What is Data Transformation and How It Optimizes Business Processes

By | 2020-04-29T05:15:57+00:00 April 29th, 2020|

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 this data before loading into another system.

Chart showing where data is stored

A chart showing how much data is stored on consumer devices, enterprise systems, and the public cloud

Data transformation tools can help businesses resolve compatibility issues, improve data consistency, and perform different functions, such as aggregations, sorting, etc. to convert data in a format that suits the requirements of the destination system. The result is optimized 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 into a format usable by the target system or application. Similarly, you can also ‘transform’ your data by filtering it based on certain rules and joining different fields to get a consolidated view.

Data transformation is an important 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.

Why is Data Transformation Important?

Businesses need to transform data for several reasons, such as consolidating records, deleting duplicates, and changing formatting, etc. Transformations are also applied to concatenate and validate data, perform lookups, or to route data to different destinations.

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. 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.

data transformation

Data from SQL Server and Excel source being transformed and mapped on to an Excel destination file

Here’s how the destination file looks after the transformation:

Data Transformation 1

Preview of the consolidated payroll stored in the Excel file

Transformations can also be used to extract value from data. Instead of overwhelming your systems with multiple – often unnecessary – records, you can use transformations to filter out irrelevant data.

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?

The good thing about data transformation is that there are several approaches to it. Each approach, however, comes with unique benefits and challenges.

On-Premise Data Transformation Tools

On-premise data transformation tools allow businesses to extract, transform, and load crucial data very easily. Having an on-premise data transformation tool also translates to increased regulatory compliance and better security management.

The majority of these 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 thing to remember is that on-premise data transformation tools are deployed on-site, so you must ensure that you have enough resources in terms of storage capacity and the right systems to run the transformation tools.

Cloud-Based Data Transformation Tools

The pay-as-you-go feature of most cloud-based data transformation tools gives businesses the freedom to scale up and down as and when necessary. It is one of the reasons why cloud-based tools are popular. Having crucial enterprise data saved on a third-party cloud-based server, however, comes with its share of security concerns.

One benefit of cloud-based data transformation tools is that installation and configuration times are reduced significantly, which means that businesses can transform their data without too many delays.

Manual Data Transformation

Manual data transformation involves a great deal of coding, which means that you will have to hire dedicated development resources to get the job done.

Transforming data manually can also take up a lot of time and resources, particularly when dealing with multiple file formats. The risk of human error and accidental deletion of crucial business data is also very high when transforming data manually.

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.

Filtering Data

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.

Filter transformation

Data from a Fixed Length Source being filtered to display records from the USA

In the example above, the Filter transformation is applied on a Fixed Length source document to show records only from the USA.

Filter transformation properties

Defining the expression that will be used to filter out data

Routing Data

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 an example:

Route transformation

Data from SQL Server being routed on to two Excel destinations

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.

Router Transformation Properties

Rules of the Route transformation in the Router Transformation Properties screen

The conditions here can be simple or complex, depending on your requirements.

Sorting Data

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:

Sort transformation

Preview of customer data stored in SQL Server database is sorted in descending order of CustomerID

This was done by selecting the CustomerID field and selecting the Sort Order as descending as follows.

Sort Transformation Properties

Properties of the Sort transformation

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 solution 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.

Data profiling in Astera Centerprise

Data profile of the Customers table used for Sorting

Want to learn more about using Astera Centerprise for data transformation? Get in touch with us to schedule a demo, and find out how transforming your data can optimize your business processes and increase efficiency.

Centerprise Demo

By | 2020-02-14T10:28:14+00:00 February 14th, 2020|

As the volume of incoming business data grows, so do information silos across the enterprise. Data-driven innovation can only be made possible when data across these silos is integrated after converting and transforming it for consistency. This process makes the data intelligible to other business systems in the enterprise. Astera Software provides a variety of built-in advanced transformations to process complex data efficiently within a code-free environment.

Create dynamic, rule-driven data transformations using drag and drop operations

Before data is loaded to a target destination, it must be transformed to meet any format and structural requirements of the target database. This involves cleaning the data, applying business rules, checking for data integrity, joining data from two sources, and more. In most cases, one or more transformations are required to meet the technical and operational requirements, such as joining, concatenating, disaggregation, lookup, transposing data, validation, and others.

Astera enables users to process structures of varying complexities using the platform’s expansive library of built-in, advanced transformations, including lookups, expressions, joins, unions, filters, routes, normalizations, denormalizations, tree joins, and tree node queries. Our cohesive data foundation allows information experts to build modular integration flows of varying complexity, refresh data automatically at preferred intervals, validate data, build individual record-level error outputs, and aggregate data quality statistics – all within an intuitive, code-free environment.

The Smarter, Easier Way to Convert and Cleanse Data from Any Source