• Data privacy
    David Green:The best HR & People Analytics articles of June 2024 David Green整理了最近的HR和PA的文章,其实最近也不仅仅是这方面的内容了,推荐大家可以了解看看,文章谈到了一些内容可以简单了解下: Justin Taylor, Keith Sonderling, Guru Sethupathy的演讲 总结: 讨论了人员分析生态系统的最新进展和未来趋势。 Insight222研究报告《构建人员分析生态系统:运营模式v2.0》预览 总结: 探讨了如何构建和优化人员分析的运营模式。 TechWolf完成4275万美元B轮融资 总结: 该融资将进一步推动其在AI和人员分析领域的发展。 Mercer和MIT发布的《技能策略指南》 总结: 解释了为什么技能应成为重新思考工作的首要任务,以及如何在AI时代克服挑战。 关于混合工作模式对员工保留和生产力的影响研究 总结: 混合工作提高了员工满意度并减少了离职率。 组织网络分析(ONA)的应用 总结: ONA能够揭示传统组织图中缺失的协作和决策影响,有助于优化工作场所策略。 Insight222的网络研讨会《HR和人员分析中的AI应用》 总结: 探讨了AI在HR和人员分析中的应用及其影响。 McKinsey关于生成式AI的研究 总结: 生成式AI的采用激增,为组织带来了可衡量的业务价值。 Mark Mortensen和Amy Edmondson关于重新定义办公室返工对话的文章 总结: 提供了领导者如何通过重新定义对话来平衡面临面的工作和灵活工作的策略。 Rashleen Kaur Arora关于制定平衡组织和员工需求的返工策略的文章 总结: 提供了如何通过证据和员工反馈制定有效的返工策略的案例。 Pietro Mazzoleni关于HR中生成式AI的应用 总结: 讨论了根据数据成熟度做出明智选择的重要性。 Greg Newman关于AI聊天机器人的员工旅程 总结: 阐述了如何通过使用员工旅程语言来最大化数字工人的价值。 Martijn Wiertz关于生成式AI重新定义工作设计的文章 总结: 提出了一个愿景,即生成式AI帮助我们重新定义工作设计,创造更多时间用于重要任务。 Max Blumberg关于AI时代的人员分析职业的文章 总结: 探讨了在AI时代保持相关性的技能需求及其透明性。 Scott Latham和Beth Humberd关于自动化对工作的四种影响的文章 总结: 讨论了基于价值类型和交付方式的工作响应自动化的四种方式。 Deloitte团队关于组织网络分析(ONA)的文章 总结: 讨论了ONA在测量员工绩效和优化工作场所策略中的应用。 Dave Hodges关于基于研究的HR决策的文章 总结: 强调了基于证据的HR决策的重要性。 Henrik Håkansson关于人员分析中的“错失恐惧症”的文章 总结: 将“错失恐惧症”概念应用于人员分析领域。 Amit Mohindra关于“72法则”的文章 总结: 提供了一个解释增长率的关键参数的简单方法。 Louise Baird关于机器学习及其在人员分析中的应用 总结: 解释了监督学习和非监督学习在人员分析中的应用。 Martha Curioni关于将可解释的AI引入HR流程的文章 总结: 提供了在招聘、预测离职和评估晋升准备度等HR流程中应用可解释AI的例子。 Nick Jesteadt和Erin Fleming关于人员分析领域前沿问题的文章 总结: 强调了生产力、技能和产品化等人员分析中的常见主题。 Christopher Rosett关于人员分析立方体的文章 总结: 介绍了人员分析立方体的概念及其应用。 Willis Jensen关于寻找人员分析工作的文章 总结: 分享了关于如何成功过渡到新角色的见解和策略。 Gregory Vial, Julien Crowe和Patrick Mesana关于高级分析中的数据隐私风险管理的文章 总结: 介绍了五种数据隐私保护方法及其对数据可用性的影响。 Cathy O’Neil, Jake Appel和Sam Tyner-Monroe关于算法风险审计的文章 总结: 提供了帮助组织评估其AI工具和算法的简单框架。 Öykü Işık, Amit Joshi和Lazaros Goutas关于生成式AI风险管理的文章 总结: 提供了管理四种生成式AI风险的蓝图。 Josh Bersin关于首席人力官(CHRO)角色演变的文章 总结: 强调了CHRO在C-suite中的重要性及其角色的多学科性。 Jeanne Meister关于未来HR工作角色的文章 总结: 介绍了未来十年HR领域的十三个新兴工作角色。 Naomi Verghese关于HR技能提升的文章 总结: 分享了HR专业人员在数据咨询和沟通方面所需的关键技能。 MIT和Mercer关于技能驱动型组织的报告 总结: 探讨了在AI时代,技能驱动型组织的重要性及其益处。 Business Roundtable关于基于技能的内部流动白皮书 总结: 提供了推动技能验证和连接人员与机会的战略。 Microsoft关于混合工作环境中新员工入职的研究 总结: 强调了角色职责、反馈和资源对新员工成功的重要性。 Dave Ulrich关于绩效管理的文章 总结: 提出了一个四步流程来改善绩效管理系统。 Erin Meyer关于构建有效企业文化的文章 总结: 提供了六条指导原则,帮助管理者应对文化建设的挑战。 Rob Cross和Katheryn Brekken关于团队网络效应的研究 总结: 发现80%的团队未能达到其生产力潜力,并提供了提高团队绩效的六种策略。 Shujaat Ahmad关于AI对生产力和繁荣的影响的文章 总结: 提倡从生产力优先转向繁荣优先的AI模型,以促进公平和可持续发展。 BCG关于女性技术领导者在生成式AI中的领先地位的研究 总结: 发现高级女性技术领导者在生成式AI的采用方面领先于男性同行。 Richard Rosenow关于人员分析新兴趋势的文章 总结: 分享了人员分析领域的六大新兴趋势。 Alicia Roach关于战略性劳动力规划的文章 总结: 讨论了战略性劳动力规划在业务成功中的重要性。 Annie Dean关于团队状态的报告 总结: 发现使用AI的团队在协作方面表现更好。 Shay David关于HR技术从自动化到增强的演变的文章 总结: 解释了HR技术如何增强人类能力和丰富员工体验。 I’m putting the finishing touches to June’s Data Driven HR Monthly in the airport lounge at Minneapolis-St Paul after a successful peer meeting for more than 50 North American members of the Insight222 People Analytics Program. The two-day peer meeting featured speakers including: Justin Taylor Keith Sonderling Guru Sethupathy and a collaboration between Bennet Voorhees and Eunice Ok. We also previewed the soon-to-be-published Insight222 research study: Building the People Analytics Ecosystem: Operating Model v 2.0 (click on the link to register to receive a copy). Other highlights in June included: We also ran a peer meeting for European members of the Insight222 People Analytics Program, which was hosted by Nestlé and featured sessions run by Nataliya Filonenko Michael Cox Alex Browne Thomas Tchako Nowe Piyush Mathur Jack Liu and Martin Janhuba. We delivered an Insight222 webinar on AI in HR and People Analytics, which featured Andrew Elston Justin Shemeley and Jasdeep Kareer, PhD (née Bhambra). Watch the recording here. In the HR Tech space, TechWolf announced a new $42.75m round of Series B funding. Congrats to Andreas De Neve ? Mikaël Wornoo? Jeroen Van Hautte ? and the team. Welcome to the more than 2000 new subscribers to the Data Driven HR Monthly newsletter, who joined in the last month. This edition of the Data Driven HR Monthly is sponsored by our friends at Mercer Strategic Shift: Skills-Powered Organizations in the Age of AI Forty-four percent of workers’ skills will be disrupted by technology in the next five years.