- 27 May 2024
Oracle HCM Table Suffixes: A Guide to Understanding Fusion Table Purposes
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Introduction:
In the intricate landscape of Oracle Fusion HCM Cloud, one encounters a myriad of tables, each bearing a distinct suffix. These suffixes hold the key to understanding the purpose and functionality of the tables they accompany. From _ALL to _A, each suffix signifies a specific aspect of data management within the Oracle HCM ecosystem. Let’s delve into the significance of these suffixes and unravel their roles
Types Of Oracle HCM Table Suffixes and Usages
| Table Suffix | Description |
|---|---|
| _ALL | Comprehensive Repositories: These tables consolidate data across various operating units in a multi-org environment. |
| _TL | Multilingual Support: Offer multilingual support with entries distinguished by values in the LANGUAGE column. |
| _B | BASE Tables with Stringent Validations: Store data with stringent validations to ensure integrity and accuracy. |
| _F | Date-Tracked HR and Payroll Data: Feature columns for EFFECTIVE_START_DATE and EFFECTIVE_END_DATE for temporal analysis. |
| _V | Organized Data Access: Views generated from BASE tables to enhance query efficiency and simplify data retrieval. |
| _VL | Multilingual Views: Combine data from BASE tables with _TL tables based on user language settings. |
| _S | Primary Key Generators: Generate unique identifiers for table entries, ensuring data integrity and consistency. |
| _A, _AVN, _ACN | Audit Functionality: Track changes and modifications for data governance and compliance purposes. |
| _EFC | Euro Financial Currency: Configured for financial data management using Euro as the financial currency. |
Optimizing Data Management Strategies with _ALL, _TL, _B, and _F Tables
1. ALL tables consolidate data across various operating units, offering a comprehensive view of organizational entities. Organizations can leverage _ALL tables to analyze trends, identify patterns, and make informed decisions based on a holistic understanding of their data landscape
2. Organizations can optimize data retrieval and reporting from _ALL tables by implementing efficient query optimization techniques, indexing critical columns, and leveraging caching mechanisms to improve performance. Additionally, adopting data warehousing and data lake solutions can provide faster access to _ALL table data for reporting and analytics purposes.
3. Organizations can ensure data consistency across different language settings when using _TL tables by implementing robust data governance practices, standardizing data entry procedures, and conducting regular data quality checks. Additionally, providing comprehensive training to users on data entry standards and guidelines can help minimize errors and inconsistencies in multilingual data.
4. Best practices for managing multilingual data within _TL tables include maintaining language-specific dictionaries for translations, implementing localization standards for date, time, and currency formats, and ensuring that user interfaces support multiple languages seamlessly. Additionally, organizations should regularly update language translations and provide support for language-specific characters and symbols to enhance user experience.
5. Organizations can establish and enforce data validation rules within _B tables by defining clear data validation requirements, implementing data validation checks at the database level using constraints and triggers, and conducting regular data quality audits to identify and address data integrity issues. Additionally, providing user-friendly error messages and validation feedback can help improve data quality and user experience.
6. Data corruption issues in _B tables can lead to inaccurate reporting, compromised decision-making, and loss of customer trust. To mitigate data corruption issues, organizations should implement robust data validation mechanisms, perform regular data integrity checks, and establish data recovery procedures in case of data corruption incidents. Additionally, providing comprehensive training to users on data entry standards and guidelines can help prevent data corruption issues from occurring.
7. Organizations can utilize _F tables to analyze historical trends and patterns in HR and Payroll data by querying data based on effective start and end dates, performing trend analysis using time-series data visualization techniques, and identifying correlations between historical data points. Additionally, organizations can leverage advanced analytics tools and techniques to extract actionable insights from _F table data and make data-driven decisions to improve HR and Payroll processes.
8. Challenges when working with date-tracked data in _F tables may include managing overlapping date ranges, handling data gaps and inconsistencies, and ensuring data accuracy and completeness. These challenges can be addressed by implementing robust data validation and cleansing procedures, establishing clear data entry standards, and providing comprehensive training to users on effective date tracking practices. Additionally, organizations should regularly review and audit _F table data to identify and resolve any issues proactively.