What is Data Transformation in Data Science?

A data transformation involves converting one format to another for analysis, processing, or integration. Data transformation plays a key role in data management and data integration. Through data transformation, companies can streamline their data management and integration processes and improve their data-driven decision-making. 

The data transformation process must keep up with the growing adoption of cloud-based data storage (67% of enterprise infrastructure is now cloud-based, according to IDC). Therefore, many organizations are seeking data integration processes and data transformation tools that will improve data quality, readability, and enterprise organization.

In this article, we will explore what data transformation in data science and how it contributes to data integration and new data transformation techniques. 

What is Data Transformation?

In data transformation, raw data is converted into a format that can be analyzed and modeled. Data cleaning may include removing errors and inconsistencies, converting data types, and aggregating data to create new attributes and features. In the data science pipeline, data transformation is crucial to ensuring that the data is usable and informative.

Data Science Digital Transformation

A data science digital transformation uses data science to improve an organization’s operations, business processes, and customer experience. Data science digital transformation is a powerful tool for improving performance and creating an organization’s competitive advantage. While data science digital transformation is a crucial component of transforming organizations, it is not a one-size-fits-all process. Depending on the goals and needs of the organization, the best approach will be determined.

How to Transform Data?

A data transformation can enhance analytical and business processes to improve data-driven decision-making. In the first phase of data transformations, hierarchical data should be flattened and data types converted. Analytics software can better analyze data by altering it. Data analysts and data scientists can add additional transformations to the data. Each processing layer must be designed to perform specific operations to meet a recognized business need or technological requirement.

Why Need to Transform Data?

Data transformation can be motivated by various factors. Here are a few of the most popular reasons:

1. The companies’ data must be transformed and aggregated with other available data to ensure a better information analysis. 
2. For example, the data type must be changed when moving your data to a cloud data warehouse.
3. Data that is structured and unstructured needs to be consolidated.
4. To enrich your data, you want to add timestamps. 

Types of Data Transformation

There are several standard methods and techniques for transforming data in addition to the types listed above:

• Translation and data mapping
• Splitting
• Generalization
• Integration
• Discretization
• Manipulation

Data Transformation Tools

Data transformation tools help data scientists and analysts transform raw data into analysis- and modeling-ready formats. Many data transformation tasks, such as cleaning, integrating, and normalizing, can be automated using these tools.

Both commercial and open-source data transformation tools are available. The following are some of the most popular data transformation tools:

• Alteryx
• Apache Spark
• Dataiku
• Informatica PowerCenter
• Matillion ETL
• Talend Open Studio
• Trifacta Wrangler

Data Transformation Techniques

The type of data transformation needed depends on the project’s specific requirements. Many different techniques can be used. The following are some everyday data transformation tasks:

Manipulation

Data manipulation involves changing a dataset’s form to understand its contents better. Combining datasets with different characteristics makes creating a new dataset with more features possible.

Revising

Revising a dataset involves modifying its format so it can be used for analysis. A data set can be restructured and laid out differently by adding new fields, removing unimportant information, or adding new fields.

Separating

The separation of a dataset is the division into smaller subsets based on standard criteria. Focusing on specific parts of your data at any given time means you won’t have to worry about missing information.

Combining/Integrating

Combination and integration are methods for merging multiple datasets that have been separated into individual subsets with separation techniques such as those described above so they can be viewed simultaneously instead of separately (for example, merging multiple tables).

Data Smoothing

In data smoothing, values are averaged across groups to minimize statistical noise from swings in values between groups over time (for example, by taking averages over several years rather than one).

Data Aggregation

In data aggregation, similar values are combined to produce a complete picture of the underlying phenomenon. You can aggregate sales records that include dollar amounts and units sold so that they are represented by just one number: total revenue or total units.

Discretization

Discretization converts continuous variables into categorical variables (for example, age into ages 0-5 or 6-10). By discretizing data, you can use algorithms that do not work with continuous variables (like regressions) but do with categorical variables (like decision trees).

Generalization

A generalization is transforming a set of values into a more generic one. A single value or group of values will represent all your data points. It ensures that all your data points are similar.

Attribute Construction

The process of attribute construction involves creating new attributes for existing data points. Using this approach, you split existing columns into multiple columns to represent different aspects of the original data point. 

Data can be organized using attribute construction, ensuring that each column represents only one feature instead of many rolled-up features.

Benefits of Data Transformation

The value of data could directly affect an organization’s bottom line and efficiencies. Using it helps us understand customer behavior, internal processes, and industry trends. Every organization can collect data, but the challenge is making it worthwhile. The data transformation process enables organizations to reap the benefits of data.

Data Utilization

The data collected is often not utilized because it is not in an appropriate format. Data transformation tools help organizations unlock the actual value of their data since they standardize and improve the accessibility and usability of the data.

Data Consistency

As data is continuously collected from various sources, metadata becomes increasingly inconsistent. Data organization and understanding are consequently challenging. By transforming data sets, we can better understand and organize them.

Better Quality Data

By transforming data, business intelligence can also be derived, which enhances its quality.

Compatibility Across Platforms

Data transformation also facilitates compatibility among data types, applications, and systems.

Faster Data Access

In a standardized format, retrieving data that has been transformed into a standardized format is much quicker and easier.

Conclusion

To summarize, data transformation involves making data more usable and understandable by converting it from one format to another. The data transformation process can improve the data quality, reveal hidden patterns and trends, and enable advanced data analysis. It is essential, however, to plan carefully and execute the data transformation process to ensure that results are accurate and reliable. It is essential for organizations to understand the key elements and challenges of data transformation to be able to prepare their data for analysis and gain valuable insights from it.