Enterprises frequently undergo changes due to mergers, acquisitions, and joint ventures. These organizational shifts result in the integration of people, processes, and data from the participating businesses, which subsequently leads to the migration of large amounts of data. However, such transitions often lead to data silos as the incoming data is mostly in different formats.
The best way to address this issue is through data conversion, which plays a pivotal role in standardizing data and helps businesses create a single source of truth (SSOT).
But what is data conversion, and how does it benefit businesses in converting raw data into meaningful information? Let’s find out.
Free 14 Day Trial - Simplify data conversion with code-free transformationsData conversion is the process of transforming data from one format to another so that it is compatible with the target system, application, or storage method. It is often carried out as part of a larger project, such as data migration or integration. The process entails extracting data from a source, such as a database, file, or web service, transforming it, and loading it into the required destination system.
Each data conversion task is unique and depends on the specific needs of a project. Based on the number and complexity of data formats involved, some data conversions may be straightforward and relatively simple, while others can be more intricate. The specific operations and transformations also differ significantly from project to project.
For example, in certain cases, a data conversion process may involve merging large amounts of data from specific fields or columns, while in other cases data might have to be split or separated in other fields or columns.
A comprehensive and effective data conversion process should:
Accurate data, whether it’s about customer behavior, sales, or marketing, can potentially increase a business’s bottom line. Insights from data can help organizations make strategic data-driven decisions, improve internal operations, and find new ways to generate revenue. However, the challenge is to ensure the usability of all the available data. Data conversion helps accomplish that. Here are a few benefits of data conversion:
Data conversion entails a structured series of steps, each with its specific purpose. These steps collectively transform data and ensures it aligns with the needs and requirements of the target system or application. Let’s explore the process in detail:
It is important to start with defining essential criteria and objectives for transforming data from one format to another. These requirements depend on how the data will be used within the target system or application. The best way to establish objective is by carefully considering factors such as the structure of the source data, the desired format and organization of the target data, necessary data cleansing and transformation steps, as well as any specific rules or guidelines that must be followed.
The next step involves conducting a comprehensive data profiling and assessment of the source data, which focuses on its structure, quality, and consistency. It includes identifying data sources, evaluating data quality, examining data relationships and dependencies, and analyzing data lineage.
As users retrieve data from its current storage location, whether it resides in databases, files, legacy systems, or cloud repositories, it is important to maintain data integrity to prevent data loss or corruption. Plus, security and compliance should always be a priority.
Once the source data is extracted, it is transformed according to format or structure of the target system. Data conversion usually involves altering data types, units of measurement, or coding schemes. It can also include data cleansing, validation, and enrichment to enhance data quality.
The transformed data is then mapped onto the destination. To do so, it is important to determine the appropriate loading method. Some of them include batch processing, real-time streaming, or incremental updates. The choice of the method based on factors like data volume and real-time requirements. Once the process starts, monitoring mechanisms are put in place to ensure successful data loading.
In this step, users conduct rigorous validation of the converted data to ensure accuracy, completeness, and compliance with defined data quality standards. This process involves applying quality rules and checks to identify errors, inconsistencies, and anomalies, which is crucial for maintaining data integrity and reliability.
Once the data validation is complete, the next stage involves addressing any issues or discrepancies that cropped up such as data anomalies, missing values, or outliers. The best practices is to establish data maintenance routines, including data governance practices, to maintain data quality over time.
Next, users conduct comprehensive testing to verify that the converted data meets the specified requirements and functions as expected. Some common types of testing include:
Finally, users introduce the converted data into the target system or application. Before deployment, they confirm the readiness of the target system by assessing factors such as storage capacity, processing power, and network bandwidth. It is also essential to verify the compatibility between the target system and the format/structure of the converted data.
Here's How You Can Automate Data ConversionWhen it comes to data conversion, it is not just a single step involved. Organizations employ a variety of methods and approaches to convert, manipulate, and optimize their data. Here are some of the common techniques in Astera Centerprise:
Changing Date and Time Format using Astera
Converting Data Types while Exporting the Data using Astera
Computing the Full Address of Customers in the Expression Builder in Astera
Documenting entries that fail the data quality rules in record level log in Astera
Data conversion is frequently misunderstood and mistaken with other data management practices. To understand how it is different, it’s essential to compare it with three related practices: data migration, data transformation, and data cleansing.
