What are some of the common challenges and pitfalls of using global talent analytics and metrics?

1 Data quality and consistency

One of the most fundamental challenges of using global talent analytics and metrics is ensuring the quality and consistency of the data collected and analyzed. Data quality refers to the accuracy, completeness, timeliness, and relevance of the data, while data consistency refers to the alignment and standardization of the data definitions, sources, methods, and formats across different countries and systems. Poor data quality and consistency can lead to inaccurate, incomplete, or misleading insights and decisions, as well as waste time and resources. To ensure data quality and consistency, you need to establish clear and common data governance policies, processes, and practices, as well as use reliable and integrated data platforms and tools.

2 Data privacy and compliance

Another challenge of using global talent analytics and metrics is complying with the various data privacy and protection laws and regulations that apply to different regions and markets. Data privacy and compliance refer to the ethical and legal obligations and responsibilities of collecting, storing, processing, transferring, and using personal and sensitive data of candidates, employees, and other stakeholders. Violating data privacy and compliance can result in legal penalties, reputational damage, and loss of trust and confidence. To ensure data privacy and compliance, you need to understand and follow the relevant laws and regulations, such as the General Data Protection Regulation (GDPR) in the European Union, as well as implement appropriate data security measures and safeguards.

3 Data interpretation and communication

A third challenge of using global talent analytics and metrics is interpreting and communicating the data in a meaningful and actionable way. Data interpretation and communication refer to the ability to translate the data into insights, stories, and recommendations that can inform and influence the global talent acquisition strategy and goals. Poor data interpretation and communication can result in confusion, misunderstanding, or resistance from the stakeholders, as well as missed opportunities or wrong actions. To ensure data interpretation and communication, you need to use relevant and reliable data sources and methods, as well as use clear and compelling data visualization and presentation techniques.

4 Data alignment and integration

A fourth challenge of using global talent analytics and metrics is aligning and integrating the data with the global talent acquisition strategy and objectives. Data alignment and integration refer to the degree of fit and coherence between the data and the overall vision, mission, values, and goals of the global talent acquisition function and organization. Misaligned or unintegrated data can result in conflicting or irrelevant insights and decisions, as well as lack of alignment or integration with other functions or departments. To ensure data alignment and integration, you need to define and align the key performance indicators (KPIs) and metrics with the global talent acquisition strategy and objectives, as well as use cross-functional and collaborative data platforms and tools.

5 Data culture and capability

A fifth challenge of using global talent analytics and metrics is fostering a data culture and capability within the global talent acquisition function and organization. Data culture and capability refer to the mindset, attitude, behavior, and skills of the people involved in collecting, analyzing, interpreting, communicating, and using the data. A weak or negative data culture and capability can result in low data literacy, awareness, or engagement, as well as resistance or fear of change or innovation. To ensure data culture and capability, you need to promote and demonstrate the value and benefits of data, as well as provide training, coaching, and support for the people to develop their data skills and competencies.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?