Guide to Data Transformation: What It Is, Steps, Techniques

A business accumulates vast amounts of data from various, often disparate, sources, such as your content management system (CMS), customer relationship management (CRM) program, and Human Resources (HR) systems. These tools can’t “talk” to each other, resulting in duplicate information and unrealized potential. So, after extracting the data from each program, the data arrive in staging in an incoherent and unstructured format — each program’s language without its translator.

Data transformation manipulates this raw data into one universal language, revealing valuable insights that can aid decision-making and lead to innovation.

What Is Data Transformation?

Data transformation is the comprehensive process of converting data from one format, structure, or value to another. Transforming data into a more usable and interpretative format lets businesses unlock the full potential of all organizational data, enabling advanced analysis, machine learning projects, and more efficient data storage practices.

Data transformation is an important step in data management that cleans, structures, and enriches data, making it more suitable for data analytics. At its core, data transformation involves numerous operations, such as:

  • Filtering data
  • Sorting data
  • Aggregating data
  • Merging data

These operations ensure data meets specific requirements and standards.

One of the most common data transformation methods occurs within a process called ETL, which stands for Extract, Transform, Load. ELT (Extract, Load, Transform) is the same except the transformation work is performed after the data is loaded into a suitably scalable database platform.

Data transformation enhances data quality and accessibility, streamlines workflows, and ensures that data is accessible by the applications that require that data to perform necessary tasks such as analytics and reporting.

Importance Of Data Transformation

Data transformation is an important key that unlocks the organizational gems hidden within raw data. The importance of data transformation transcends technical requirements — it serves as the foundation for data-driven business intelligence and decision-making.

Mastering data transformation:

  • Enhances the value and accessibility of data
  • Propels businesses toward unprecedented productivity
  • Transcends data analytics
  • Offers insights into strategy and task execution

Let’s look at a data transformation example for a global retailer.

A global retail corporation operates in several countries. The company collects vast amounts of data every day from multiple sources, including:

  • Online sales
  • In-store transactions
  • Customer feedback
  • Supply chain operations

Before transforming, this data is heterogeneous, fragmented, and siloed within its programs. In this state, its potential to inform strategies or improve customer experiences remains untapped.

With data transformation techniques, the retailer can:

  • Aggregate the data
  • Standardize its formats
  • Cleanse inaccuracies
  • Create a more holistic view of operations and market dynamics

Unifying the data landscape helps the retailer retain a competitive advantage and drive business growth.

Businesses today must consider machine learning and artificial intelligence, so data transformation takes on an even greater significance. AI algorithms and models — the very engines driving predictive analytics and automation — require high-quality, structured data to function effectively.

Benefits Of Data Transformation

Understanding the advantages of data transformation is essential for programmers and engineers looking to use data efficiently and effectively. The benefits of data transformation are extensive, impacting all areas of data analytics and decision-making. By converting data into a more usable format, businesses can get valuable insights, simplify operations, and improve overall performance.

Some of the key benefits of data transformation include:

  • Improved data quality: High-quality data is instrumental in minimizing errors in analytics and decision-making processes. Using higher-quality data for analysis increases the reliability of outcomes.
  • Data integration: Data transformation is a crucial part of the integration process that ensures data from disparate sources can be brought together coherently for easier analysis and visualization.
  • Decreased data duplication: Data transformation helps reduce and remove redundant, irrelevant, and duplicate data.
  • Richer data analytics and insights: Structured, clean data results in more effective analytics and helps data scientists and business analysts uncover patterns, trends, and correlations previously hidden.
  • Advanced data-driven strategies: Data transformation lays the foundation for employing machine learning, predictive analytics, and other sophisticated processes.
  • Enhanced regulatory compliance: For most businesses, complying with data protection and privacy laws is non-negotiable. Data transformation processes should include anonymization and encryption of sensitive information to help maintain compliance and avoid legal risks.
  • Greater business decision-making: Access to high-quality, integrated data helps businesses make faster, more informed decisions based on comprehensive and accurate information.

