What is Data Transformation?[+Types, Benefits and Methods]

Data Transformation can be achieved by manually writing scripts or by using an ETL tool. And, with an ETL tool, you can ease the process So, if you are looking for an ETL tool that can automate the transformation of your data, then we are here to guide you.

• In the world of big data where data is streaming in massive volume from multiple sources, Data Compatibility becomes a challenge for you.
• This is where Data Transformation comes into play. You need to transform your data to ensure Data Compatibility.
• Companies with On-premises Data Warehouses and Custom Integration solutions often use a Data Replication process to transform data before loading it to its destination.

What is Data Transformation?

• Data Transformation refers to the process of converting or transforming your data from one format into another format.
• It is one of the most crucial parts of data integration and data management processes, such as data wrangling, data warehousing, etc.
• Data transformation can be of two types – simple and complex, based on the necessary changes in the data between the source and destination. 
• Transformation of data allows companies to convert data from any source into a format that can be used in various processes, such as integration, analysis, storage, etc.
• You can use any ETL tool to automate your transformation or use any scripting language, like Python for manual data transformation. 

Why Need to Transform Data?

1. Companies need to transform their data to make it compatible with other available data so that they can aggregate the information and have a better analysis. 
2. At times, you want to move your data to a new source, such as a cloud data warehouse, which requires a change of the data type.
3. You want to consolidate structured and unstructured data.
4. You want to add more information to your data such as timestamps, to enrich it. 

How to Transform Data?

• Data Transformation can help analytic and business processes run more efficiently and enable improved data-driven decision-making.
• Data type conversion and flattening of hierarchical data should be included in the first phase of data transformations.
• These processes alter data in order to make it more compatible with analytics software. Additional transformations can be applied as needed by Data Analysts and data scientists as distinct layers of processing.
• Each processing layer should be built to do a specified set of operations in order to satisfy a recognized business or technological requirement.
• Within the Data Analytics Stack, Data Transformation serves a variety of purposes.

What are the steps involved in Data Transformation Process?

1. Data Discovery
2. Data Mapping
3. Code Generation
4. Code Execution
5. Data Review

What are the Methods of Data Transformation?

1. Aggregation

• What it is: Aggregation involves summarizing or combining data, typically by grouping it based on specific criteria (e.g., summing sales by region, averaging scores by category).
• When used: Useful when you need to condense large datasets into a more manageable format, often for reporting or analysis.
• Example: Grouping sales data by region and calculating the total sales for each region.

2. Attribute Construction

• What it is: This method involves creating new attributes (features) based on existing ones, often to enhance the predictive power of the dataset.
• When used: Common in machine learning when you need to create derived features that can provide more useful insights for modeling.
• Example: Creating a “profit margin” attribute by dividing “profit” by “revenue.”

3. Discretization

• What it is: Discretization involves converting continuous data (like ages or prices) into discrete categories (e.g., age ranges or price buckets).
• When used: Useful in scenarios where machine learning models or analyses require categorical inputs instead of continuous data.
• Example: Converting ages 20-29 into the category “20-30 years” to simplify analysis.

4. Generalization

• What it is: This method involves reducing the level of detail in the data, making it more abstract.
• When used: Often used to maintain data privacy or to simplify complex data for higher-level decision-making.
• Example: Replacing specific product names with a higher-level category like “electronics” or “furniture.”

5. Integration

• What it is: Data integration combines data from different sources or systems into a unified dataset.
• When used: Essential when working with heterogeneous data sources, such as integrating data from multiple databases into a data warehouse.
• Example: Merging customer information from both online and in-store databases into a single profile.

6. Manipulation

• What it is: Data manipulation refers to the modification or adjustment of data, such as transforming, restructuring, or changing data values for specific use.
• When used: Typically used when data needs to be prepared or structured in a way that makes it more suitable for analysis.
• Example: Reversing the order of dates from descending to ascending, or splitting a full name into first and last names.

7. Normalization

• What it is: Normalization adjusts data into a standard scale, often to bring different ranges of values into a common range (e.g., 0 to 1).
• When used: Important when combining data from sources with different units or scales to ensure fair comparison.
• Example: Scaling test scores between 0 and 1 to make comparisons easier across different subjects or datasets.

