In designing an HR data warehousing solution, what critical factors should be taken into account for organizations of varying sizes - large, medium, and small - given the complexity or simplicity of their HR processes? How can we achieve an optimal equilibrium between cost, efficiency, and functionality? Additionally, what are the potential implications of this balance on the overall strategic HR management, decision-making processes, and the organization's bottom line? Furthermore, how can we ensure the solution is scalable and adaptable to future trends and changes in HR technology, data privacy regulations, and organizational growth? Finally, what strategies can be employed to secure stakeholder buy-in, and what challenges might be encountered during the implementation phase?

Can you provide guidance on the best practices for designing a data model for HR data warehousing that incorporates [employee performance data/benefits data/compensation data/other HR data], and [supports/optimizes] [reporting/analytics/machine learning] capabilities?

How can I ensure [data accuracy/data completeness/data quality] in my HR data warehousing solution, especially when dealing with [multiple data sources/complex data transformations/historical data]?

What are some common challenges in implementing an HR data warehousing solution, such as [data silos/data privacy concerns/legacy systems], and how can they be [mitigated/addressed/resolved]?

Can you recommend any specific tools or technologies to use in creating an HR data warehousing solution for [on-premise/cloud-based/hybrid] systems, taking into account [data privacy regulations/data security considerations/data scalability needs], and [supporting/enabling] [real-time data integration/batch data processing/self-service analytics]?