What is Data Transformation?

Definition

Data Transformation is the practice of enhancing data to improve its ability to address relevant business questions, including cleansing, filtering, attributing, and structuring to define, construct, and dimensionalize topically, semantically, and consistently for effective querying. Data Transformation is part of a broader set of activities referred to as ETL: Extract, Transform, and Load, which is the process of making source data available, accurate, and relevant for insights creation, including for user access, reporting, and analysis.

Purpose

The purpose of data transformation is to enhance the raw data from its source to provide a consistent, accurate, and understandable set of data that can be queried by a broad array of business insights creators and consumers to address relevant business questions effectively. 

Types of Data Transformation

Basic

• Deduplication: Duplicate records result in incorrect answers to queries, a common data transformation step is removing dupes.
• Format Revision: Date/Time conversions, units of measure, and character set encodings are common for multinational corporations that do business in the US (Imperial) and everywhere else in the world (metric). 
• Cleaning: Null handling, standardization on things like M/F for gender is critical for grouping dimensions, and getting the correct summarization of metrical values.
• Key Engineering: Occasionally, the relationship between data stored in different databases is some function of a key. In these cases, key restructuring transforms are applied to normalize the key elements.

Advanced

• Predication/Filtering: Only move data that satisfies the filter conditions.
• Summarization: A key element of Business Intelligence. Values are aggregated and stored at multiple levels as business metrics. For example, Order Quantity totals by city, state, and country.
• Derivation: Applying business rules to your data that derive new calculated values from existing data, for example, creating a revenue metric that subtracts taxes.
• Splitting: Splitting a single column into multiple columns.
• Data validation: Can be a simple “if/then” calculation or can be a multi-valued assessment, for example, if value A is “foo” and value B is greater than 100, reject this row of data
• Integration: Similar to Key Engineering: Standardize how data elements are addressed. Data integration reconciles different data keys and values for data that should be the same.
• Joining: Standard database joining, and more exotic joining from API or unstructured sources. Consider an example where your customers are stored in Salesforce and your sales data is in a homegrown system. Wouldn’t it be nice to have intelligence across both?

Primary Uses of Data Transformation Software

Data Transformation is used to enhance data from its source so that it is analysis-ready: relevant, business-defined, accurate, and consistent.  Changes to the data involve many different options, including filtering, attributing, and structuring. Major transformation activities are defined below:

Cleansing – Data cleansing involves removing or altering source data that is not necessary, complete, or accurate. Cleansing ensures that data is relevant, useful, and understandable.

Aggregation – Data aggregation is the method for transforming data to conform to definitions across dimensions, such as aggregating data that is sourced at a day level to be aggregated at different levels, such as month, quarter, year-to-date, etc. Aggregation ensures that the data is defined consistently based on how the data is used by the business. Aggregation also improves query speed and performance.

Attributes – Data attributes are definitions of the attributes or standardized groupings of the attributes instantiated in the data to improve analysis relevance and consistency. This can involve creating standard definitions for ranges of values of data, such as defining business-relevant geographical regions, age groups for demographic analysis, or instantiating other standardized segmentation and grouping. Other methods for data grouping include discretization of continuous data: binning, trending, probability distribution fitting, and association/regression.

Metrics / Features – Data Transformation can also involve creating metrics – standardized calculations  (e.g., Key Performance Indicators) that are used for analysis. Metrics used for data science are often referred to as features, and these definitions are also defined and instantiated as part of the data transformation process.

Hierarchies – Data transformation can be the step to define and instantiate hierarchies, which enable logical movement from summary to detail in the data. For example, a geographical hierarchy might involve a sequence such as “where marketed”> “state”> “county” where there is a direct and complete relationship between the values between highest and lowest level of detail. 

Normalization – Data normalization is the process of scaling the data to enable “apples-to-apples” comparisons of metrics and features across multiple data sets, either integrated or analyzed and compared separately. Normalization may also be used to eliminate noise or skews in the source data. Examples of normalization techniques are as follows:

• Min-max normalization: performing a linear transformation on the original data.

• Z-score normalization: Z-score normalization assigns a value to the value associated with its place/position relative to other values using a normal/Gaussian distribution 

• Decimal scaling: normalizing the value of the attribute by standardizing the decimal point placement.

How Data Transformation is Accomplished

The first step of data transformation is mapping. Data mapping determines the relationship between the data elements of two use cases and defines how the data from the source application is transformed before it is loaded into the target. Data mapping produces a set of instructions or metadata that is needed before the actual data conversion takes place.

