Data transformation helps businesses make raw data more useful for data analysis and decision-making. It cleans and restructures raw data to boost its quality and relevance. The resulting data is more accessible and consistent, making it reliable enough for reports, strategic planning and business analytics. It's a process that ultimately transforms data into a vital business asset. 

Data transformation unlocks the full potential of data. This process improves data usability, enabling you to extract valuable business intelligence. To tap into the full value of your data, you’ll need to consolidate it in a central repository for thorough analysis.

ELT software facilitates this process by extracting data from multiple sources, depositing it into a data warehouse or data lake and then formatting it appropriately for analysis. This streamlined process ensures your data is ready for strategic use.

What is data transformation?

Data transformation converts raw data into analysis-ready data models. Think of these data models as strategic frameworks that structure data into usable formats such as metrics and reports. As a result, they enable organizations to monitor, analyze and optimize their performance in pursuit of specific business goals. 

While the data transformation phase increasingly occurs during the extraction, loading and transformation (ELT) process, it's essential not to overlook the traditional ETL process. The ETL process — standing for Extract, Transform, Load — extracts data from various sources, transforms it to fit operational needs and loads it into a target system. ETL tools handle these tasks efficiently.

The shift to ELT, where transformation occurs within the database itself, represents an evolution in data strategy. This approach optimizes the data for analysis directly where it lives, leveraging the power and scalability of modern data warehouses.

Enhancing the data transformation process

Data professionals are increasingly turning to dbt (data build tool) to refine their data transformation processes. They can use dbt to implement advanced transformations directly within their data warehouses. It also provides them with version control, which tracks changes and maintains data consistency over time. This dbt approach supports SQL-based analytics and agile software practices, further optimizing data usage.

Fivetran streamlines the initial stages of this workflow by automating the data extraction and loading processes. This process simplifies the data transfer from the source system to the data warehouse. Once the data reaches the warehouse, it can be efficiently transformed using dbt, which integrates seamlessly with Fivetran to enhance data operations. 

Data transformation use cases

In business, the data transformation process prepares data for a range of use cases, including:

• Business intelligence: By transforming data for analytical reports, companies gain critical insights into their performance, customer behavior and market trends. 
• Marketing optimization: Through the analysis of transformed data, businesses can assess the effectiveness of marketing campaigns, optimize ad placements and deepen their understanding of consumer engagement across various channels. 
• Machine learning models: Preparing datasets for machine learning by cleaning, normalizing and engineering features to train models more effectively. Useful in predictive analytics, anomaly detection and automation processes. 
• Regulatory compliance: Data transformation ensures compliance with legal and regulatory standards. This process includes anonymizing personal data to meet privacy regulations like GDPR and generating reports for regulatory oversight, safeguarding both the business and its customers.
• Real-time data monitoring: Transforming streaming data enables real-time monitoring applications crucial for sectors such as network security, banking and manufacturing. This capability allows for immediate analysis and response, enhancing security and operational quality control.

In these scenarios, data transformation makes data more accurate, consistent and complete. This fine-tuning lets businesses customize it to fit their specific needs. It also makes the data easier to use, which improves business intelligence and ensures compliance with regulations. 

The benefits of data transformation 

Any business that views data transformation as a mere technical necessity risks missing out on its deeper benefits. Forward-thinking companies view it as a strategic asset that significantly improves data utility and business operations. Here are some of the key benefits:

• Streamlined data management: Transforming data simplifies management tasks by standardizing formats and structures across sources. This uniformity allows for easier data maintenance and updates, reducing the complexity and cost of data management.
• Enhanced analytical power: By refining raw data into a more digestible and relevant format, data transformation empowers analysts and data scientists to extract more meaningful insights. This transformed data is better suited for deep analytics that can drive strategic decisions. 
• Increased business agility: With data transformation, businesses can adapt more quickly to market changes or internal demands. The ability to rapidly integrate and analyze new data types or sources means that companies can respond with agility to new opportunities or threats.
• Better decision making: High-quality, well-structured data leads to more accurate data analytics and reporting. This clarity supports more informed decision-making at all levels of the organization. It also means business strategies are based on solid data rather than intuition or flawed information.
• Operational efficiency: Automated data transformation processes reduce manual data handling, minimizing errors and freeing up valuable time for data teams to focus on more strategic tasks rather than data wrangling. It's also helpful for handling large volumes of data efficiently.
• Regulatory compliance: Making sure that data transformation processes align with compliance requirements protects the organization against data breaches and penalties. Properly transformed data maintains the integrity and confidentiality required by regulatory standards.

The benefits of data transformation stretch far beyond the IT department, impacting every facet of an organization. It not only makes data more usable and valuable but also supports broader organizational goals by enabling more effective, data-driven strategies.

Exploring the data transformation process

The data transformation process is a systematic approach to preparing raw data for analysis and storage. This data preparation makes data accurate, consistent and useful for business needs. Here's an overview of the steps involved from first identifying data to its eventual storage: 

Data Identification

Identifies the data to collect and transform. This process could involve locating various data sources such as internal databases, spreadsheets and external data services. Understanding the source, nature and structure of the data is crucial for planning the subsequent steps.

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Data Extraction

Pulls data from its original sources, which may require connecting to different databases, APIs or file systems. The extraction process must be done carefully to maintain data integrity.

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Data Cleaning

Scrubs data to correct inaccuracies, fill missing values, remove duplicates and handle outliers. This step must occur before data transformation to address inaccuracies, inconsistencies or missing values. 

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Data Transformation

Transforms data into a format or structure that suits the analytical needs. This step involves data normalization, data aggregation and encoding. Transformations also include changing data types, merging fields, splitting columns and creating calculated fields.

