A Beginner’s Guide to Data Transformation: Techniques and Best Practices

Today, data is collected in so many different formats and systems that the majority isn’t really ready for analysis or the generation of useful insights. Data arrives messy, incomplete, and often in many different places and ways.

To generate useful insights for machine learning models and support the creation of data-driven business intelligence dashboards, the data must undergo data transformation and refinement. Both beginner and advanced practitioners of data analytics need to understand and apply data transformation concepts, techniques, and practices.

What Is Data Transformation and Why It Matter

So then at its essence, what is data transformation? The idea behind Data Transformation is to take the data you have collected (extracted from its source), and convert that data into an appropriate format for use in the final product (usually either a Data Warehouse or Data Lake). The goal of this step in the ETL process is to prepare the Raw Data to be analysed, and to present that raw data to the Consumer in a way that is clean (standardised), high-quality, and ready to use in whatever way the consumer chooses to use it.

Due to the vastly different formats of source data, it is necessary to do meaningful data transformation. Examples of source data include relational databases, cloud-based applications, spreadsheets, and many other types of APIs. Each type of source data has its own unique attributes, conventions, and inconsistencies that will require transformation to enable analysts to focus on analysis rather than cleaning and wrangling data.

The above points are important to note when deciding to do data transformation, but it is equally important to understand how data transformation impacts an analyst’s work. The following are direct impacts of data transformation on analysts:

1. Data quality: Transformation helps to fix missing values, invalid values, duplication, and enforce a consistent format across all datasets.
2. Usability/accessibility: Transformation allows the analyst to change data types, rename fields, and format their data into a schema (model) that is more efficient for querying and retrieval.
3. Compatibility: Using data transformation, the analyst has the ability to connect data from different sources into one cohesive view of the business.
4.  Performance: By summarising, reducing the volume of data, and aggregating that data, data transformation provides significantly better performance in terms of analytic speed.

Understanding the Role of Data Transformation in Data Analytics

In the process of transforming data, the objective is to create a cohesive analysis of these disparate pieces of information. The transformed data is then used as a basis for making decisions (i.e., identify trends), generate hypotheses about the future (i.e, forecasting), as well as generating reports (i.e., reporting). 

To a data scientist, data transformation is synonymous with “feature engineering”. This is where raw data is aggregated or otherwise manipulated to create variables that can serve as independent predictors in a regression model or supervised learning algorithm. Examples of this include creating a time-based metric from a timestamp, or normalizing an integer (scale). Another example is onehot encoding (creating binary columns for multiple variables) for categorical variables. 

For businesses that rely on business intelligence (BI) and data modeling, transformation provides the framework for building “dimensional” models (i.e., star or snowflake schemas) so users can quickly and efficiently navigate through various dimensions of their organization (i.e., time dimension vs. location dimension, etc.) 

Transformation allows businesses to move beyond just creating reports to analyze their data and to move into making predictions and prescriptions about the future. It is the mechanism that transforms raw data into coherent, understandable narratives (i.e. existing insight based on historical data).

Key Steps in the Data Transformation Process: From Discovery to Validation

The process of data transformation is not a single action but a workflow encompassing multiple distinct phases. While the exact steps can vary depending on the architectural approach (ETL vs. ELT), the core activities remain consistent.

The Foundation: Data Discovery and Profiling

A data professional must have a complete understanding of all available input before they can modify any column within it. Data profiling is the systematic assessment of all relevant aspects of data quality, structural integrity and contents within a data set.

Data profiling can generally be broken down into: 

• Structurally assessing the schema of the table & its data types, as well as the file format
• Statistically comparing/assessing all content within the tables (i.e. calculating summary statistics; determining unique values and null counts; ranking frequency of appearance)
• Quality assurance of all data within the data set by identifying errors, inconsistent values and violations of business rules

For more complex needs in sourcing data (consolidation of both publicly available information and company owned records), one of the most critical initial stages is how to scrape data from a website. The discovery phase, i.e. completion of Discovery Documentation, informs all other steps in the Transform phase of the ETL process and assists in determining the methods to utilize along with producing a final output that meets the expectations of downstream users.

