Top 8 Data Transformation Methods

Data transformation is a technique of conversion as well as mapping of data from one format to another. The tools and techniques used for data transformation depend on the format, complexity, structure and volume of the data. 

It enables a developer to translate between XML, non-XML, and Java data formats, for rapid integration of heterogeneous applications regardless of the format used to represent data. 

• How Data Mining Works
• Top 10 Research Papers On Federated Learning
• How To Apply Smoothing Methods In Time Series Analysis
• Vision for using alternative financial data to optimize credit risk and the lending process
• Can We Use Batch Normalisation For NLP

Here, we have listed the top eight data transformation methods in alphabetical order.

1| Aggregation

Data aggregation is the method where raw data is gathered and expressed in a summary form for statistical analysis. For instance, raw data can be aggregated over a given time period to provide statistics such as average, minimum, maximum, sum, and count. After the data is aggregated and written as a report, you can analyse the aggregated data to gain insights about particular resources or resource groups. There are two types of data aggregation: time aggregation and spatial aggregation.

Know more here.

2| Attribute Construction

This method helps create an efficient data mining process. In attribute construction or feature construction of data transformation, new attributes are constructed and added from the given set of attributes to help the mining process.

Know more here.

3| Discretisation

Data discretisation is the process of converting continuous data attribute values into a finite set of intervals and associating with each interval some specific data value. There are a wide variety of discretisation methods starting with naive methods such as equal-width and

equal-frequency to much more sophisticated methods such as MDLP.

Know more here.

4| Generalisation

Data Generalisation is the method of generating successive layers of summary data in an evaluational database to get a more comprehensive view of a problem or situation. Data generalisation can help in Online Analytical Processing (OLAP). OLAP is mainly used for providing quick responses to the analytical queries which are multidimensional. The method is also beneficial in the implementation of Online transaction processing (OLTP). OLTP refers to a class system designed to manage and facilitate transaction-oriented applications, especially those involved with data entry and retrieval transaction processing.

Know more here.

5| Integration

Data integration is a crucial step in data pre-processing that involves combining data residing in different sources and providing users with a unified view of these data. It includes multiple databases, data cubes or flat files and works by merging the data from various data sources. There are mainly two major approaches for data integration: tight coupling approach and loose coupling approach.

Know more here.

6| Manipulation

Data manipulation is the process of changing or altering data to make it more readable and organised. Data manipulation tools help identify patterns in the data and transform it into a usable form to generate insights on financial data, customer behaviour etc.

Know more here.

7| Normalisation

Data normalisation is a method to convert the source data into another format for effective processing. The primary purpose of data normalisation is to minimise or even exclude duplicated data. It offers several advantages, such as making data mining algorithms more effective, faster data extraction, etc.

Know more here.

8| Smoothing

Data smoothing is a technique for detecting trends in noisy data where the shape of the trend is unknown. The method can help identify trends in the economy, stocks, consumer sentiments etc.

Know more here.