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Data Integration

Maps data to ensure data from multiple sources aligns properly within the data warehouse. This process resolves data conflicts where different sources may have different formats, combining sources into a comprehensive view.

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Data Loading

Loads transformed and integrated data into a target database, data warehouse or data lake. Optimize this step in ETL to handle large volumes of data efficiently. As ELT implies, this data loading process comes earlier in the data migration process.

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Data Validation and Testing

Validate and test the data to ensure that the transformation processes have been executed as expected. This step includes checking data accuracy, completeness and reliability. Validation ensures that the data meets the business and analytical requirements.

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Data Storing and Archiving

Stores the transformed data so it’s accessible for future use. This process includes considerations for data security, backup and recovery. Additionally, some data may be archived if not needed for immediate analysis but may be useful for regulatory purposes.

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By adhering to these steps, companies can effectively ensure that their data transformation efforts are successful. This process produces high quality data that is ready for analysis. These steps also position businesses to make strategic decisions supported by reliable data.

Data transformation techniques 

Data transformation involves various techniques that manipulate and reformat data, making it suitable for analysis, reporting and decision-making. Here are some common data transformation techniques used across different industries:

Database normalization

A method for structuring data to eliminate redundancies and simplify relationships by dividing a database into "normal" forms. This technique decreases storage requirements and improves the accessibility of the data model for analysts.

Data formatting

Data formatting streamlines data by replacing incompatible characters, converting units and dates and altering data types. This process improves data quality by correcting format inconsistencies and errors prior to any data transformation. Data cleansing, which adjusts data values for formatting compatibility, is a key component of this technique.

Key restructuring

Key restructuring involves creating generic identifiers from values that inherently contain specific meanings, allowing them to serve as consistent, unique keys across different tables. This process maintains data integrity and confirms reliable joins in database operations. It also establishes clear links between datasets, which simplifies data management and boosts query performance.

Deduplication

Deduplication involves the identification and removal of duplicate records within a dataset. This process prevents redundancy, making data more accurate and reliable. 

Data validation

Data validation assesses the validity of records based on their completeness, typically by identifying and excluding incomplete entries. Validation checks exist for completeness, correctness and compliance of data. This process may include statistical tests to ensure data integrity and accuracy.

Derivation

The derivation process creates new data values from existing ones through specific computational formulas or algorithms. This technique can involve simple operations like calculating the sum or average of columns, or more complex functions such as extracting new insights by combining multiple data fields. It even includes log transformations to normalize data distributions, making them more suitable for analysis.

Pivoting 

Pivoting reorganizes data from a long format to a wide format or vice versa. This process transforms specific column values into column headers, effectively reshaping the table structure. It’s particularly useful for data visualization and when preparing data for certain types of analysis that require data to be structured differently.

Scaling, standardization and normalization 

These techniques adjust numerical data to a uniform scale. For instance, Z-score normalization standardizes data points based on their distance from the mean, measured in units of standard deviation. 

These methods also make it possible to compare different numerical values by bringing them onto a common scale, leading to more insightful analyses. They are also particularly useful for preparing continuous data for uniform analysis.

Vectorization 

Vectorization transforms non-numerical data into numerical arrays, which is useful for various machine learning applications. Fields like natural language processing (NLP) and image recognition also use this technique, where it enables machine learning algorithms to process and analyze data efficiently.

Separating

Separating breaks data values into their individual components. The collection process often results in data amalgamation; detailed analysis requires separation. This technique includes splitting a single column into multiple columns. 

Data splitting is useful for handling fields with delimited values or transforming a column that includes several categorical values into separate dummy variables. It’s widely used in regression analysis.

Joining and merging

Joining involves linking data across tables, while merging, also known as appending or union, combines records from different tables. Merging data often involves using common level data attributes to combine records from different tables. A practical example would be merging sales and marketing data to comprehensively map the customer journey. 

Filtering

Data filtering removes data based on specific criteria related to row values or columns. This process eliminates irrelevant or unnecessary information, leading to more focused and accurate analysis.

Attribute construction

Attribute construction, also known as feature engineering, is the process of creating new attributes or features from existing data. This technique improves the predictive power of machine learning models by deriving additional relevant information not present in the raw data. In a date field, one might extract the day of the week, month or year as separate features to provide more detailed inputs for analysis. 

Similarly, new variables created specifically for linear regression models allow for better predictions and understanding of relationships within the data. These new variables enhance both model accuracy and the depth of insights into data patterns.

Data anonymization

Data anonymization refers to the process of protecting personal or sensitive information by removing or obfuscating identifiers that connect an individual to stored data. This technique improves compliance with privacy laws and regulations, such as GDPR or HIPAA. Common methods of anonymization include data masking, pseudonymization and generalization.

Organizations benefit from these advanced data transformation techniques, as they shape their data in ways that help them make smart decisions. Integrating these techniques into modern data platforms keeps their data accurate and useful throughout its lifecycle.

How Fivetran enhances data transformation strategies

Fivetran simplifies the modern data transformation challenges that data professionals often face. It schedules data extracts from your source data and automatically manages schemas and different data types, tailoring them to your specific destination. This automation results in tables that are ready for immediate querying in your data warehouse or data lake.

Additionally, Fivetran provides robust data transformation tools with features like column blocking and hashing so you can exclude or anonymize sensitive data. It also boasts an extensive library of data models that integrate seamlessly with supported data sources. Business users can easily leverage these tools without needing deep technical knowledge. 

For a more comprehensive exploration of this topic, download The Ultimate Guide to Data Transformation.

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