* To thrive through this disruption, businesses must adapt their operating models to perpetually reinvent themselves as demand for skills ebbs and flows with greater velocity and volatility. The next-generation organization will be at the forefront of this strategic shift toward making skills the currency of work, cultivating a culture of talent sharing, automating work to take mundane tasks off employees’ hands, augmenting human creativity with AI, and reshaping the entire talent life cycle. Find out how to make the shift to a skills-powered organization The new Skills Strategy Guide from Mercer and MIT SMR Connections explores: Why skills should be a top priority in rethinking work in the age of AI The obstacles that stand in the way Practical steps to overcome challenges and reap the rewards Learnings from Standard Chartered Bank’s skills journey How to build a skills-powered approach to work Read the Strategy Guide *Source: The World Economic Forum’s 2023 Future of Jobs report To sponsor an edition of the Data Driven HR Monthly, and share your brand with close to 130,000 Data Driven HR Monthly subscribers, send an email to dgreen@zandel.org. Heartfelt thanks to Richard Rosenow It’s ten years since I published the first edition of the Data Driven HR Monthly (which featured pioneers like Luk Smeyers Andrew Marritt Ian OKeefe Jonathan Ferrar and Greta Roberts). Unbeknown to me, Richard Rosenow organised a wonderful surprise – see here. It’s such a kind gesture - thank you Richard and the One Model team for creating this and the 100 people - friends, colleagues, clients, and peers - many of whom have inspired me in my journey in the wonderful world of people analytics. Thank you too to my colleagues at Insight222 - and everyone who has contributed to the Data Driven HR Monthly over the last decade. Share the love! Enjoy reading the collection of resources for June and, if you do, please share some data driven HR love with your colleagues and networks. Thanks to the many of you who liked, shared and/or commented on May’s compendium (see Thank You section at the end of this issue). If you enjoy a weekly dose of curated learning (and the Digital HR Leaders podcast), the Insight222 newsletter: Digital HR Leaders newsletter is published every Tuesday – subscribe here. HYBRID, GENERATIVE AI AND THE FUTURE OF WORK MCKINSEY - The state of AI in early 2024: Gen AI adoption spikes and starts to generate value If 2023 was the year the world discovered generative AI, 2024 is the year organizations truly began using—and deriving business value from—this new technology. New research from McKinsey highlights that adoption of AI – and GenAI – has surged in the last 12 months, that organisations are reporting measurable benefits, that there is increased mitigation of the risk of inaccuracy, and that there are a small group of high-performers leading the way. From a HR perspective, the study finds: (1) 12% of respondents are regularly using GenAI in HR. (2) Organisations most often see meaningful cost reductions from GenAI use in HR (see FIG 1). (3) HR functions are most often able to put their GenAI capabilities to use within one to four months. (4) Talent is one of six areas of best practice where