Six Key Challenges in the Data Conversion Process
- 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
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.
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
- 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
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