Data Transformation and Data Anonymization

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Data Transformation and Data Anonymization

In this article, I am going to discuss Data Transformation and Data Anonymization in Data Science. Please read our previous article, where we discussed Data Formats, Data Quantity, and Data Quality. 

Data Transformation

The data transformation process’s goal is to extract data from a source, convert it to a usable format, and deliver it to a destination. This entire procedure is referred to as ETL (Extract, Load, Transform). During the extraction phase, data is identified and extracted from multiple locations or sources and stored in a single repository.

Data extracted from the source location is frequently raw and in its original form is unusable. The data must be transformed to overcome this barrier. This is the step in the ETL process that gives your data the most value by allowing it to be mined for business intelligence. A number of steps are taken during transformation to convert it into the desired format. Data must be cleansed in some cases before it can be transformed. Data cleansing is the process of preparing data for transformation by removing inconsistencies or missing values. Following the cleansing of the data, the transformation process proceeds as follows:

1. Data Discovery: The first step in the data transformation process is to identify and comprehend the data in its original format. Typically, this is accomplished with the assistance of a data profiling tool. This step assists you in determining what needs to be done to the data in order to convert it to the desired format.
2. Data mapping: This is the process of mapping data. The actual transformation process is planned during this phase.
3. Generating code: To complete the transformation process, a code to run the transformation job must be created. These codes are frequently generated with the assistance of a data transformation tool or platform.
4. Executing code: The planned and coded data transformation process is now in action, and the data is converted to the desired output.
5. Review: Data that has been transformed is checked to ensure that it has been formatted correctly.

Benefits of Data Transformation

Businesses and organizations across all industries recognize that data has the potential to increase efficiencies and generate revenue, whether it is information about customer behaviors, internal processes, supply chains, or even the weather. The challenge here is to ensure that all of the data collected can be used. Companies can reap massive benefits from their data by utilizing a data transformation process, such as:

1. Getting the most out of data: According to Forrester, between 60% and 73% of all data is never analyzed for business intelligence. Companies can use data transformation tools to standardize data in order to improve accessibility and usability.
2. Improved data management: Inconsistencies in metadata can make it difficult to organize and understand data generated from an increasing number of sources. Data transformation refines metadata to make it easier to organize and comprehend the contents of your data set.
3. Performing faster queries: Transformed data is standardized and stored in a source location where it can be retrieved quickly and easily.
4. Improving data quality: Because of the risks and costs associated with using bad data to obtain business intelligence, data quality is becoming a major concern for organizations. Data transformation can reduce or eliminate quality issues such as inconsistencies and missing values.

Data Anonymization

The process of protecting private or sensitive information by erasing or encrypting identifiers that link an individual to stored data is known as data anonymization. You can, for example, run Personally Identifiable Information (PII) such as names, social security numbers, and addresses through a data anonymization process that retains the data while keeping the source anonymous.

Even if you clear identifiers from data, attackers can use de-anonymization methods to retrace the data anonymization process. De-anonymization techniques can cross-reference the sources and reveal personal information because data typically passes through multiple sources, some of which are accessible to the public.

Techniques for Data Anonymization

Data masking: It is the process of concealing data by changing its values. A mirror version of a database can be created and modified using techniques such as character shuffling, encryption, and word or character substitution. You can, for example, replace a value character with a symbol such as “*” or “x.” Data masking prevents reverse engineering or detection.

Pseudonymization: It is a data management and de-identification method that replaces private identifiers with fictitious identifiers or pseudonyms, such as replacing “John Smith” with “Mark Spencer.” Pseudonymization maintains statistical accuracy and data integrity, allowing the modified data to be used for training, development, testing, and analytics while maintaining data privacy.

Generalization: The deliberate removal of some of the data in order to make it less identifiable. Data can be transformed into a set of ranges or a broad area with defined boundaries. You can remove the house number from an address, but you must not remove the street name.

Data swapping: It is also known as shuffling and permutation, which is a technique for rearranging dataset attribute values so that they no longer correspond with the original records. Swapping attributes (columns) containing identifier values, such as date of birth, may have a greater impact on anonymization than changing membership type values.

Data Perturbation: It modifies the original dataset slightly by rounding numbers and adding random noise. The value range must be proportional to the perturbation. A small base may result in weak anonymization, whereas a large base may reduce the dataset’s utility. For example, because it is proportional to the original value, a base of 5 can be used to round values such as age or house number. You can multiply a house number by 15 and the value may still be valid. However, using higher bases, such as 15, can make the age values appear fabricated.

Here, in this article, I try to explain Data Transformation and Data Anonymization and I hope you enjoy this Data Transformation and Data Anonymization article.