Data Mapping and Code Generation

Mapping data is an essential step in establishing the relationship between a source and a target. The mapping is both a roadmap and a set of instructions for how data will be manipulated (moved, aggregated, calculated, transformed). An example of data mapping would be three different source fields (first name, middle initial, last name) are mapped to one target field (Customer Name) in the data warehouse.

After the mapping process is completed, there is a process called Transformation Logic that takes the mapping and translates it into executable code (SQL, Python with Pandas, or a Data Platform GUI).

Execution of transformation and validation post-processing:

During this phase, the rules established in the Data Mapping phase are applied to the data, and it is converted to the target format. Validation of the transformed data is required after completion of transformation execution. Validation checks should be conducted to ensure the data has been accurately transformed, is complete, and retains its integrity. Validation checks generally include:

• Data Count Validation: Make sure the number of records does not change unexpectedly (unless the filtering/aggregation was intentional)
• Data Threshold Checks: Verify that numeric values are within acceptable limits
• Data Referential Integrity Checks: Ensure key relationships between tables remain intact

Common Data Transformation Techniques Explained

Data transformation refers to a group of specialized methods used for vacuuming, cleansing and organizing data in preparation for analysis by improving the overall quality of the data that will be used.

1. Cleansing and Standardization are two primary types of Data Transformation methods. An example of Cleansing would be:
   • Deduplication which involves locating and eliminating duplicate entries.
   • Imputation is a process by which a statistical technique, such as calculating the mean or median, is used to replace (impute) missing data (nulls) according to predetermined business rules.
   • Format Conversion is a means of ensuring all date fields use the ISO 8601 format, all measurements were standardized (e.g., were converted to kilograms), and all character encodings were corrected.
2. Aggregation and Summarization: This involves reducing the data to a lower level of detail (granularity). For example, rather than loading all transactions within a day, instead all transactions could be grouped by day or month or by customer, as in the case of calculating total daily sales per store.
3. Normalization and Scaling: Essential for machine learning, these techniques adjust numerical data to a common range:
   • Normalization (Min-Max Scaling): Scales values to fall between 0 and 1, useful when features have vastly different ranges but a Gaussian distribution isn’t guaranteed.
     $$X’ = \frac{X – X_{min}}{X_{max} – X_{min}}$$
   • Standardization (Z-Score Scaling): Transforms data to have a mean ($\mu$) of 0 and a standard deviation ($\sigma$) of 1. This is ideal when the feature distribution is roughly Gaussian.
     $$X’ = \frac{X – \mu}{\sigma}$$
4. Discretization/Binning: This is the process of converting continuous numerical data into discrete numerical data (categories or bins). An example of this is converting a person’s age (continuous data) to their age category (bins) such as ‘Youth’, ‘Middle-Aged’ and ‘Senior’.
5. Joining and Merging: The process of joining or merging two (or more) datasets on a common key (such as a customer ID) for enhancing the value of the merged dataset. For example: A sales table would be merged with a customer table to associate the sales records of customers with the relevant demographic information of each customer.
6. Pivoting and Unpivoting: There are two types of pivoting (or unpivoting):
   • Pivoting: The rotation of data from ‘long’ format (many rows and few columns) to ‘wide’ format (fewer rows and many columns). Pivoting is commonly used to convert categorical values into columns for ease of analysis.
   • Unpivoting: The opposite of pivoting, this allows you to convert a wide format dataset (fewer rows and many columns) into a long dataset (many rows and fewer columns). Unpivoting is often used for improving the storage of datasets.

Data Transformation and the Modern Data Stack: ETL vs. ELT

ETL (Extract, Transform, Load) has historically been the primary method of moving data. Data was extracted from multiple sources into a staging area, transformed, and finally loaded into a data warehouse (or other destination). Transformation took place prior to loading the data into the warehouse. As a result of using ETL, organizations could store data on a separate server and then use that temporary storage to transform and load data into a production system.

With the emergence of large-scale cloud-based systems such as Snowflake, Google BigQuery, and Amazon Redshift, organizations have begun to take advantage of these new sources of data. These cloud-based systems have the ability to store huge amounts of data and perform extremely fast computations, which has led to the emergence of ELT (Extract, Load, Transform) solutions.

The Significance of the “T” in ELT

In the ELT model, data is first extracted from the source and loaded directly into the destination data warehouse in its raw form. The Transformation step then occurs inside the data warehouse using its native compute capabilities (usually SQL).