high-performers are leading with GenAI (see FIG 2). (Authors: Alex Singla Alexander Sukharevsky Lareina Yee and Michael Chui with Bryce Hall) FIG 1: Organizations most often see meaningful cost reductions from generative AI use in HR and revenue increases in supply chain management (Source: McKinsey) FIG 2: Organizations seeing the largest returns from generative AI are more likely than others to follow a range of best practices (Source: McKinsey) MARK MORTENSEN AND AMY EDMONDSON - Leaders Need to Reframe the Return-to-Office Conversation Framing refers to how an issue is presented; it’s the meaning layered onto an issue or situation that shapes how people think about its objective facts. More precisely, it’s about re-framing: deliberately replacing taken-for-granted cognitive frames with more helpful ones. Mark Mortensen and Amy Edmondson discuss the concept of ‘framing’ and its role for leaders in engaging in dialogue with employees about the balance between in-person and flexible working. They offer a three-step process to communicate flexible work policies: (1) Acknowledge the bind and be patient. (2) Focus on mutual value, not just organisational benefits. (3) Approach the process as data-driven, co-created, iterative learning. For more on this topic, listen to Mark in conversation with me on the Digital HR Leaders podcast: How to Foster Collaboration Within Hybrid Working Teams. RASHLEEN ARORA - Design a Return-to-Office Strategy That Balances Organizational and Employee Needs It’s becoming increasingly evident that rigid return-to-office (RTO) mandates can backfire by antagonising employees and impacting retention. Rashleen Kaur Arora presents Gartner research that outlines how HR leaders can craft a RTO strategy that balances organisational objectives with employee buy-in. The article includes a powerful case study on how Scotiabank implemented an evidence-based hybrid RTO model (see FIG 3). FIG 3: Scotiabank’s role aligned hybrid personas (Source: Gartner) PIETRO MAZZOLENI - Generative AI in HR: making smart choices depending on your data maturity | GREG NEWMAN - What's the employee journey of an AI chatbot? | MARTIJN WIERTZ - How will we use GenAI to redefine our Work Design: Creating Great Places to Be | MAX BLUMBERG - Saving your People Analytics Career in the Face of AI | SCOTT LATHAM AND BETH HUMBERD - Four Ways Jobs Will Respond to Automation Organizations which provide an environment where the needs of the workforce are aligned with the outlines of the future will have the competitive advantage. June saw a plethora of insightful reads about the impact of AI on HR and people analytics, so I’ve gathered five together here along with a prescient piece from 2018 on the professions most susceptible to automation. (1) Pietro Mazzoleni examines the importance of data maturity when it comes to the successful adoption of GenAI solutions in HR. (2) Greg Newman outlines why using the language of the employee journey will help your organisation maximise the value you gain from digital workers. (3) Martijn Wiertz presents a compelling vision where GenAI helps redefine our work design, creating time we can utilise for doing the work, care and training we need as a community – from great places to work to great places to be (see FIG 4). (4) Max Blumberg (JA) ?? explores how people analytics roles may evolve in the age of AI, the skills needed to remain relevant, and the importance of transparency in these issues within the people analytics community. (5) Finally, and thanks to Hung Lee for featuring it in a recent edition of Recruiting Brainfood, let’s revisit an article by Scott Latham and Beth Humberd that outlines four ways in which jobs will respond to automation based on two factors: the type