Let’s delve into each practice to provide a clearer understanding:
| Aspect | Data Conversion | Data Migration | Data Transformation | Data Cleansing |
| Objective | Change data format or structure | Move data to a new location | Modify data to meet requirements | Cleanse data to improve quality |
| Data Alteration | Reformat or translate data | Preserve data as-is | Modify content and structure | Correct errors and inconsistencies |
| Scope of Changes | Narrow focus on format/structure | Limited to relocation | Broad changes to data content | Focused on data quality |
| Examples | Converting CSV to XML | Migrating data to a new database | Aggregating sales data | Removing duplicate records |
| Outcome | Altered data in a new format | Data remains unchanged | Data tailored for a new purpose | Clean, error-free data |
Data conversion is central to entire data management strategy. However, this seemingly straightforward task is often riddled with complexities and challenges that demand meticulous planning and execution. Here are some of the key hurdles organizations often encounter during data conversion and strategies to tackle them effectively.
Data loss during conversion can result from errors, inadequate backups, or transformation issues. It can render the entire exercise useless, which is why it is important to tackle this issue proactively by implementing comprehensive data backups, rigorous testing, and detailed audit logs.
Mapping data from the source to the target format can quickly get complex, particularly when there are so many diverse systems or databases involved. Any errors can lead to misinterpreted data, which is why it is absolutely essential to do thorough planning, documentation, and monitoring.
Before starting the data conversion, a business must ensure compatibility between converted data and target systems as format or structure changes can disrupt processes. Moreover, it will require thorough testing and potential system upgrades later.
When data is moved around or converted, data quality issues are bound to happen. The best way to mitigate challenge is to implement rigorous validation and quality checks along with data governance best practices.
Data conversion projects often operate under tight timelines and budget constraints. Rushed projects may lead to errors, overlooked details, or inadequate testing, increasing the risk of data-related issues, and demanding efficient resource allocation.
To determine whether an organization should invest in data conversion, it is important to keep certain factors in mind. Some of them include:
Before diving into the decision, it’s crucial to assess the nature of the data. Consider the volume, variety, and complexity of data being handled. Data can come in various formats, such as text, images, audio, or video. If an organization deals with diverse data types regularly, data conversion may become a crucial process to ensure compatibility and usability.
Data integration is often essential, especially if an organization relies on multiple data sources, databases, or software platforms. Data conversion can facilitate seamless integration by converting data from one format to another, helping maintain consistency and efficiency across systems.
Manual data conversion can be a time-consuming and error-prone task. Investing in data conversion tools can significantly reduce the workload on teams and potentially save time and minimize the risk of errors.
Organizations should evaluate whether their data conversion processes can adapt to their changing needs. They should consider the scalability of their current approach – can it handle increasing data volumes and evolving data formats as their business grows?
While considering data conversion tools, it’s essential to assess the costs involved and compare them with the benefits it brings. Organizations should calculate potential savings in terms of time, labor, and error reduction. Additionally, they should factor in the long-term advantages of enhanced data management, such as improved decision-making and increased efficiency.
Data security is a paramount concern. Enterprises should ensure that their data conversion processes adhere to relevant data protection laws and regulations. Implementing robust security measures to safeguard sensitive information during the conversion process is crucial.
Astera is an enterprise-grade, end-to-end data management solution with powerful data conversion features. These include:
Astera offers enhanced functionality to develop, debug, and test dataflows in an interactive, visual environment. It allows users to build entire ETL/ELT pipelines simply by dragging and dropping the required transformations and data sources. Users can leverage its point-and-click functionality to map data effortlessly, without writing a single line of code.
Its built-in job scheduler allows users to automate their data conversion and integration workflows seamlessly. Moreover, the platform includes built-in features like FTP uploads and downloads, SQL query execution, and outside program execution that help streamline the process.
Astera enables connections to a wide range of sources, including popular formats like Excel, TXT, and XML. It also supports cloud services such as Amazon S3 and Microsoft Azure, as well as commonly used databases like SQL Server.
The platform makes it easier to create complex dataflows using built-in transformations, such as expressions, lookups, join, normalize, aggregate, and more. All users need to do is simply drag and drop these transformations onto the dataflow designer and transform entire datasets in a no-code environment.
Whether an organization is dealing with structured or unstructured data, Astera provides advanced data preparation features to manage data quality and ensure that the data conversion is precise, consistent, and error-free. Some of its prominent data preparation features include:
Convert, map, and validate complex data structures without writing a single line of code using Astera. Request a 14-day free trial and experience lightning-fast data conversion for yourself.
