Data Transformation
Every business generates a good amount of data daily, but the same is not useful until it is transformed into a useful format. To get benefitted from raw data, its transformation is necessary. With data transformation, you can make different pieces of data compatible with one another, move them to another system, and join with other data to drive useful business insights.
“Data transformation is the process of converting data or information from one format to another, usually from the format of a source system into the required format of a new destination system.”
The tools and techniques used for data transformation depend on the format, complexity, structure, be, and volume of the data.
Here, we have listed and explained the top eight data transformation methods
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 organized. Data manipulation tools help identify patterns in the data and transform it into a usable form to generate insights on financial data, customer behavior etc. Know more here.
7| Normalization
Data normalization is a method to convert the source data into another format for effective processing. The primary purpose of data normalization is to minimize 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.