of value provided and how it’s delivered (see FIG 5). FIG 4: Source – Martijn Wiertz FIG 5: Which Professions Are Most Vulnerable to Automation? (Source: Latham and Humberd) PEOPLE ANALYTICS MAYA BODAN, DON MILLER, SUE CANTRELL, GARY PARILLIS, AND CARISSA KILGOUR - Harnessing organization network analysis (ONA): Measure workforce performance and optimize strategies ONA reveals insights absent in traditional organizational charts—such as how people collaborate, who influences decision-making and/or operates in silos, and sentiments surrounding trust and influence. A helpful primer on how to use Organisational Network Analysis (ONA) from the Deloitte team of Maya Bodan Don Miller Susan Cantrell Gary Parilis and Carissa Kilgour. Their article discusses the myriad of use cases ONA can be used for, including to: (1) Measure workforce performance, (2) Understand individual workforce performance, and (3) Optimise workplace strategies. FIG 6: ONA can help uncover collaboration within an organisation (Source: Deloitte) DAVE HODGES - Facts Over Fads: HR Decisions Grounded in Research |HENRIK HÅKANSSON - People Analytics: The fear of missing out | AMIT MOHINDRA - The "Rule of 72": A Gentle Reminder | LOUISE BAIRD - Machine Learning and its Applications in People Analytics | MARTHA CURIONI - Building Explainable AI Into HR Processes | NICK JESTEADT AND ERIN FLEMING - People Analytics Frontiers aka Why are We Asking the Same Questions Again? | CHRISTOPHER ROSETT - The People Analytics Cube If you’re not practising evidence-based HR, what type of HR are you practising? In recent editions of the Data Driven HR Monthly, I’ve featured a collection of articles by people analytics leaders. These act as a spur and inspiration to the field. Seven are highlighted here. (1) David Hodges takes inspiration from Rob Briner’s research to make the case for evidence-based HR. As Dave asks: “If you’re not practising evidence-based HR, what type of HR are you practising?” (see FIG 7). (2) Henrik Håkansson applies the popular concept of “fear of missing out” to people analytics in his astute article. (3) Amit Mohindra provides a handy explanation of the ‘Rule of 72”, which can be used to extract a key parameter from a growth rate. (4) Louise Baird breaks down the two different types of machine learning – supervised and unsupervised – and how it can be applied to people analytics. (5) Martha Curioni provides examples of building explainable AI into a range of HR processes including: hiring, predicting attrition, and assessing promotion readiness. (6) Based on their survey of people analytics practitioners, Nick Jesteadt and Erin Fleming highlight three common yet seemingly elusive themes in the field: productivity, skills and productisation. (7) Christopher Rosett breaks down the People Analytics Cube (see FIG 8) in his LinkedIn post with a nod to Alexis Fink. FIG 7: What is being used in HR instead of evidence? (Source: Evidence Based HR: A New Paradigm by Rob Briner, Corporate Research Forum) FIG 8: People Analytics Cube (Source: Christopher Rosett) WILLIS JENSEN - Finding a People Analytics Job One of the features of the people analytics field is that it is pretty fluid with many that work within it moving roles in the last 12-18 months – as evidenced by the invaluable People Analytics Job Board that Richard Rosenow oversees. In his article, Willis Jensen shares insights from his recent transition to a new role including: (1) Being very clear about what you want in your next job. (2) Don’t write an AI-generated cover letter. (3) Use a resume tool to help you tailor your resume for each job. (4) Do not use a shotgun approach of applying for hundreds of jobs. (5) Use LinkedIn as a job-hunting tool. For more on people analytics