Comparison:

Feature	ETL (Traditional)	ELT (Modern)
Transformation Location	Dedicated staging server/engine	Target data warehouse/lake
Speed/Latency	Slower; transformation must finish before data is available	Faster initial load; data is immediately available in raw form
Raw Data Access	Limited; raw data is often discarded after transformation	Full; raw data is stored and available for audit or re-transformation
Scalability	Limited by the staging server’s capacity	Highly scalable; leverages cloud compute resources

The ELT approach is favored in the modern data stack because it offers flexibility, faster ingestion, and the ability to re-transform data as business needs change without needing to re-extract the source data.

Tools and Platforms for Data Transformation

Various software offerings support the massive panorama of transforming data ranging from enterprise-grade platforms to open-source scripting libraries. Choosing the appropriate tool for transformation depends upon the amount of data being processed; how complex the transformations are; and how involved you would like to be with using either ETL (Extract Transfer Load) or ELT (Extract Load Transfer).

1. Code-Based Tools (focusing on ELT):

• dbt (data build tool): The most commonly utilized tool in the current ELT industry. Provides a way for data engineers and analysts alike to develop different data transformations by writing and executing them inside a data warehouse as modular SQL code with version control features.
• Python Libraries (Pandas, PySpark): Have more significant use in creating bespoke transformations for complicated processes in both Data Science and Machine Learning pipelines where particular data manipulation occurs (i.e., for feature generation and scaling).

2. GUI-Based ETL Platforms:

• Informatica PowerCenter, Talend, SAP Data Services: These are examples of legacy/current enterprise-grade ETL platforms that include drag-and-drop graphical user interfaces (GUIs) that allow developers to configure and monitor extensive data flows.
• Cloud Services: An example of managed cloud service(s) with Serverless Integration and Transformation solutions that allow for high-volume scaling in serverless environments such as AWS Glue and Azure Factory around an Data Lake Architecture.

3. Specialized Data Sourcing Tools:

In many cases, the “E” (Extraction) phase requires specialized tools to handle unstructured or semi-structured web data. Using a specialized tool like a web scraper chrome extension can help streamline the extraction of public-facing data, which then needs intensive transformation to be structured and usable.

Leveraging AI for Advanced Data Transformation and Data Sourcing (Introducing Magical API)

The focus of traditional data transformation methods was primarily on getting existing data ready to be transformed by creating a clean and organized state of the data prior to transforming it, whereas modern methods use advanced technology like artificial intelligence (AI) to further assist in this process (e.g., during the most critical first stages of obtaining and preparing the data).

AI-based services are invaluable in enabling the conversion of unstructured/semi-structured data into a complete/structured format and being able to easily access and prepare for analysis.

An example of a platform that specializes in structuring complex,data that has many human components is Magical API, which has many software components that use AI to help with this challenge of processing, scoring, and extracting relevant information (professional/company information) from a multitude of sources quickly (time-efficient) and accurately (accuracy).

Magical API enables organizations (companies) to complete this data transformation process that is one of the major barriers of success in data transformation — converting inconsistent, disorganized, and often dissimilar various forms of data (documents and websites) into organized and combined/structured forms that can be easily accessed and processed using automated processes.

Magical API offers several key features

• Resume Parser: This AI-based application will automatically convert resumes, which are usually very unstructured, into formatted JSON documents. It will produce this standardised output so it can be uploaded and then transformed another time for recruitment analytics; therefore, assuming the creation of thousands of data entry points by manual process eliminates the hundreds of hours of wasted time spent cleaning data. The use of such an application will greatly improve the early stages of acquiring data.
• Magical Data Products: This provides an immediate way of utilizing the LinkedIn Profile scraper and LinkedIn Company Scraper applications to get publicly accessible Professional or Company information quickly. The big benefit of this type of data extraction is that it will create a structured output without the need for significant clean up and mapping, which is typically required when performing web scraping.

By using dedicated AI tools for complex data extraction, companies ensure that clean high-quality data is entered into their transformation process and allows their data teams to concentrate on the aggregation, modeling, and application of business logic versus the tedious task of cleansing the data.

