The 10 golden rules for establishing a people analytics practice十大黄金法则：
战略适配性：确保人力资源分析项目与组织的战略目标对齐，以实现最大的价值和影响。
持续的员工倾听：通过整合员工和业务的声音，优先处理正确的战略人力资源问题。
证据基础的HR服务整合：将所有基于证据的HR服务整合到一个功能中，提升人力资源分析的交付速度和质量。
清晰的人力资源分析操作模型：建立一个目标操作模型，明确客户、可交付服务、服务水平和交付时间。
数据隐私合规性：遵守数据隐私法规，同时考虑数据分析在文化和业务连续性方面的影响。
数据驱动决策的HR能力提升：通过提升HR社区的数据和洞察力使用，将业务机会转化为分析服务。
管理HR数据：建立集中的企业级数据基础设施，改善数据的组合、共享和分析能力。
产品设计和思维：确保人力资源分析服务的用户设计友好，易于导航，并激励用户在决策中使用数据。
实验与最小可行产品：通过实验和最小可行产品，逐步评估和改进解决方案，避免大规模实施失败。
利用人工智能的潜力：构建和实施基于机器学习的AI功能，确保模型的性能和有效性，同时控制数据偏见和合法性。
这些法则展示了通过系统方法创建并采纳人力资源分析实践的重要性，强调了以数据和证据为基础支持人力资源功能的必要性。
It is time for an update on my previous posts on the 10 golden rules of people analytics, simply because so much has happened since then. For example, continuous employee listening, artificial intelligence (AI in HR), agile HR, employee experience, strategic workforce management, and hybrid working are just a few emerging topics in recent years listed in Gartner's hype cycle for HR transformation (2023).
In the last year, I have spoken to many people working in different organisations on establishing people analytics as an accepted practice. I have also joined some great conferences (HRcoreLAB, PAW London & Amsterdam) where I learned from excellent speakers. I also (re)engaged with some excellent people analytics and workforce management vendors, such as Crunchr, Visier, eQ8, AIHR, One Model, Mindthriven, and Agentnoon. Finally, I also enjoyed having multiple elevating discussions with some thought leaders who influenced my thinking (e.g., David Green, Rob Briner, Jonathan Ferrar, Dave Millner, Sjoerd van den Heuvel, Ian O'Keefe, Brydie Lear, Jaap Veldkamp, RJ Milnor, and Nick Kennedy).
These encounters and my ongoing PhD research on adopting people analytics resulted in a treasure trove of new ideas and knowledge that confirmed my experience and beliefs that it is all about creating an embraced people analytics practice using a systemic approach in supporting HR in becoming more evidence-based. So, like I said, it's time for an update. I hope you enjoy and appreciate the post, and I invite you to engage and react in the comments or send me a direct message.
Create a strong strategy FIT.
It is obvious but not a common practice that your people analytics portfolio needs to align or fit with your strategic organisational goals. A strong strategic FIT ensures you execute people analytics projects with the most value and impact on your organisation. It is, therefore, important to integrate the decision-making on where to play in people analytics with your periodic HR prioritisation process.
Strategic workforce management and continuous employee listening are pivotal in prioritising the right strategic workforce issues
The bigger picture is that two people analytics-related HR interventions, strategic workforce management and continuous employee listening, are pivotal in prioritising the right strategic workforce issues. By blending the insights from these HR interventions, you ensure you are prioritising based on the voice of the business and the voice of the employee. See also my previous post on strategic workforce management. Because people analytics is at the core of these HR interventions and provides many additional strategic insights, I argue we need a new HR operating model where the people analytics practice is positioned at the centre of HR.
I argue that we need a new HR operating model where the people analytics practice is positioned at the centre of HR
Grow and integrate evidence-based HR services.
Based on my experience and research, I strongly advise integrating all evidence-based HR services into one function. See also my previous post on establishing a people analytics practice. This integration will enhance the speed and quality of your people analytics delivery, make you a trusted analytical strategic advisor, and make you a more attractive employer for top people analytics talent. All other people analytics function setups seem like compromises.
With evidence-based HR services, I refer to activities such as reporting, advanced analytics, survey management, continuous employee listening, organisational design and strategic workforce management. It is hardly ever that a strategic question is answered by only one of these services. In most cases, you will need to combine survey management (i.e., collecting new data), perform advanced analytics (i.e., build a predictive model), and share the outcomes in a dashboard (i.e., reporting) or build new system functionality based on the models (e.g., vacancy recommendation).
You will need to combine various people analytics services to provide real strategic value
Create a clear people analytics operating model.
Because the people analytics practice is maturing, it deserves a clear target operating model. In a target operating model, you clarify to the organisation whom you consider your clients, what services or solutions you can deliver, what service levels your clients can expect, and when and how you will deliver the solution.
Being transparent about your target operating model will build trust and legitimacy in your organisation. Inspired by the work of Insight222, a people analytics target operating model consists of a demand engine (understanding and prioritising demand), a solution engine (e.g., data management, building models, designing surveys), and a delivery engine (e.g., dashboards, advisory with story-telling, bringing models to production), ideally covering all the evidence-based HR services mentioned under rule 2 in this post. Additionally, more practices are applying agile principles to increase time-to-delivery and are using some form of release management to balance capacity.
