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Best Practices for Oracle Fusion HCM Analytics

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Oracle Fusion HCM Analytics, a part of Oracle Fusion Data Intelligence Platform (DIP) (earlier known as Fusion Analytics Warehouse), equips various management levels with deep insights to effectively manage the workforce across the organization. DIP is to the most part a ready-to-use data and analytics solution that is typically implemented in a matter of weeks. There are some key considerations though to ensure a successful rollout, not just for the baseline (out of the box) content, but also for ensuring reliability and extensibility to accommodate ongoing and evolving analytics needs. In this blog I will highlight key points to plan for on your journey to rolling out Oracle Fusion HCM Analytics.

  1. What data to include in DIP? To answer this question, we go through the functional areas that need to be enabled. Identify the HCM modules that are in use and enable corresponding DIP functional areas (such as Workforce Management, Absence Management, Talent Acquisition, etc.). Another aspect of answering this question is deciding how much historical HCM data to include in DIP for analytics. It is also important to take into consideration when Oracle HCM was first implemented and how comprehensive the conversion effort was from legacy HCM systems. If there were acquisitions over the years, it is also good to understand if there have been data conversion anomalies that introduce exceptions with respect to workforce data setups for the rest of the organization. The end goal here is to understand how far back we want to go in terms of loading historical data while maintaining data quality and having enough historical trends to generate realistic forecasts (such as hiring and attrition projections).
  2. Data Corrections in Oracle Fusion HCM: Running the data pipeline from Oracle HCM to the DIP data warehouse will result in rejected records if there are data inconsistencies within Oracle HCM. To minimize the chances of these record rejections, it is recommended to run Oracle HCM diagnostic tests to identify and correct person, manager hierarchy, and legislative information records, prior to loading DIP.
  3. Investigate Rejected Records: The data pipeline from Oracle HCM to the DIP data warehouse is managed by Oracle and therefore requires little intervention. This is a huge differentiator compared to the massive effort of going the route of a home-grown data and analytics solution. These data pipelines enforce data quality checks as they load data in DIP and will therefore reject and flag data issues that need to be addressed. So it’s important to plan to go through the list of rejected records with their reasons, perform any necessary corrections in Oracle HCM and re-run incremental data refresh in DIP.
  4. Run Data Validations: DIP has built-in capability to compare its KPIs to metric calculations sourced directly from native Oracle HCM OTBI reporting subject areas. This helps understand any variances in metric formulas that may need to be adjusted with custom calculations that best fit your organization.
  5. Dashboard Content Organization: You can set up your own company specific catalog of standard dashboards before rolling them out to various user groups. This can be done by copying over content from the Oracle provided list of workbooks into a new custom folder to validate and publish to various groups. The default Oracle shipped folder is locked down and can’t be edited but it can be used to copy content from and edit within a custom folder.
  6. Plan for Various Scenarios of Implementing DIP Security: A major part of a DIP implementation is having a detailed plan of implementing HCM analytics security to accommodate various roles and responsibilities within HR and across the enterprise. DIP offers various layers of implementing analytics security, therefore it’s crucial to lay out the different types of Application Roles and plan on using them to handle the following security aspects:
    • Who has access to create new analytics content and who has view only access?
    • Which Oracle HCM roles have access to each workbook or group of workbooks? It is recommended to group workbooks with similar access criteria within the same folder therefore setting application role permissions at the folder level.
    • Which Oracle HCM roles require data security? Map these roles to the corresponding data security role. If out of the box data security roles don’t achieve the required outcome, plan on implementing custom security configurations to achieve your goal.
    • Are there groups of users who should not be able to drill down to detail information (such as individual information) but still be able to report at a summary level? A custom security configuration will be needed in this case.
  7. Data Security for Line Managers: Manager hierarchy security is supported out of the box by leveraging the default Line Manager Data Security Application Role. However, this role can’t be combined with other customer data security roles. Therefore, in this situation it is required to configure a custom manager data security application role which may be combined with other custom data security roles to achieve the desired data security model.
  8. Data Security Assignments: Data Security assignments are supported for various dimensions such as Business Unit, Department, Country, Legal Entity and Self-Record. However, the process to assign data security assignments is manual in the DIP Security console whereby the assignments are done to individual users. While there is a file upload process that enables setting up these data security assignments in bulk, it will require re-upload of assignments on a regular basis whenever there is a need to update assignments. This can be a significant maintenance effort, but luckily it is possible to automate the process of performing data security assignments and therefore eliminating any security risks.
  9. Leverage Benchmarks: Providing line managers visibility into how their own teams compare to the company wide averages allows them to identify areas for improvement. This enables line managers to see their own team’s performance in comparison to other parts of the company. Some examples of doing this include tracking KPIs around diversity, turnover, promotions, hires, and their corresponding trends over time. Since typically we don’t want to open up access to detail level of information for all line managers, we go through a configuration process to enable benchmark reporting at an aggregate level without jeopardizing access to secured information.
  10. Compensation Reporting with Element Entries: If you are looking to provide a full picture of employee compensation and benefits with information sourced directly from your Oracle HCM Element Entries, it is possible to extend DIP with a custom subject area to do so. You will then be able to pull in all historical information that relate to compensation and benefits down to the most granular level and roll it up together with the rest of the salary analysis information.
  11. Support for Custom Fields: Each organization has their own list of descriptive flexfields that have been configured in Oracle Fusion HCM. To allow for a consistent and standardized experience of self-service analytics in DIP, these descriptive flexfields will need to be enabled in DIP and incorporated in the various subject areas that are applicable.
  12. Report on Dynamic Time Periods: It is straightforward to report on fixed time periods such as by month, quarter and year. However, there is often the ask to enhance dashboards by enabling dynamic filtering on a sliding time window such as year-to-date, quarter to date or evening a rolling 12-month duration. In-dashboard filtering can be added to enable such variable time windows without generating multiple versions of the same reports.

For help with implementing Oracle Fusion HCM Analytics or other DIP products, contact Mazen Manasseh at Perficient.

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Mazen Manasseh, Director of Business Analytics

Mazen is a Director of Business Analytics at Perficient and an accomplished professional services leader with 20 years of being a customer advocate. An analytics solutions delivery expert in functional domains covering Supply Chain, Financials, HCM, Projects and Customer Experience. Being a thought leader in the business analytics space, he conducted numerous business training sessions and spoke at technology conferences around analytics and machine learning.

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