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/
Top 10 HR and People Analytics Themes of 2023
As we near the end of another successful year here at Insight222, we want to reflect on the top themes that have emerged in our content.
From data-driven insights to real-world examples, our team has worked meticulously to deliver informative and persuasive articles that aim to enhance the HR and people analytics function. And we have seen some exciting changes and advancements in the field this year.
So, without further ado, here are the top themes that have taken front stage in our content during 2023.
Psychological Safety in the Workplace
Psychological safety in the workplace has been proven time and time again that without it, a team cannot thrive. In fact, we like to think of it as the epitome of successful teams.
Therefore, it's no surprise that this theme carries over from last year. Some of our most popular blogs discuss measuring psychological safety in the workplace, understanding how organisational culture impacts it and exploring how companies like Microsoft are transforming their organisational culture to prioritise psychological safety and promote a positive work environment.
Behavioural Science in HR
(Source: People Analytics Trends 2021)
The integration of behavioural science into HR and people analytics practices has been gaining traction for the past few years, and this year was no exception. With the changing nature of skills and roles in HR, the need for understanding human behaviour and decision-making has become increasingly important in driving impactful business outcomes.
With this, our article on exploring the role of behavioural science in HR and how it can be leveraged to improve employee engagement, performance, and productivity was one of our most popular reads of 2023.
How AI is Changing the HR Landscape
No discussion about the future of work is complete without considering the role of artificial intelligence (AI).
(Source: The Impact of GPT and Generative AI Models on People Analytics (Interview with Andrew Marritt))
AI has been incorporated into HR for some time now. We have been using it to automate routine tasks, streamline recruitment processes and improve HR analytics. However, with the birth of generative AI models like Chat-GPT, it is an understatement to say that AI has revolutionised every aspect of HR. Better yet, it's safe to say that it has and will continue to revolutionise every business function within an organisation.
From utilising AI in people analytics to how it is transforming the HR landscape, our articles on the impact of Chat-GPT and generative AI models and how AI is changing HR analytics have been among the most popular reads of this year. And for good reason - with the potential to improve decision-making, streamline processes, and enhance employee experience, AI is a topic every HR and people analytics professional should pay attention to.
The Impact of Analytics on HR
Our Insight222 research has shown time and time again that organisations that invest in people analytics drive better business outcomes, which is why, this year, we continued to dig deeper into this topic by exploring the New Model for People Analytics.
With the rise of digital transformation and the increasing importance of data in driving strategic business decisions, our articles on using statistics to drive actionable outcomes, why people analytics is so important for HR, and how social capital can be measured have been highly sought-after reads.
Upskilling the HR Function and Building Data Literacy at Scale
Considering the previous points, it's understandable that upskilling the HR function and building data literacy at scale have emerged as key themes this year. To fully leverage the benefits of AI and data analytics, HR professionals must develop a strong understanding of data and how it can be used to drive strategic decision-making.
As such, in July, we released our research, Upskilling the HR Profession: Building Data Literacy at Scale, which outlines the skills and competencies that HR professionals need to succeed in the digital age. It also highlights how HR leaders need to build an effective skill-based workforce planning capability.
(Source: Measuring the ROI of Employee Training and Development)
Interestingly, this research has also sparked discussions on who holds the responsibility for scaling data literacy across HR, which we explore in our article Who Holds the Responsibility for Scaling Data Literacy Across HR?
Measuring the ROI of Employee Development
Building upon the theme of upskilling and data literacy, it's important to also focus on measuring the ROI of employee development. As professionals in the HR sector, we know all too well that investing in employee training and development is crucial for an organisation's long-term success.
But with senior executives increasingly asking (and expecting) HR to demonstrate the value of these investments, our article on measuring the ROI of employee training and development has been one of the most popular reads this year.
Delivering Greater Value for the Business Through People Analytics
At its core, people analytics is about delivering greater value for the business. Our 2022 research, Impacting Business Value: Leading Companies in People Analytics, is a testament to this. Leading Companies (organisations that drive the most business impact through people analytics) have consistently shown better financial performance, higher employee engagement and retention rates, and overall greater success compared to their less data-driven counterparts.
This is why, in 2023, we have seen a surge of interest in articles on delivering greater value for the organisation with people analytics and the growing influence of people analytics in strategic business decisions.
And this trend will only continue as more and more organisations recognise the importance of incorporating data-driven insights into their decision-making processes.
Challenges to Building Data Literacy
If there is one thing we have identified as a common theme this year, it's the challenges of building data literacy within HR. From understanding the technical aspects of data analysis to gaining buy-in from senior leadership, organisations face various hurdles when trying to build a culture of data literacy.
(Source: Insight222 Research: Upskilling the HR Profession: Building Data Literacy at Scale)
However, as we continue to uncover the value that analytics brings to HR and the business as a whole, these challenges will become easier to overcome. And with more resources and tools available to support data literacy efforts within organisations, we are confident that this theme will evolve in 2024.
Evolving the HR Practice
In all, as we wrap up another year, it's clear that people analytics and data-driven HR practices have become even more ingrained in our work. From the importance of psychological safety and behavioural science to the impact of AI, measuring ROI, and delivering greater value to the business - these are just a few key themes that have shaped our content this year.
However, as we move forward, HR professionals must continue developing their data literacy and upskilling themselves to drive the success of their organisations further.
To that end, we look forward to seeing how these themes will evolve and shape the future of HR in the coming years.
Manpreet RandhawaDecember 18, 2023
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