8. Smoothing

• Example: Using a moving average to smooth out sales data over time and make long-term trends more visible.
• What it is: Smoothing is the process of removing noise or fluctuations from data, making trends or patterns clearer.
• When used: Useful in time-series data, where smoothing helps reduce the impact of outliers or short-term variations.

What are the Techniques of Data Transformation?

• Revising: Data is revised to ensure that values are correct and organized in a way that makes sense for their intended use.
  • Data values are converted for formatting compatibility during data cleansing.
  • Incompatible characters are replaced, units are converted, date formatting is changed, and other data types are altered during format revision or conversion.
  • Key restructuring transforms values with inherent meanings into generic identifiers that can be used as fixed, distinctive keys across all tables.
  • Deduplication is the process of locating and eliminating duplicate records.
• Computing: Calculating rates, proportions, summary statistics, and other significant figures is a frequent application of computing new data values from existing data.
  • Simple cross-column calculations are included in the derivation.
  • Summarization is the process of generating summary values using aggregate functions.
  • By pivoting, column values become row values and vice versa.
  • Records are organized in some order through sorting, ordering, and indexing to enhance search performance.
• Separating: By breaking down values into their component parts, we can separate them. Because of idiosyncrasies in data collection, data values are frequently combined within the same field, but they may need to be separated for more granular analysis.
  • For fields with delimited values or to convert a column with multiple possible categorical values into dummy variables for regression analysis, splitting a single column into multiple columns is a common practice.
  • Data is excluded through filtering based on specific row values or columns.
• Combining: Bringing together data from various tables and sources to create a complete picture of an organization’s operations is a common and crucial task in analytics.
  • Data from different tables are connected through joining.

Learn about Best Data Transformation Tools and find effective ways to transform data.

What are the Types of Data Transformation?

1. Traditional or Batch Data Transformation

• Data transformation uses batch processes traditionally. It involves executing code and implementing transformation rules on your data in a data integration tool.
• Micro batch refers to the process of transforming and delivering data with low latency. The process is quick and easy.
• Companies have been using traditional data transformation for decades. But, it has a lot of limitations such as the involvement of personnel, cost, speed, etc. which reduces its efficiency.

2. Interactive Data Transformation

• Interactive transformation allows companies to interact with datasets through a visual interface, such as to understand data, correct and change data through clicks, etc.
• In Interactive data transformation, all the steps are not followed linearly, and it doesn’t require specific technical skills. 
• It shows the user patterns and anomalies in the dataset to reduce errors in the data.
• There is no need for a developer in interactive data transformation, which reduces the time required to prepare and transform data. It gives business analysts the power to control and manage their datasets.

What are the Benefits of Transforming Data

1. Improved Data Quality: Cleans and standardizes data, reducing errors and inconsistencies.
2. Better Decision-Making: Transformed data is more accurate, leading to reliable insights.
3. Enhanced Data Integration: Makes it easier to combine data from different sources.
4. Optimized Data Analysis: Structured data is more efficient for analysis and reporting.
5. Increased Scalability: Supports smoother processing of large data sets for future growth.
6. Compliance and Security: Ensures sensitive data is formatted properly for regulatory requirements.

What are the Challenges of Transforming Data?

1. Data Quality Issues: Inconsistent, incomplete, or inaccurate data can complicate transformation efforts.
2. Complexity of Data Sources: Integrating diverse data formats and sources can be challenging.
3. High Costs: Data transformation can be resource-intensive, requiring significant time and computing power.
4. Scalability Concerns: Handling large volumes of data during transformation can strain infrastructure.
5. Data Loss or Corruption: Errors in the transformation process can lead to data loss or corruption.
6. Maintenance and Updates: Keeping transformation processes up to date as data sources evolve can be difficult.

Additional Resources on Data Transformation

• Explore more in Data Transformation Tools
• How to Transform Data in Power BI
• Read more about Snowflake Data Transformation

Conclusion

• In this blog, you have learned about Data Transformation in detail.
• Transformation of data allows you to convert your data from various sources into the desired format. With data transformation, you can refine your data for data integration and data management.

Frequently Asked Questions