Handling Data Quality and Consistency Issues: A Governance Perspective

Maintaining data quality isn’t just something you do once; you need to continuously commit to Data Quality throughout the entire transformation lifecycle. Having a well-established Data Governance policy is the first step toward maintaining high-quality, consistent data.

Quality Management Strategies: 

1. Clearly Define Quality Metrics: Determine the metric(s) to measure data quality. These include completeness (the percentage of non-null values), validity (how well a data point conforms to established business rules), accuracy (how close to the truth), and consistency (the same across various source systems).

2. Create Automated Quality Checks: Incorporate data validation rules directly into the ETL scripts through CHECK constraints in SQL or tests in dbt. These validations will automatically check and either flag or quarantine records that don’t conform to quality criteria.

3. Create Detailed Traceability and Lineage: You must create, maintain, and document the lineage for each data point from the original source to final destination. This documentation must also include all transformation rules applied. Traceability is vital to audit, troubleshoot, and stay compliant with regulations. Whenever there is a data quality problem, you will be able to quickly identify the point of failure in the lineage.

4. Document Clear Error Handling Procedures: You need to have documented procedures for dealing with rejected records (e.g., should they be corrected before being re-processed, should they be ignored, or sent to a data steward for review? If you have consistent handling of errors, you reduce the risk of data loss, and therefore increase the reliability of your data.

Data Transformation Architectures: The Medallion Layer Approach

In order to improve the quality, lineage, and adaptability of data, ETL/ELT pipelines have transitioned from simple to complex multi-layer architectures. The Medallion Architecture is one of the most popular methods of doing so, as it breaks data into three separate logical layers (implemented in a Data Lake, or Lakehouse):

1. The Bronze Layer, also referred to as the Raw layer, is a form of unmodified source data stored in its original state. The main intent of the Bronze Layer is to provide a historical record and maintain an immutable state; this way, if a company needs to modify its transformation logic, it can always go back to the original source data. Raw data will be loaded with minimal processing (extraction and load) into this layer.
2. The Silver Layer represents the first time any transformation has taken place on the data, and also validates, cleans, normalizes, and structures the data into entity-centric tables (e.g., order, product, customer). This layer acts as a way to remove NULL values, to fix discrepancies between records, and to establish a source of truth for core business entities.
3. The Gold Layer is the consumption or end-user layer. The data is aggregated, summarized, and refined, and will display optimization (in terms of performance) for the purposes of business intelligence, reporting, and machine learning. The data is structured and modeled to suit specific use cases, such as a star schema for sales reporting or a specialized feature set for a recommendation engine.

By using the Medallion concept, companies can perform complicated transformations in a step-wise process, making it easier to track the changes to the data and, as a result, increase the overall quality of the data, making maintenance simpler.

Best Practices for Effective Data Transformation

Effective data transformation doesn’t just rely on techniques; it is the result of disciplined best practices that focus on collaboration, efficiency, and scalability.

1. Prioritize Business Logic: Drive every transformation with an analytical goal or specific business need: Always identify the end-user benefit and value-added when you apply a change in the transfomeration process. By doing this, you’ll avoid adding unnecessary complexity and wasting time on duplicative efforts.
2. Modularize and version-control your transformation logic: The best practice for transformation logic creation is to think about it as if it were software code. This means breaking down complicated transformation logic into smaller, reusable components (also known as ‘dbt models’). So, whenever you create transformation logic, it is a best practice to create a backup of the transformation logic in a version-control system like Git. This allows for tracking transformation-related changes, the ability to collaborate on transformation logic, and the ability to rollback errors when necessary. This is essential to maintaining your data pipeline.
3. Ensure Idempotency: Design equal transformations to be idempotent, meaning that applying the same transformation multiple times to the same input will yield the exact same output each time. This is critical for ensuring reliable operation of pipelines so they can be restarted or run again without generating duplicate or corrupted data.
4. Create Incremental Pipelines: For extremely large datasets, design here the entire historical dataset to be transformed each time there is a new job. Pipelines should be constructed such that only the delta or incremental changes from prior jobs are processed. This will significantly reduce computing time and cost.
5. Documenting every transformation very carefully is also of utmost priority: Keep comprehensive documentation on every transformation being performed including what the transformation is transforming from the source or original system to what it transforms to in the schema that contains it. Keep records of who created/owns that transformation. “Living documentation” will allow smooth transitions from one group to the next without losing the knowledge that was built.

