Six Key Challenges in the Data Conversion Process

June 2, 2022
Jhansi Rani
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Published On June 2, 2022

Data conversion refers to transforming your data from one format to another. The data conversion process involves:

Extracting data from the source.
Transforming it into another form.
Loading the converted data into the destination based on requirements.

Data conversion can be of several types, such as converting file formats or more complex conversions from one database to another or one vendor to another.

Key Challenges in the Data Conversion Process

Traditional data conversion is labor-intensive, error-prone, expensive, time-consuming, and requires robust data governance. Several hurdles can derail your data conversion process. However, modern, data-driven organizations can use Data Conversion Tools to automate the extract and load part of their data conversion process and focus their efforts on data transformation. Let us try and understand some of the top challenges:

Lack of Understanding of Source Data

This lack of understanding includes not being aware of the issues in your data, such as missing information, duplicates, and inaccurate data. It’s easy to assume that your data can quickly be configured into the parameters of a new system, but it could result in critical failures when it comes to user acceptance. You need to develop a good understanding of the source data to ensure successful database conversion.

Inadequate Data Analysis

Information can remain hidden due to technical constraints, or the lack of awareness among users, leading to incomplete or inaccurate information and creating challenges during the data conversion process. Businesses usually don’t have the time or the resources to rectify this issue. Conducting comprehensive data analysis early in the process, usually in the planning stage, can help you identify these hidden issues.

Non-Standard Processes

Data conversion involves a diverse set of people using disparate technologies. This includes cumbersome and error-prone spreadsheets to unsanctioned, third-party Data Conversion Tools. This can result in complications or failure in data conversion, resulting in higher costs and wasted time. Using fully validated data conversion automation tools, you can mitigate these risks with a standardized, best-practice-driven approach to data conversion.

Ineffective Validation of Specifications

It is essential to validate the specifications for converting and migrating data into a target source. Early misses can have huge repercussions later in the data conversion process. It is validating your database conversion specifications in the initial stages with actual data rather than just essential documentation can simplify the rest of the process.

Lack of Proper Testing

Typically, users see the actual data in the new system at the end of the design and development phase. You can’t make too many changes at this point if there are unforeseen issues, such as data incompatibility in the new system. You can save time and money by incorporating an agile, phase-wise testing process and involving your users early in the process to gather feedback.

Lack of Effective Collaboration

Data conversions typically involve a diverse set of people using multiple technologies and, in some cases, a complex mix of internal employees and external consultants. Working in disconnected silos can decrease efficiency, and if things don’t go as planned, it can be challenging to bring everyone together to resolve the issues. Effective collaboration from the beginning of the project ensures that all parties have access to the same information across project stages, leaving no room for future misunderstandings.

ConvertRite - Oracle Data Conversion Tool

ConvertRite is a secure, HIPAA-compliant Oracle Data Conversion Tool built on Oracle PaaS, designed to automate data conversion from any ERP to Oracle Cloud Applications. With ConvertRite, you can automate manual, time-consuming, and error-prone processes such as data mapping and validation and accelerate Data Migration from On-premises to Cloud.

Template-based design for data mapping between the source and Oracle Cloud Applications
Validation of data using cloud data elements
Creation of “pre-validated” FBDI or HDL files
In-built project management to manage end-to-end conversions
Dashboard to report progress of conversions between source and cloud