An example of field mapping: the information in one application might be rendered in upper case, while another use expects it in lower case.

The structure of stored data may also vary between data consumers, requiring semantic mapping prior to the transformation process. For instance, in a multinational company, there may be a transformation required to change the format used to represent dates, or simply to alter the precision of that particular data type.

Overall, there are two ways to approach ETL transformation:

• Multistage data transformation: Extracted data is moved to a staging area where transformations occur prior to loading the data into the warehouse.
• In-warehouse data transformation – Data is extracted and loaded into the analytics warehouse, and transformations are done there. This is sometimes referred to as Extract, Load, Translate (ELT).

In-warehouse transforms are gaining traction, driven by two factors:

1. The increased performance and scalability of the modern analytics database
2. These types of transforms are expressed in SQL, the data manipulation language of choice

AtScale allows for in-warehouse transforms, lately bound and materialized for performance mitigation. We empower data consumers to employ powerful transformations to their data warehouse using a feature we call Composer, which is a visual user interface without a requirement for the user to understand SQL.

Benefits of Data Transformation

Data transformation refines raw, unstructured inputs into analysis-ready formats that power actionable insights. Organizations prioritizing this process gain these strategic benefits:

• Enhanced Data Quality – Automated cleansing eliminates duplicates, errors, and inconsistencies. Standardized formats ensure reliable inputs for analytics across systems and teams.
• Accelerated Decision-Making – Structured data feeds real-time dashboards and AI models. Teams access unified metrics to respond swiftly to market shifts and customer needs.
• Cost Optimization – Streamlined workflows reduce manual data handling and storage redundancies. Automated processes minimize infrastructure costs and error-driven rework.
• Advanced Analytics Readiness – Clean, formatted data integrates seamlessly with machine learning tools. Organizations build accurate predictive models without time-consuming preprocessing.
• Regulatory Compliance – Built-in encryption and access controls protect sensitive information. Automated audit trails simplify adherence to GDPR, HIPAA, and industry standards.
• Cross-Functional Collaboration – Centralized repositories break down departmental silos. Teams align strategies using shared datasets accessible through BI tools like Tableau and Power BI.
• Scalable Infrastructure – Cloud-native transformation adapts to growing data volumes. Enterprises handle seasonal spikes or global expansion without performance bottlenecks.

AtScale’s semantic layer amplifies these benefits by virtualizing transformation workflows, ensuring governed access to analysis-ready data across all business intelligence platforms. This approach turns fragmented data into a cohesive foundation for innovation, efficiency, and competitive agility.

Common Roles and Responsibilities for Data Transformation 

Data transformation requires collaboration across specialized roles to ensure raw data becomes actionable intelligence. Below are key professionals who drive this process and their core responsibilities:

• Data Engineers – Data engineers design and maintain pipelines that extract, transform, and load data from sources like databases and APIs. They optimize ETL/ELT workflows to handle structured and unstructured data while ensuring scalability and reliability.
• Analytics Engineers – These professionals bridge data engineering and data science. They prepare datasets for machine learning models, implement business logic in transformation workflows, and manage integration between BI tools and AI platforms.
• Data Modelers – Data modelers design conceptual, logical, and physical data structures that align with organizational needs. They define schemas, relationships, and transformation rules to ensure consistency across systems.
• Data Governance Specialists – Specialists enforce compliance by classifying sensitive data, managing access controls, and documenting lineage. They ensure transformations adhere to regulations like GDPR and CCPA while maintaining audit trails.
• Automation Architects – Architects develop reusable templates for common transformation tasks and integrate CI/CD tools into workflows. They standardize testing frameworks to validate outputs and minimize manual intervention.
• Technical Architects – These experts design the infrastructure supporting transformation processes. They optimize storage formats (e.g., Parquet) and compute resources to balance performance with cloud costs.
• Data Analysts – Analysts translate business requirements into transformation rules. They define metrics, validate outputs against use cases, and identify gaps in data quality or coverage.