Built trust and legitimacy
Compliance with data privacy regulations has been an important topic since the early days of people analytics ten years ago. Even before the GDPR era, organisations did well to understand when personal data could be collected, used, or shared. Legislation such as GDPR offers guidance and more structure to organisations on how to deal with data privacy issues.
Being fully compliant is not where responsible data handling ends
However, being fully compliant is not where responsible data handling ends. Simply because you can, according to data privacy regulations, doesn't mean you should. There are also contextual and ethical elements to take into account. For example, being able and regulatory-wise allowed to build an internal sourcing model matching internal employees with specific skills with internal vacancies doesn't mean you should. From a cultural or business continuity perspective, creating internal mobility may not be beneficial or desired in specific areas of your organisation. Assessing the implications of using data analytics in a broader context than just regulations will also enhance the needed trust and legitimacy.
Upskill HR in data-driven decision-making
Having a mature people analytics practice that delivers high-quality, evidence-based HR services is not enough to ensure value creation for your organisation. Suppose your organisation, including your HR community, struggles to translate business opportunities into analytical services or finds it hard to use data and insights on a daily basis in their decision-making. In that case, upskilling is a necessary intervention.
HR upskilling in data-driven decision-making is a necessity in growing towards a truly evidence-based HR culture
Creating awareness of the various analytical opportunities, developing critical thinking, creating an inquisitive mindset, identifying success metrics for HR interventions and policies, evaluating these metrics, and understanding the power of innovative data services, such as generative AI, is essential. When upskilling, be sure to recognise the different HR roles and their needs and preferences. For example, your HR business partners will likely want to develop their skills in identifying strategic workforce metrics and strategic workforce management. However, your COE lead (i.e., HR domain leads) wants to develop their ability to collect and understand internal clients' feedback and improve their HR services (e.g., recruitment, learning programs, leadership development). So, diversify your learning approach to make it more effective.
Manage your HR data
There is enormous value in integrating your HR and business data in a structured matter. Integrated enterprise-wide data allows you to combine, improve, share, and analyse data more efficiently. More organisations are using data warehouse and data lake principles to create this central enterprise-wide data infrastructure based on, for example, Microsoft Azure or Amazon Web Services technology.
A mature people analytics team is best equipped to create an HR data strategy and manage the corresponding data pipeline.
HR would do well to improve its capability to manage the data pipeline by hiring data engineers. It is an interesting discussion about where to position this data management capability and related skill set. The first thought is to position this capability close to the HR systems and infrastructure function. This setup might work perfectly. However, based on your HR context and maturity, I argue that the people analytics practice is a good and sometimes better alternative. Mature people analytics teams are likely more able to think about data management and creating data products and services built with machine learning models. Traditional HR systems and infrastructure teams may tend to focus too much on the efficiency of the HR infrastructure (e.g., straight-through processing, rationalising the HR tech landscape).
Excel in product design and thinking
Successful people analytics or evidence-based HR services excel in product design. Whether built with PowerBI or vendor-led BI platforms (e.g., Crunchr, Visier, One Model), dashboards must be user-friendly, easy to navigate, and motivate users to work with data in their decision-making. The same applies to functionality based on machine learning models, such as chatbots, learning assistants, or vacancy recommendations. The user design, the functionality provided, and the flawless and timely delivery all contribute to maximising the usage of these analytical services and, ultimately, decision-making.
Strong product design and thinking requires product owners to have a marketing mindset
As important as the product design is product thinking by the product owner. A product owner for, e.g., recruitment or leadership programs, should be constantly interested in hearing what internal clients think about their products. This behaviour requires product owners to have a marketing mindset. As part of a larger continuous listening program, an internal client feedback mechanism should provide the necessary information to improve your products and services continuously. A product owner should be curious about questions like: Are your internal clients satisfied? Should we tailor the products for different user types? What functionality can we improve or add?
Allow yourself to experiment
When a solution looks good and makes sense based on your analytics, management tends to go for an immediate big-bang implementation. However, don't be afraid to experiment and learn before rolling out your solution to all possible users. Starting with a minimum viable product (i.e., MVP) allows you to evaluate your product among a select group of users early in the development process. Based on feedback, you can enhance your product incrementally (i.e., agile) manner.
It also enables you, when valuable, to compare treatment groups with non-treatment groups. These types of experiments (i.e., difference-in-difference comparisons) help you to evaluate the effect the new product intends to have. People analytics services can support this incremental approach, testing a minimal viable product (MVP) and obtaining feedback to provide additional insights that may avoid a big implementation failure of your new products.
Embrace the potential of AI in HR
Today, artificial intelligence (AI) is predominantly based on machine learning (ML). These AI-ML models provide powerful functionality such as vacancy and learning recommendations, chatbots, and virtual career or work schedule assistants. There is no need to fear these applications, but having a deeper understanding of them is necessary. However, implementing these types of functionality without checking and validating them is risky and, therefore, unwise.
