Enhancing a Talent Data Platform by Transforming Employees’ Career Experience into Decision-Ready Data

At the major logistics company, talent data had been accumulated through the integration of personnel action, payroll, and attendance information from various human resources systems, as well as through various activities conducted within the talent management system, such as performance evaluation and training. However, information regarding employee experience and preferences was insufficient for utilization in decision-making by management and human resources departments, making it necessary to rely on frontline managers with direct knowledge of individual employees when making decisions such as transfers and assignments. Although field-based judgments rarely resulted in errors, they made it difficult to identify overlooked talent and could lead to perceptions of unfairness resulting from information gaps. As a result, the company needed to establish a mechanism to define and collect data on experience and preferences and manage it in a form that could be utilized for decision-making. In response to these management challenges, ABeam Consulting, acting as a hands-on partner responsible for supporting the initiative from planning through execution, advanced this effort through the following three themes.

1. Definition and Collection of Talent Data Intended for Utilization
   Information such as experience and preferences that had not previously been sufficiently utilized was defined as data items necessary for decision-making, and a mechanism was established to collect responses from employees.
2. Converting Unstructured Data into a State Suitable for Utilization
   The utilization of talent data requires not only aggregation but also the contextualization needed to make diverse talent data comparable. For example, even if experience information is collected, it cannot be compared if the level of detail and periods vary, causing the data to go unused. The inability to fully utilize collected and integrated data is a common challenge among many companies, and this initiative similarly addressed the need to contextualize data.
3. Measures for Utilizing Data in Business Operations
   The talent data required, as well as the manner in which it should be presented, differs according to the roles and responsibilities of those utilizing it. Even within personnel assignment decisions alone, there are multiple use cases, including assignment to key positions, selection of overseas assignees, and rotational assignments for new employees. Before this initiative, "the large variety of data types and the lack of clarity regarding which data should be used" constituted a barrier to utilization. Therefore, use cases were organized, and a mechanism was established that enabled responsible personnel to easily access the information they needed. This made it possible to present data in an optimized manner for each user and supported practical utilization in the field.

The database of the talent management system was designed as a general-purpose database for organizing and storing information and could accommodate both generation and utilization of data to a certain degree; however, it was not optimized for either purpose. Therefore, this initiative was based on the concept that "the optimal databases for data generation and data utilization are different." In the generation phase (data collection from employees), a user-friendly database was developed with a limited number of input items to facilitate ease of entry. This improved both completion rates and the quality of employee input. Meanwhile, in the utilization phase, a database was established that could manage more specialized items in a comparable format to support diverse decision-making needs. The enhancement of meaningfully defined data items also expands the potential applications of AI.
The most critical success factor of this project was the establishment of a semantic layer that connected data generation and utilization through business meaning by designing databases optimized for each purpose.

Through this initiative, employees’ career experience was contextualized and transformed into standardized, comparable data, enabling organization-wide visualization and comparison of talent across the company. As a result, personnel transfer and assignment decisions that previously relied on field intuition can now be made based on data. Furthermore, the solution was realized using only the standard functions of a cloud-based talent management system, without relying on custom add-on development or customization. This reduced the burden associated with additional development and ongoing maintenance and contributed to lowering Total Cost of Ownership, including system implementation, operation, and maintenance costs.
Going forward, the company will further advance talent management as a business foundation for data-driven talent decisions by leveraging the established talent data foundation for capabilities such as AI-based skill estimation, optimal personnel assignment, and the enhancement of global talent strategies. ABeam Consulting will continue to support initiatives that develop corporate talent management into core business infrastructure by applying the knowledge accumulated through long-term, hands-on partnership support rather than merely implementing systems.