Real-World Examples of Data Transformation in Action

To illustrate the practical applications of data transformation, consider these three real-world scenarios:

1. E-Commerce Customer Analytics

Through their global retailing operations, the retailer combines information from three sources: transaction systems, website clicks, and customer management data (CRM).

• Challenge: How do you reconcile two very different ways of identifying customers? Also, how do you identify customers using click stream data that is identified only by timestamps?
• How to Combine the Data:
  • Joining: The database containing transactions uses customer identifiers in a completely separate format from the CRM. A complex joining process is performed to connect the transaction identifiers to the CRM identifiers.
  • Enrichment: Click stream data is connected with log files that track customer logins and profile them with their respective customer identifiers. This step associates anonymous click sessions with known customer identifiers.
• Aggregation: To determine a Customer Lifetime Value metric (which is a critical business KPI), all purchases for each customer are aggregated and loaded into the Gold layer.

2. Financial Compliance and Reporting

The bank has a requirement to consolidate quarterly financial reporting across a large number of regional branches in order to satisfy regulatory requirements.

• The challenge is that each regional branch uses a different combination of currency codes, date formats, and descriptions of the transactions conducted in that branch.
• The solution involved taking the following steps:
  • Standardizing all currency codes as per ISO standards; converting foreign currencies into one standard reporting currency (USD) based on a fixed exchange rate for that quarter;
  • Cleaning the transaction description field and categorizing it by using regular expressions to align many variations of transaction descriptions to a few standardized transaction categories like ‘ATM withdrawal’, ‘Online payment’, etc.

Common Pitfalls to Avoid and Future Trends in Data Transformation

Regardless of the data transformation tools, even if they are the best, it is possible for teams to end up making mistakes or poor decisions during their implementation of a tool for the purposes of data transformation and cleaning. In order to achieve long-lasting success, teams must be careful to recognize common pitfalls and appropriately prepare for future changing trends within the business.

The following list contains common pitfalls to avoid when implementing tools for data transformation and cleaning:

1. Premature Optimization: Converting or changing data before it is ready for use. Cleaning it before it is required for the specific use case results in wasted time and effort; it is often better to store raw (uncleaned) data and only convert it when it is needed, especially in the case of an ELT Data Pipeline model.
2. Not Maintaining Data Lineage: Losing track of where the data originated and how it has been transformed makes it almost impossible to audit the data or correct errors in a timely manner.
3. Siloed Transformation Logic: Allowing several different teams to calculate the same business rule (e.g., calculating active users) using different logic creates inconsistencies in the reported values and erodes the company’s confidence in the use of the data.
4. Failing to Capture Change Data (CDC): Re-running a complete data load for a table even if only a few data records have changed wastes resources and is not scalable to accommodate future business growth.

Transformation for Real-Time and Streaming Data

Data transformation has been a major change for organisations as data processing is now trending towards more ‘real-time’ based processing where businesses are taking advantage of streaming platforms such as Apache Kafka and stream processing engines such as Spark Streaming or Flink to do data transformation while it is still in motion instead of waiting for it to occur through preplanned batches.

This development makes it possible to do anomaly detection instantaneously, to deliver personalised content instantly, and to make business decisions based on real-time data. The average time elapsed between the occurrence of the event in question and the obtaining of the actual value by the organisation has therefore decreased considerably as a result of implementing this approach.

To take advantage of this trend, organisations must develop state management practices and workflows that focus on event-driven architectures, event-based processing and event-sourcing, as well as continuous transformation.

Conclusion: Building a Strong Data Foundation

Data transformation is more than a technical procedure; rather, it is an essential business strategy or imperative. Data transformation converts disorganized and raw input data into a well-structured, reliable asset that can then be analysed to provide meaningful insights and create better-informed decisions.

Through mastering key techniques, such as standardisation and aggregation, implementing modern architecture, such as extract, load, transform (ELT) and the Medallion layer, and utilising advanced technologies, such as the Linkedin Profile Scraper and Resume Parser, organisations position themselves and their workforce to fully exploit the power of their data. With a structured and well-governed data transformation process, organisations will have a robust and scalable data foundation on which to build during times of ever-increasing analytic challenges.

Common Questions a bout what is data transformation