What is data transformation?
Data transformation is the process of mapping and transforming data from one format to another. It allows you to translate between XML, non-XML, and Java Primitives, and Java classes, thus making it possible for you to rapidly integrate heterogeneous applications irrespective of the format that is used to represent data.
The data transformation functionality is available via a Transformation Control. It is possible to package data transformations as controls, treat them as resources and then reuse them across multiple business processes, applications, and integration solutions.
Why is data transformation important?
Data transformation is done to make the data better organized. Data that is transformed might be easier to use for humans as well as computers.
When the data is properly formatted and validated, the data quality is enhanced and applications are protected from unseen issues like null values, unexpected duplicates, incorrect indexing, and incompatible formats.
Transforming data also increases the compatibility between applications, systems, and types of data. If data is being used for several purposes, it may need to be transformed in multiple ways.
Data transformation is crucial in activities like data integration, data conversion, and data management. This is because it helps in standardizing, shaping, and introducing consistency between various datasets.
Data transformation assists you in moving data into its target destination in an effective and efficient manner. It helps organizations maximize the value that they gain from the data they have collected and manage data in a simple manner to avoid information overload.
What are the steps of data transformation?
The exact steps in the data transformation process will vary depending on the situation and the type of data transformation being used. However, here are the most common steps involved in the data transformation process:
1. Data interpretation
If you want to transform your data, you first need to interpret your data to figure out what type of data you are currently dealing with and what you have to transform it into.
This process is not always as easy as it seems. Many operating systems form conclusions about the way data is formatted simply based on the extension that is appended to a file name. However, the actual data that is inside the file, directory, or database could be completely different from what is suggested by the file name.
For accurate data interpretation, you need to have tools that are able to peer deeper inside the structure of a file, directory, or database and take a look at what is actually inside, instead of just jumping to conclusions based on the file name or database table name says is inside.
You also need to figure out your target format (the format that your data should be in after the transformation is complete.
2. Pre-translation data quality check
After figuring out the current data format and the target format, you need to run a quality check to identify problems like missing or corrupt values in the source data, which could cause problems in future steps of the data transformation process.
3. Data translation
This involves picking every part of your source data and then replacing it with data that actually fits within the formatting requirements or your target data format.
Normally, data transformation is not limited to just replacing it with data that fits within the formatting requirements or your target data format.
4. Post-translation data quality check
In this step, you look for inconsistencies, missing information, or other errors that may have Even if your data was error-free before translation, there is a decent chance that problems will have been introduced during translation which is why you need a post-translation data quality check.
What are the types of data transformation?
Here are the 8 most commonly used types of data transformation:
In data aggregation, raw data is gathered and expressed in a summary form for the purpose of statistical analysis. The raw data can be aggregated over a specific period of time so that statistics like average, minimum, maximum, sum, and count can be provided.
The aggregated data can then be analyzed to glean insights about specific resources or resource groups.
There are two types of data aggregation - time aggregation and spatial aggregation.
2. Attribute Construction
Attribute construction helps in creating an efficient data mining process. In attribute construction or feature construction of data transformation, new attributes get constructed and added from the set of attributes to aid in the data mining process.
Data discretisation involves transforming continuous data attribute values into a finite set of intervals and then associating a specific data value with every interval. There are several data discretisation methods existing, starting with simple methods like qual-width and equal-frequency and going on to sophisticated methods like MDLP.
Data generalisation refers to the generation of successive layers of summary data in an evaluational database for the purpose of getting a more comprehensive view of an issue or a situation.
This method can help with Online Analytical Processing (OLAP) which is primarily used to provide quick responses to the analytical queries which are multidimensional.
It also adds value in the implementation of Online transaction processing (OLTP) which is a class system that is designed for the purpose of managing and facilitating transaction-oriented applications, particularly the ones involved with data entry and retrieval transaction processing.
Data integration refers to combining data residing in different sources and providing users with a unified view of the data. It is a key step in data pre-processing.
The two major approaches to data integration are the tight coupling approach and the loose coupling approach.
Data manipulation involves changing or altering data to make it more organised and improve its readability. Data manipulation tools aid in identifying patterns in the data and converting it into a usable form to generate insights on financial data, customer behaviour etc.
Data normalization is a method that is used to convert the source data into a different format so that it can be processed more effectively. The primary objective of normalization is the minimization and exclusion of duplicated data.
Some of the advantages that it brings include speeding up data extraction and increasing the effectiveness of data mining algorithms.