What is Data Transformation? Definition, Types and Benefits

Data is continually created, gathered, and analyzed in today’s society. To be useful, this data must be properly processed and formatted so that it can be comprehended and used.

The capacity to convert data from one format to another is an essential skill for everyone who deals with it. In a world where information is gathered and stored in a variety of formats, you must not only be able to do fundamental transformation stages but also understand which kind will best fit your business’s needs at the time. This may appear daunting at first, but don’t worry, because this article will describe what happens throughout a typical data translation procedure.

You can opt for the Data Science Certification Course in Noida, Delhi and other parts of India. Let’s start with an overview of data transformation and then look at the various forms. Then, we’ll go over some of the benefits that firms may get from embracing data transformation.

What is data transformation?

Data transformation is the process of changing data from one format to another.

Transformations often require turning a raw data source into a format that has been cleansed, verified and is suitable for use. Data transformation is critical for data management operations such as data integration, migration, warehousing, and data preparation.

Data transformation is also known as extract, transform, and load (ETL). The extraction step entails identifying and extracting data from the numerous source systems that generate it, and then transferring it to a single repository. The raw data is then sanitized as needed. It is then converted into a target format that may be fed into operational systems, data warehouses, data lakes, or other repositories for use in business intelligence and analytics applications. The transformation may include changing data types, deleting duplicates, and improving the original data.

Data transformation is essential for activities such as data integration, management, migration, warehousing, and data wrangling.

It is also a necessary component for any corporation that wants to use its data to develop timely business insights. As the volume of data grows, businesses must have an efficient means to harness it to efficiently use it for commercial purposes. Data transformation is one aspect of using this data since, when done correctly, it guarantees that data is easy to access, consistent, safe, and ultimately trusted by the intended business users.

The Data Transformation Process

In a cloud data warehouse, the data transformation process is often called ELT (Extract Load Transform) or ETL (Extract Transform Load). With cloud storage costs falling year after year, many teams choose ELT—the distinction being that all data is placed into cloud storage before being converted and added to a warehouse.

The transformation process typically involves six stages:

1. Data Discovery: In the first step, data teams seek to interpret and discover relevant raw data. By profiling data, analysts and engineers may have a better understanding of the necessary modifications.
2. Data Mapping: During this stage, analysts decide how individual fields are adjusted, matched, filtered, merged, and aggregated.
3. Data Extraction: This step involves moving data from a source system to a target system. Structured data (databases) and unstructured data (event streams, log files) are both possible sources for extraction.
4. Code Generation and Execution:Once retrieved and loaded, the raw data must be transformed before it can be stored in a manner suitable for BI and analytics. This is usually performed by analytics engineers, who use SQL/Python to programmatically modify data. This code is run daily/hourly to deliver timely and relevant analytic data.
5. Review: Once implemented, the code must be evaluated and tested to ensure that it is proper and acceptable.
6. Sending: The last phase entails transmitting data to its intended destination. The aim might be a structured database or data warehouse.

Data transformation techniques

A variety of data transformation techniques are used to clean and arrange data before it is stored in a data warehouse or evaluated for business intelligence. Not all of these strategies apply to all sorts of data, and many techniques may be used. Nine of the most often used approaches are:

1. Revising

Revising guarantees that the data supports its intended use by structuring it in the necessary and right manner. It achieves this in a variety of ways.

• Dataset normalization revises data by removing redundancies. The data model becomes more exact and readable while taking up less space. This method, however, requires a great deal of critical thinking, inquiry, and reverse engineering.
• Data purification assures the formatting capabilities of data.
• Format conversion alters data types to guarantee compatibility.
• Key structure translates values with built-in meanings into generic identifiers that may be used as unique keys.
• Deduplication detects and eliminates duplicates.
• Data validation verifies records and deletes those that are incomplete.
• Repeated and unneeded columns can be eliminated to enhance the dataset’s overall efficiency and readability. 

2. Manipulation

This entails creating new values from existing ones or modifying current data using computation. Manipulation is also used to transform unstructured data into structured data for machine learning systems.

• Derivation involves cross-column computations.
• Summarization that aggregates values.
• Pivoting includes transforming column data into rows and vice versa.
• Sorting, sorting, and indexing data to improve search performance.
• Scaling, normalization, and standardization aid in comparing distinct statistics by placing them on a common scale.
• Vectorization is the process of converting non-numerical input into number arrays, which is commonly employed in machine learning. 

3. Separating

This entails breaking down the data values into constituent elements for detailed examination. Splitting is the process of splitting a single column with several values into distinct columns containing each of those values. This enables for filtering based on certain values.

4. Combining/ integrating

Records from several tables and sources are integrated to provide a more comprehensive view of an organization’s operations and functions. It incorporates data from many tables and databases, as well as records from different tables.

5. Data smoothing

This method eliminates useless, noisy, or corrupted data from the dataset. Outliers are removed, making it easier to see trends.

6. Data aggregation

This approach collects raw data from several sources and converts it into a summary format that may be utilized for analysis. One example is raw data that contains statistics such as averages and sums.

7. Discretization

This approach creates interval labels in continuous data in an attempt to improve efficiency and make analysis easier. This approach uses decision tree algorithms to turn huge datasets into category data.

8. Generalization

The notion of hierarchies is used to turn low level data attributes into high level attributes, resulting in layers of consecutive summary data. This aids in establishing clear data snapshots.

9. Attribute construction

To make the mining process easier, this approach generates a new collection of characteristics from an existing set. 

Data Transformation: Benefits and Challenges

Data transformation provides both benefits and problems in the field of data management. 

Data Transformation Benefits

Transforming data may benefit firms in a number of ways. Here are some of the main benefits:

• Better Organization: Transformed data is more convenient for both people and computers to use. Transformation is the process of evaluating and modifying data in order to improve storage and discoverability.
• Improved Data Quality: Bad data has a lot of problems. Data transformation can assist your company prevent quality concerns and limit the potential of misunderstanding.
• Faster Queries: By standardizing data and storing it appropriately in a warehouse, query performance and BI tools may be enhanced, resulting in less friction during analysis.
• Simpler Data Management: Data transformation involves a significant amount of metadata and lineage monitoring. By applying these approaches, teams may greatly simplify data administration. This is especially critical when businesses develop and require data from a wide range of sources.
• Broader Use: Transformation simplifies data management by standardizing and making it more useful.

Data Transformation Challenges

While data transformation technologies provide many advantages, it is crucial to recognize that there are also possible downsides.

• Transformation can be expensive and resource-intensive: While processing and computing prices have decreased in recent years, it is not uncommon to hear about exorbitant AWS, GCP, or Databricks bills. Furthermore, the resource cost in terms of man-hours and salaries is significant: most firms require a team of data analysts/engineers/scientists to extract value from data.
• Contextual awareness is crucial: Extreme mistakes may occur if analysts/engineers converting data lack business context or knowledge. While data observability tooling is improving, there are some problems that are nearly undetected and can lead to misinterpretation of data or wrong business decisions.

Conclusion 

In brief, data transformation may assist you in overcoming data quality concerns, increasing data storage and processing efficiency, facilitating better decision-making, and improving customer happiness. When done correctly, data transformation may be a very effective tool for growing your organization.

There are several techniques to alter data. Data cleaning, mining, and warehousing are some examples of conventional approaches. Each approach has advantages and disadvantages, so selecting the best one for the task is critical.