financial data reporting: financial data reporting often requires transformation techniques like conversion of currency standards, aggregation of columns, modifying data to suit compliance standards, etc.

IoT data processing: In order to extract data from IoT and smart devices and make it usable, you will need data transformation techniques like conversions, cleansing, noise removal, and more.

Sales analysis in retail and eCommerce: if you want to assess sales performance across multiple regions, basic and advanced math and logical functions have to be in place. Like merging, finding average, turning into %, etc.

Supply chain efficiency: finding supply chain efficiency involves handling real-time data from warehouse, distribution partners, and manufacturing units. This can involve transformation tasks like data consolidation, cost estimations, enrichment using seasonal trends, etc. 

Predictive maintenance ensures optimal use and predictive care of equipment in manufacturing and logistics units. It involves data transformation techniques like data cleaning, aggregation, integration, etc.

Data transformation, a part of the extract, transform, and load process, ensures that your filtered out data is ready for analysis and decision-making. With prior data discoverability measures, you could ensure that the available insights are relevant for business teams and that there will be no inconsistencies.