A mature people analytics practice allows you to build your own machine-learning-based AI functionality
A mature people analytics practice allows you to create and build these AI functionalities internally. You can also buy AI functionality by implementing a vendor tool, but please ensure you do not end up with a new vendor for each AI functionality you desire. If you choose to buy AI functionality, the people analytics team should act as a gatekeeper. Internally built machine learning models are subject to checks and balances. And rightfully so. However, the same should apply to ML-based AI functionality from external providers. The people analytics team should check the performance and validity of the model and control for biases in the data and legal and ethical justification.
The people analytics leader can make the difference
If you are the people analytics leader within your organisation, it might be daunting or reassuring to hear that you can make the difference between failure and success. You bring the people analytics practice alive by reaching out to stakeholders, developing your team, understanding your clients, learning from external experts, and building a road map to analytical maturity.
A successful people analytics practice starts with the right people analytics leader
As a people analytics leader, you should excel in business acumen, influencing skills, strategic thinking, critical and analytical thinking, understanding the HR system landscape, understanding the possibilities of analytical services, project management, and, last but not least, people management (as all leaders should). The result of having all these capabilities is that a people analytics leader, together with the people analytics team, becomes a trusted advisor to senior management, understands the most pressing issues within an organisation, can effectively manage the HR data pipeline, and can build new analytical services to enhance decision-making and ultimately drive organisational performance and employee well-being.
I hope you enjoyed my update on the 10 golden rules for establishing people analytics practice. If you enjoyed the post, please hit ? or feel invited to engage and react in the comments. Send me a direct message if you want to schedule a virtual meeting to exchange thoughts one-on-one.
Thanks to Jaap Veldkamp for reviewing.
作者 ：Patrick Coolen
https://www.linkedin.com/pulse/10-golden-rules-establishing-people-analytics-practice-patrick-coolen-85use/
HR transformation
2024年04月15日
HR transformation
What Issues are Top of the Mind for HR Leaders Heading into 2024?根据康奈尔大学工业劳动关系学院高级人力资源研究中心的一份调查报告，“转型和演变”这一广泛而重要的话题最近受到人力资源领导者的关注，该问题被确定为2024年企业的最紧迫问题。
“考虑到公司一直面临的所有颠覆，无论是在业务方面，还是在地缘政治问题的更广泛环境中，看到转型成为今年的首要目标，我并不感到惊讶，”康奈尔大学战略人力资源教授兼该中心主任布拉德贝尔说。
根据上周发布的调查，超过三分之二（67%）的人力资源领导者认为转型和演变（包括人力资源转型、文化演变和混合工作演变）是首要问题。而2023年，转型与演进排名第三，只有大约45%的受访者认为是首要问题。
调查显示，由于地缘政治力量和劳动力变化导致的业务中断正在加剧人们对转型和演变的担忧。Bell 说，人力资源领导者特别关注人力资源内部的转型，例如保持公司的敏捷性、提高效率和优化运营。他指出，中东的冲突和乌克兰的持续战争限制了这些地区的员工流动，另外，总体上减缓了一些人力资源转型工作。他补充说，对组织治理问题的高度关注，包括股东对高管薪酬的发言权，也在缓和人力资源转型，因为这种努力可能会限制招聘工作。
此外，Bell 表示，调查参与者报告说，快速的组织文化变化使员工难以建立联系并发展共同的目标，尤其是在当今分散的工作环境中。作为回应，人力资源领导者经常更新他们的混合工作模式，这可能会损害包容性或其他相关目标，从而阻碍文化发展。
HR 优先事项如何变化
排名前五的问题分别是人才管理、技术、员工体验以及领导力和继任计划。
Bell说，技术是今年进入前五名的新事物，这主要是由于人力资源部门对人工智能的兴趣。在前几年，该主题被嵌入到其他类别中，例如数字员工体验。去年排名第四的总奖励从榜单上掉了下来。
“每年，似乎都会有一个新话题出现在前 5 名名单上，”贝尔说。他说，2023 年，在高通胀和寻求为员工提供经济救济的组织推动下，总薪酬是增加的。但今年，通胀正在放缓，对经济衰退的担忧正在缓解，这可能会减少雇主对这一领域的担忧。
DEI 和福祉仍然是人力资源的优先事项吗？
Bell 说，尽管他们没有进入前五名，但 DEI 和福祉仍然是人力资源领导者最关心的问题之一。与去年一样，他们在 2024 年分别排名第六和第七。
Bell 说：“人力资源主管谈到希望保持他们迄今为止在 DEI 方面取得的进展，甚至希望将这些努力提升到一个新的水平。“例如，他们不仅考虑多样性和包容性，还考虑我们如何推动公平和各种人才实践。”
然而，他指出，一些公司正在撤回他们的 DEI 努力。这些行动反映在最高法院去年对平权行动作出裁决后，削减 DEI 预算和裁员 DEI 官员。
他说，同样，雇主对幸福感的关注也在减弱。
“大流行后，人们对员工的健康和福祉非常关注，”贝尔说。“我认为它已经有所消退。我不认为它像我们在调查中看到的其他一些主题那样受到同等程度的关注。
Source Human Resource Executive
HR transformation
HR transformation
HR transformation
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