How Data Transformation Works

Data transformation follows a structured workflow to convert raw, unstructured inputs into analysis-ready formats. This process ensures data aligns with organizational standards and analytical requirements through six key stages:

1. Data Discovery and Profiling – Systems identify sources ranging from databases to IoT streams. Profiling assesses data quality, structure, and relationships to define transformation rules.
2. Cleansing and Standardization – Errors like duplicates and null values get removed. Data formats get standardized (e.g., converting dates to YYYY-MM-DD) to ensure consistency across systems.
3. Structural Transformation – Data gets reshaped for analytical use-cases. Tasks include aggregating sales figures by region, pivoting rows to columns, or splitting nested JSON into relational tables.
4. Enrichment and Integration – External datasets merge with internal sources. Location data might append demographic insights, while customer records integrate purchase histories from CRMs.
5. Validation and Quality Assurance – Automated rules check for accuracy, completeness, and compliance. Outliers trigger alerts for review, ensuring only verified data progresses downstream.
6. Loading and Storage – Transformed data lands in warehouses, lakes, or BI tools. Optimized formats like Parquet or Avro improve query speeds while reducing storage costs.

Common Technologies Categories Associated with Data Transformation

Technologies involved with data transformation are as follows:

• Data Engineering – Data engineering is the process and technology required to move data securely from source to target in a way that is easily available and accessible.
• Data Transformation – Data transformation involves altering the data from its raw form to a structured form that is easy to analyze via queries. Transformation also involves enhancing the data to provide attributes and references that increase standardization and ease of integration with other data sources.
• Data Warehouse – Data warehouses store data that are used frequently and extensively by the business for reporting and analysis.  Data warehouses are constructed to store the data in a way that is integrated, secure, and easily accessible for standard and ad-hoc queries for many users.
• Data Lake – Data lakes are centralized data storage facilities that automate and standardize the process of acquiring data, storing it, and making it available for profiling, preparation, data modeling, analysis, and reporting/publishing. Data lakes are often created using cloud technology, which makes data storage very inexpensive, flexible, and elastic.
• Trends / Outlook for Data Transformation

Data Transformation Trends and Future Outlook

Key trends to watch in the Data Transformation arena are as follows:

AI-Powered Automation and Self-Optimization

Machine learning now handles repetitive tasks like schema mapping, anomaly detection, and pipeline optimization. Algorithms analyze historical transformations to predict optimal workflows, reducing manual effort in enterprises adopting these tools. For example, AI models automatically adjust data cleansing rules when source formats change, ensuring uninterrupted analytics.

Real-Time and Event-Triggered Processing

Organizations prioritize instant insights, with streaming platforms enabling transformations on live data from IoT devices, transactions, and customer interactions. Event-driven architectures trigger transformations when specific thresholds are met, such as inventory drops or social media sentiment shifts. This approach reduces decision latency from hours to milliseconds.

Augmented Analytics and Democratization

Natural language processing (NLP) allows non-technical users to define transformation rules via conversational interfaces. Tools auto-generate SQL or Python code from prompts like “segment customers by region and lifetime value,” bridging the gap between business needs and technical execution.

Synthetic Data Generation

Privacy regulations and data scarcity drive the adoption of artificially generated datasets. Synthetic data mimics real-world patterns without exposing sensitive information, enabling safe AI training and scenario modeling. Healthcare providers use this to develop predictive models while complying with HIPAA.

Decentralized Data Architectures

Data mesh principles distribute transformation ownership across domain-specific teams. Marketing, finance, and operations teams design context-aware pipelines while adhering to centralized governance standards, balancing agility with consistency.

Cloud-Native Scalability

Serverless transformation engines automatically scale resources during peak loads, handling terabytes without infrastructure overhauls. Integrated AI services in platforms like AWS SageMaker and Azure ML accelerate feature engineering for machine learning.

Privacy-Preserving Techniques

Homomorphic encryption allows computations on encrypted data, while differential privacy adds noise to datasets during transformation. These methods enable cross-organization collaboration without exposing raw information, which is critical for industries like finance and pharmaceuticals.

Unified DataOps/MLOps Integration

Transformation pipelines now feed directly into model training environments, with CI/CD practices ensuring synchronized updates. Metrics like data drift detection auto-retrain models when input patterns shift, maintaining AI accuracy.

AtScale and Data Transformation

The AtScale semantic layer platform revolutionizes data transformation by providing a universal semantic layer that bridges raw data infrastructure with business-ready insights. Its unified metrics layer automates complex transformations, which enables organizations to define, govern, and deploy analysis-ready data across BI tools without redundant pipelines.

By virtualizing transformations, AtScale eliminates manual coding and ensures consistent definitions for metrics like revenue or customer lifetime value, regardless of underlying data sources. Built-in governance features enforce security policies and compliance standards during transformation, while intelligent caching optimizes query performance on cloud data platforms. Book a demo or contact AtScale to learn more.