Data Transformation

Data transformation refers to a process that involves transforming raw data into a particular format that suits modeling and analysis. The main objective of converting data into a specific format is to ease data mining and facilitate the retrieval of useful insight and strategic information.

This process is part of most data management and integration tasks, for example, data warehousing and wrangling. Besides facilitating data integration, it enhances data quality, ensuring that the machine learning algorithm can utilize it. Without it, artificial intelligence cannot make accurate forecasts. This process has different types, such as structural, aesthetic, and constructive.

• Data transformation refers to a procedure that involves cleansing, structuring, and converting data into a certain format that enables one to analyze and take crucial decisions, driving a business’s growth.
• Some popular types of this process are structural, destructive, constructive, and aesthetic.
• Data analysts utilize various techniques, such as smoothing, generalization, aggregation, and manipulation, to carry out this process.  
• This process has multiple limitations. For example, it is resource-intensive and expensive. That said, it offers different benefits, such as enhanced data quality, better organization, and faster queries.

Data transformation refers to the process of transforming the format of the source system’s data to the format necessary for the destination system. It improves the efficiency of a business’s analytical processes and operations. Moreover, it allows organizations to make data-based decisions to propel business growth.

The data transformation process involves the following steps:

1. Data Discovery: In this phase, data analysts put in efforts to spot and comprehend data in the source format. They utilize different data profiling tools for data discovery. This step allows data analysts to determine what they must do to obtain data in their desired format.
2. Data Mapping: This step involves analysts carrying out data mapping to find out how the individual fields are mapped, joined, aggregated, filtered, and modified. This step is essential for multiple processes, and one must remember that even a single misstep might lead to inaccurate analysis, which can have a negative impact on the entire business.
3. Data Extraction: In this phase, analysts extract data from the original source, which may include streaming sources, for example, customer log files, and structured sources, like databases.
4. Code Generation: Analysts have to create a code for completing the transformation post-extraction of data. In many cases, analysts generate codes utilizing data transformation tools or platforms. Then, the analysts execute the code hourly or daily to offer correct and timely data.
5. Review: In this stage of the data transformation process, analysts ensure whether the format is correct.
6. Sending: In the last step, analysts send the data to the target destination, for example, a data warehouse or any database that handles unstructured and structured data.

The different types of this process are as follows:

• Constructive: In this case, the process involves copying, adding, or replicating data. 
• Aesthetic: This process involves standardizing the data to fulfill certain requirements.
• Destructive: In the case of this type of process, the system removes fields or records.
• Structural: In this case, the reorganization of the database occurs by combining, moving, or renaming columns.

The techniques of data transformation in data analytics are as follows:

• Aggregation: Analysts utilize this technique to accumulate raw data from different sources and store it in one format for correct analysis and accurate reports. When organizations accumulate substantial data volume, using this technique is crucial.
• Smoothing: This technique removes meaningless or distorted data from a dataset. Moreover, it detects minor changes to the data for identifying certain trends and patterns.
• Generalization: The generalization technique transforms attributes of low-level to high-level ones, using concept hierarchies. This leads to the creation of a clear snapshot of data.
• Normalization: This technique transforms the data to ensure that attributes remain within a certain range for data mining applications and improved extraction efficiency.
• Attribute Construction: The attribute construction technique enables analysts to organize data sets through the creation of new attributes from existing data sets.
• Manipulation: This technique involves changing or altering the data to improve its readability and organization.

Let us look at a few data transformation examples to understand the concept better.

Example #1

Algorics, a leader in biostatistics, clinical data management, data standardization, and statistical programming, announced the launch of MetaVate’s latest version. This solution will change the process of data transformation in clinical research by utilizing its AI or artificial intelligence-enabled automation and data transformation capabilities.

It features an inbuilt metadata library framework that adheres to the Clinical Data Interchange Standards Consortium standards and supports the conversion of clinical data from any source system to the destination system. This helps the sponsors automate the process of data standardization.

Example #2

Abacus Insights obtained the HITRUST r2 certification for the data transformation platform hosted by the organization at AWS or Amazon Web Services. The certification showed that the business achieved the gold standard for regulatory industry requirements, regulatory compliance, and risk management.  

The HITRUST evaluation is a demonstration of expanded practices in addition to compliance factors, unique risk factors, and a more detailed approach to assessment, including policies, implementation, and processes.

Abacus Insights is the only organization offering data solutions and a data transformation platform developed particularly for health plans. The company makes data related to healthcare useable by making sure that it meets certain dimensions — timely, versatile, accurate, relevant, accurate, use case and application agnostic, and complete.

Some key advantages of data transformation in data analytics are as follows:

• This process can help businesses eliminate quality issues concerning data and minimize the chances of misinterpretation.
• Individuals can use the abovementioned techniques to simplify data management, which is crucial as organizations expand and accumulate data from various sources.
• This process standardizes data and makes it more usable. This makes it easier for businesses to make the most of the available data.
• Standardization of data and its proper storage in a data warehouse allows BI (business intelligence) and query speed optimization, which minimizes data friction.
• The process involves changing and evaluating data to optimize discoverability and storage.

• This process can be resource-intensive and expensive. One must remember that the cost depends on software, tools, and infrastructure utilized to process data. Also, expenses typically include the cost of hiring the required personnel, computing resources, and licensing.
• Analysts’ carelessness and lack of expertise can cause various issues during this process. Moreover, individuals lacking the expertise will be unable to spot incorrect data as they are unaware of the range of permissible and accurate values.
• Businesses may end up carrying out transformations that do not fulfill their requirements.

This has been a guide to what is Data Transformation. Here, we explain its techniques, examples, types, benefits, and disadvantages. You can learn more about it from the following articles –

• Data-Mining Bias
• Feature Engineering
• Time Series Analysis