The IDC predicts that by 2020, more data will be stored on the public cloud and enterprise systems than on consumer devices. The data on these platforms can be in heterogeneous formats and structures, which means that businesses will have to manage, enrich, and manipulate it before loading it into another system and perform data analytics. This is where data transformation comes into play.
Data transformation tools can help businesses resolve compatibility issues and improve data consistency. This can be done through performing different transformation functions, such as aggregations, sorting, data cleansing, etc. to convert data in a format that suits the requirements of the destination system. The result is optimized, integrated data that can further be processed to draw insights, generate reports, and make data-driven decisions.
What is Data Transformation?
Data transformation is the process of converting data from one format into another format that is more usable by the target system or application. Data transformation includes multiple activities: you may ‘transform’ your data by filtering it based on certain rules and joining different fields to get a consolidated view.
Data transformation is an important middle step in the extract, transform, and load (ETL) process – a prerequisite to loading. Most ETL tools also come with predefined functions that can be used to transform your data quickly and efficiently.
After discussing what transformation is, we can now understand some of its important steps:
- Identify the structure of the source files
- Extract data from the file source
- Next, map the data from the source file to the data transformation tool
- Perform the transformation i.e. filter, sort, cleanse or aggregate the data
- Finally, send the transformed file to the destination
Why is Data Transformation Important?
Businesses need to transform data for several reasons, such as migrating data, consolidating records, deleting duplicates, and changing formatting, etc. Transformations are also applied to concatenate and validate data, perform lookups, or route data to different destinations.
Let’s look at a transformation example: suppose a bank acquires an insurance firm operating in the same region. Once the acquisition is complete, it is decided that a single payroll will be generated for all the employees. The payroll generation process would have been straightforward if all the employee data was stored in a unified system, such as a data warehouse or database. However, in this case, one company stored employee data on an SQL Server and the other stored their payroll information in an Excel sheet. To create a consolidated payroll for the employees, the data needs to be transformed to fit the requirements of the target system i.e. Excel file.
Here’s how the destination file looks after the transformation:
Data transformations can also be used to extract values from different data types. Instead of overwhelming your systems with multiple – often unnecessary – records, you can use different types of data transformations to filter out irrelevant data.
For instance, if you want to generate a report of all the sales conducted in a particular country, say the USA, applying the Filter transformation will prevent the destination system from being strained unnecessarily since only relevant records will be passed through. Storing relevant and comparatively fewer records in the destination system means less memory consumption during data processing, which will reduce execution time.
On-Premise, Cloud-Based, and Manual Data Transformation: Which One Should You Choose?
On-Premise Data Transformation
On-premise data transformation allows businesses to extract, transform, and load crucial data very quickly. Having an on-premise data transformation tool also translates to increased regulatory compliance and better security management.
The majority of these data transformation tools come with drag-and-drop functionality, which means that non-technical employees will be able to transform data and convert it into a usable format.
One important thing to note is that on-premise tools are deployed on-site; therefore, you must ensure that you have enough resources in terms of storage capacity and the suitable systems to run these data transformation tools.
Cloud-Based Data Transformation
Manual Data Transformation
How to Transform Your Data
Data can be transformed in several ways, depending on your goal and destination system requirements. Transformations can not only be used to clean, filter, split, and join data but also to enrich it. Here are some types of data transformations:
Businesses need to process numerous records to retrieve data that is relevant for a particular scenario. Data can be filtered based on one or more rules. You can then use this narrowed-down data for further processing without making the destination system work on irrelevant records.
In the example above, the Filter transformation is applied on a Fixed Length source document to show records only from the USA.
You can use the Route transformation to direct source data to different paths of similar or different formats based on certain logical rules.
Here’s a data flow to illustrate the feature:
The Route transformation here is used to send the data retrieved from SQL Server to two different Excel destination files. Conditions on the ShipVia field determine which Excel file the data will be routed to.
The conditions here can be simple or complex, depending on your requirements.
Large businesses often need to sort their data to make it more manageable. The Sort transformation can be applied to any field to arrange the output in ascending or descending order.
Here’s an example in which the Sort transformation is used to display Customer ID records in descending order:
This was done by selecting the CustomerID field and selecting the Sort Order as descending as follows.
The Return Distinct Values Only box could also be checked to remove redundancies and display unique records.
Benefits of Data Transformation
Transformation tools, when used correctly, can improve data quality significantly and improve process efficiency. Transformed data is easier to use, trustworthy, and compatible with end systems and applications. High-quality transformed data ensures that the destination system only has data with the required format and structure.
Here are some other benefits of data transformation:
- Transformations can help businesses reap the maximum value from their data.
- Standardizing data through transformations can improve data management.
- Transformed data can be utilized by various tools for different applications, such as visualizations, reporting, analysis, etc.
Astera Centerprise: The Easy Way to Transform Data
Astera Centerprise is a complete data integration platform that allows users to extract, map, transform, and load their data in a code-free environment. In addition to drag-and-drop transformations, Astera Centerprise also includes data profiling capabilities, allowing users to get a holistic view of their data. You can also profile data to compare its pre and post-transformed statistical overview.
Want to learn more about using Astera Centerprise as a data transformation software? Get in touch with us to schedule a demo, and find out how transforming your data can optimize your business processes and increase efficiency.