careers, listen to Serena H. Huang, Ph.D. in discussion with me on the Digital HR Leaders podcast: How to Enhance Your Career in People Analytics. ETHICS AND PRIVACY SPECIAL GREGORY VIAL, JULIEN CROWE, AND PATRICK MESANA - Managing Data Privacy Risk in Advanced Analytics | CATHY O’NEIL, JAKE APPEL AND SAM TYNER-MONROE - Auditing Algorithmic Risk | ÖYKÜ ISIK, AMIT JOSHI, AND LAZAROS GOUTAS - 4 Types of Gen AI Risk and How to Mitigate Them Three articles covering ethics, risk and privacy with regards to advanced analytics and AI, which should be invaluable to people analytics professionals and HR technologists alike. (1) Gregory Vial Julien Crowe and Patrick Mesana present five approaches to measuring data privacy and how each approach impacts on data usability (see FIG 9) before discussing mitigation strategies. (2) Cathy O’Neil Jacob Appel and Sam Tyner-Monroe, Ph.D. lay out a set of simple frameworks (see example in FIG 10) designed to help organisations evaluate that their AI tools and algorithms are fair and working as intended. (3) Öykü Işık Amit Joshi and Lazaros Goutas outline a blueprint for managing four types of generative AI risk (see FIG 11). FIG 9: Five Approaches to Preserving Data Privacy (Source: Vial, Crowe and Mesana) FIG 10: A Simplified Ethical Matrix (Source: O’Neil, Appel, and Tyner-Monroe) FIG 11: Four types of AI risk (Source: Isik, Joshi, and Goutas) THE EVOLUTION OF HR, LEARNING, AND DATA DRIVEN CULTURE JOSH BERSIN - The Ever Expanding Role Of The Chief HR Officer (CHRO) The CHRO must transform the HR team, moving from the “service delivery” model to an HR team of consultants, problem-solvers, and analysts. Josh Bersin lays out a compelling case for the CHRO being the most important role of all in the c-suite now. He highlights factors such as the abundance of people challenges, labour shortages, organisation redesign, and globalisation. Josh also introduces his initial findings from a study of 47,000 CHROs: (1) There is a major increase in the C-level importance of the CHRO. (2) The CHRO job is multi-disciplinary, and more difficult than it looks. (3) The CHRO role is expanding. (4) Strong CHROs are now transforming the HR function. (5) The HR function is not developing itself - 80% of high-performing CHROs are external hires. FIG 12: The two roles of the CHRO (Source: Josh Bersin) JEANNE MEISTER – 13 HR jobs of the future In HR, this is our Promethean moment as we navigate a complex future, one with limitless possibilities in how we work, where we work, who we work with and what we expect in our workplace. Based on her conversations with HR leaders, Jeanne C M. presents 13 HR jobs of the future, which will emerge between now and 2030 (see FIG 13). Jeanne then explains how each of these roles “embody five core workplace themes enabling leaders to embrace reinvention as a strategy where humans and machines collaborate to deliver in which to the organization.” The five themes are: (1) Building GenAI literacy, (2) Working from anywhere, (3) Building human-machine partnerships, (4) Driving innovation and wellbeing in the workplace, (5) Creating a personalised employee experience. FIG 13: 13 HR Jobs of the Future (Source: Jeanne Meister) NAOMI VERGHESE - Investing in the Right Approach to Upskilling HR When the CHRO and HRLT role-model the use of people data and analytics in their day-to-day job, then other HR professionals also use people data and analytics in their work. Naomi Verghese shares the key findings from research she led at Insight222 to identify the key skills HR professionals need to consult and communicate effectively with data. The study, Upskilling the HR Profession: Building Data Literacy at Scale, identified five essential skills for HR professionals: (1) Consulting, (2) Influencing