One of the emerging opportunities to leverage the benefits of data warehousing is in the area of global human resource management (GHRM). Many companies have separate human resource information systems (HRISs) across the globe. In general, these autonomous systems are not only technically diverse, but the HR business processes they support are often fundamentally different. This presents a challenge to an organization needing to analyze and develop its human assets globally.

There are two prevalent market trends driving demand for human resources data warehousing (HRDW). First, many companies have implemented, or are in the process of reengineering, their multiple autonomous HR business processes and systems to a common enterprise-wide model. In many cases, this involves implementing an enterprise-wide HRIS to support their initiatives, using packaged software systems such as PeopleSoft or SAP. However, the ERP/HRIS implementation process could take a long time to incorporate data from around the world and typically addresses only part of employee-related information (e.g., payroll, benefits, etc.).

The second trend involves the unique and sometimes autonomous HR policies of large global organizations resulting in unique HRIS implementations to support these diverse processes. There may be no need or desire to try to fully integrate these diverse policies within the transactional systems.

Our experience shows both issues can be effectively addressed using a single approach that leverages data warehousing. It involves six basic steps.

1) Develop and validate the business case for global integration of HR information. Some business needs that can be addressed by an integrated global HR data warehouse are: reliably track employee history as the employee changes location; integrate data sources for HR programs that may be administered at a global level; and fully understand and plan how the workforce is developed, trained and deployed globally.

2) Define a global model, source/target mapping requirements and reporting/analysis requirements. In general, when multiple HRISs are implemented to support relatively autonomous HR business functions globally, the data models for each of these systems tend to be unique. A "top-down" analysis should focus on defining the data needed to fulfill global HR reporting and analysis requirements. A "bottom-up" analysis should focus on determining the availability of the data items in the individual local HRISs and defining how to map these data items into the global model.

3) Implement data stores and extract/transformation capabilities. Once the global data model is understood and the approach for mapping the data from the local HRISs is defined, it is time to begin implementation. It is critical to do so in small, incremental pieces, adding local HRISs as data sources and building data stores one piece at a time (subject by subject). In addition, the transformation and integration functions that map the local HRISs data into the global data model should not be "hard coded" but should be "rule driven." As the local HRISs evolve, the extract/transform functions must also evolve to continue to map the local data to the global data model.

4) Implement user applications. Once the data stores of integrated global data are available, user applications such as reporting, OLAP and data mining can be implemented. These can use any of a number of commercially available query/reporting, OLAP, data mining and/or analysis tools. The specific user applications to be built will depend on the reporting/analysis requirements and will evolve over time.

5) Meta data ­ maintaining the local/global mapping rules. Companies should implement a meta data knowledge base for HR information that is integrated with the HR data warehouse. This meta data can be extracted from the knowledge base repository into the tables that can be used directly by the integration function. A well-designed meta data knowledge base will provide the added benefit of giving HR data warehouse users awareness of, and confidence in, the accuracy of the data warehouse information. Good meta data provides flexibility, reduces duplication of effort and delivers a confidence-building "road map" of how the data warehouse was built and the HR data's "heritage."

6) Reengineer local business processes and HRISs. Sometimes, data from local HRISs cannot be integrated into a global model because the local HR business processes, and hence the HRIS automation that supports those processes, are incompatible. In this case, one has only a few choices: a) Recognize the restrictions associated with the level of integration that can be achieved with the local HRISs as they are, realizing that some global data may be inconsistent or incomplete. b) Reengineer the local business processes and update the local HRISs to be compatible with the global data model and reporting requirements. c) Incrementally implement changes to local business processes and the local HRISs to become more compatible with the global data model and reporting requirements.

The insights derived from your global human resource management data warehousing efforts can provide tremendous strategic value.

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