So, what are the steps in the data transformation process?

7 Data Transformation Steps & Process

Transforming data requires a systematic approach so the initial raw data can become high-quality information that is ready for analysis. 

The seven steps of the data transformation process are:

1. Identify data source and requirements

The first step involves pinpointing where your data is coming from and understanding the format, structure, and quality of this initial dataset. Equally crucial is identifying what the data will be used for once transformed - whether for analytics, reporting, or further data processing tasks. This dual understanding lays the groundwork for the transformation process.

2. Data assessment and cleansing

Before any transformation takes place, it’s vital to assess the quality of your data. This involves identifying any inaccuracies, inconsistencies, or missing values that may hinder analysis. Cleansing involves rectifying these issues, whether it’s through removing duplicates, correcting errors, or inputting missing values.

3. Data structuring

Data often comes in unstructured or semi-structured formats that are not immediately suitable for analysis. Structuring, or data modeling, involves organizing data into a more analysis-friendly format, typically involving the creation of tables, columns, and relationships between different data sets.

4. Data enrichment

This step is about enhancing the data’s value by appending additional information or attributes from other data sources or by creating new derived attributes from the existing data. It’s crucial to emphasize that enrichment should add context or insights, making the data more useful for specific analyses or decisions.

5. Data transformation

At its core, this step involves applying a series of functions or operations on the data to convert it into the desired format or structure. This can include aggregating data points, normalization (scaling of data), creating calculated fields, and more. The goal is to tailor the data to meet the specific needs identified in the first step.

6. Data validation

After transformation, it’s essential to validate the data to ensure that the process has been successful and that the output meets the predefined requirements. This involves checking for data integrity, and consistency, and making sure no data has been lost or improperly altered during transformation.

7. Storage and accessibility

The final step involves storing the transformed data in a dedicated storage system (like a database or data warehouse) and ensuring it’s accessible for analysis, reporting, or further processing. Considerations around security, data governance, and compliance with regulations should also be addressed at this stage.

Taking these steps with care can ensure your data transformation efforts are effective and result in high-quality, actionable data that’s ready for a broad array of applications from analytics to reporting.

Data Transformation Types & Techniques

Data transformation techniques are tools in our collective belt that help us wrangle raw data so it can yield actionable insights. Here are the top 10 data transformation techniques:

  1. Filtering - Selects only the pieces of data that meet specific criteria, reducing the data set size based on condition(s).
  2. Aggregation - Combines multiple data entries into summarized results, typically using operations such as sum, average, count, max, and min.
  3. Mapping - Applies a function or a set of rules to each data item to transform its value, such as converting temperatures from Celsius to Fahrenheit.
  4. Normalization - Adjusts numerical columns in the dataset to a common scale without distorting differences in the ranges of values, often used in machine learning preprocessing.
  5. Deduplication - Removes duplicate records from the dataset to enhance the quality of the data, often necessary in scenarios where data entries are repeated.
  6. Pivot/Unpivot - Pivot transforms data from long format to a wide format, aggregating data, while Unpivot does the reverse, turning columns into rows, thus increasing detail.
  7. Joining - Combines data from multiple sources based on a related column to form a single dataset, such as merging customer and order data on a customer ID.
  8. Splitting - Divides a column into multiple columns based on a delimiter, useful for splitting full names into first and last names, for example.
  9. Type Conversion - Alters the data type of a column from one type to another (e.g., converting string to date type or integer to float), often necessary to align data types before analysis.
  10. Encoding - Transforms categorical data into numerical format to be easily processed by algorithms, includes methods like one-hot encoding or label encoding.

Data transformation is an art as much as it is a science. It requires technical skills, creativity, and a deep understanding of both the data and its place in the business context. Understanding the specific challenges and requirements of any data transformation project can help your team produce high-quality, actionable data. Remember, today’s decisions build tomorrow’s innovative solutions.

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