Stakeholders, (3) Data Interpretation, (4) Building Recommendations from Insights, (5) Storytelling. The study also identified the importance of role-modelling by the CHRO and HR leadership team with regards to data literacy (see FIG 14, and above quote). FIG 14: (Source: Insight222, Upskilling the HR Profession: Building Data Literacy at Scale) WORKFORCE PLANNING, ORG DESIGN, AND SKILLS-BASED ORGANISATIONS MIT SMR CONNECTIONS AND MERCER - Strategic Shift: Skills-Powered Organizations in the Age of AI By making skills the backbone of their talent practices, organizations can better allocate people to projects, help employees explore different career paths, and gain the flexibility to allocate their capital more effectively as their needs change. In their collaborative study, MIT and Mercer break down why skills should be a priority in rethinking work and people management in the age of AI. The report highlights the benefits for employees and employers of a skills-based approach (see FIG 15), provides practical guidance on how to overcome challenges, and provides powerful learnings from Standard Chartered’s skills journey. Features contributions from experts including Peter Cappelli Tanuj Kapilashrami Ravin Jesuthasan, CFA, FRSA Brad Bell Joseph Fuller Tom Kochan and Audrey Mickahail. FIG 15: Benefits of a skills-powered approach (Source: MIT and Mercer) LINKEDIN LIVE: Skills-Powered Organizations in the Age of AI | JULY 24, 2024 Register to join Tanuj Kapilashrami, Ravin Jesuthasan and David Green for a LinkedIn Live discussion on Skills-Powered Organizations in the Age of AI on July 24 at 10.00am EST. BUSINESS ROUNDTABLE - Skills-Based Internal Mobility Playbook Summary | White Paper Skills are five times more predictive of a person’s future performance than their education An excellent playbook compiled by the Business Roundtable on skills-based internal mobility, which is organised into five chapters covering critical areas such as stakeholder engagement, skills assessment and validation (see FIG 16), how to connect people with opportunities, how to measure success, and enabling technologies. Features examples from a myriad of companies including: Walmart, Chevron, Workday and Vistra.  Thanks to Brian Heger for highlighting this resource in his excellent weekly Talent Edge newsletter. FIG 16: Skill validation characteristics (Source: Business Roundtable) EMPLOYEE LISTENING, EMPLOYEE EXPERIENCE, AND EMPLOYEE WELLBEING DAWN KLINGHOFFER, KAREN KOCHER, AND NATALIE LUNA - Onboarding New Employees in a Hybrid Workplace New hires who are provided with clarity about their role responsibilities, feedback on how they are doing, and resources to help them answer questions are three to four times more likely to contribute to their team’s success during the first 90 days. Now we are in the era of hybrid work, what’s the ideal way to onboard new employees today? That was one question that the people analytics team at Microsoft sought to answer in a recent study, along with: How can we ensure that new hires thrive while also supporting flexibility? The findings confirmed that onboarding to a new role, team, or company is a key moment for building connections with the new manager and team and doing so a few days in person provides unique benefits. But just requiring newcomers to be onsite full time doesn’t guarantee success. In their article, Dawn Klinghoffer Karen Kocher and Natalie Luna explain and provide examples of how onboarding that truly helps new employees thrive in the modern workplace is less about face time and more about intention, structure, and resources. For example, the study found that the top factors that make the most difference in onboarding new employees are clarity about role responsibilities, feedback on how they are doing, and resources to help them answer questions. New hires who are successfully set up with these three elements are three to four times more likely to contribute to their team’s success during the first 90 days. For more on Microsoft’s approach to employee thriving, which they define as: being energized and empowered to do meaningful work, listen to Dawn in conversation with me on the Digital HR Leaders podcast: How Microsoft Created A Thriving Workforce By Going Beyond Employee Engagement. LEADERSHIP, CULTURE, AND LEARNING DAVE ULRICH - Reflections on Performance Management: How to Make Meaningful Progress In May’s Data Driven HR Monthly, I featured a McKinsey article on a performance management system that puts people first. In his recent article, Dave Ulrich cites a number of sources highlighting that pretty much all stakeholders (including employees, executives and HR leaders) are unhappy with their performance management systems. Ulrich lays out a four-step process for performance management (see FIG 17) comprising: (1) Clarifying expectations with meaningful goals. (2) Measuring and tracking performance. (3) Allocating financial and non-financial rewards. (4) Having positive coaching conversations. Dave then highlights the recently launched Manifesto for Flourishing at Work, a collaboration of practitioners, consultants, and academics to reinvent performance management. He highlights three critical topics from the manifesto: First, align performance and purpose by making sure that performance encourages progress towards a company’s purpose that includes all stakeholders. Second, manage the complex system of performance by focusing both on individuals within the system and also the system itself. Third, ensure that leaders are secure enough in themselves to use their power to empower others and to allow employees to contribute to their own improvement. FIG 17: Performance management process – four steps (Source: Dave Ulrich) ERIN MEYER - Build a Corporate Culture That Works If you hire people whose personalities don’t align with your culture, no matter what else you get right, you are unlikely to get the desired behaviors. Ever since Peter Drucker’s infamous assertion that “culture eats strategy for breakfast,” it has been widely acknowledged that managing corporate culture is the key to business success. Yet the link between ‘values’ and ‘behaviours’ is often stark. As INSEAD professor Erin Meyer asks in her latest Harvard Business Review article: “If culture eats strategy for breakfast, how should you be cooking it?” Erin blends in examples from the likes of Amazon, Netflix, Airbnb, Pixar and others and presents six guidelines to help managers who are confronting the challenges of culture building: (1) Build Your Culture Based on Real-World Dilemmas. (2) Move Your Culture from Abstraction to Action. (3) Paint Your Culture in Full Colour. (4) Hire the Right People, and They Will Build the Right Culture. (5) Make Sure that Culture Drives Strategy. (6) Don’t Be a Purist. An absolute must-read. At Insight222, we’re delighted that Erin Meyer will be speaking at our Global Executive Retreat in Amsterdam in September. The Retreat is exclusively for member companies of the Insight222 People Analytics Program. Click on this link to find out more. ROB CROSS AND KATHERYN BREKKEN | I4CP - The Team Network Effect™: How Precision Collaboration Unleashes Productivity A new study of 1,400 organisations on team effectiveness, led by Rob Cross and Katheryn Brekken, Ph.D. for The Institute for Corporate Productivity (i4cp), finds that 80% of teams fall short of reaching their full productivity potential due to corporate dysfunction. The study identifies six snares that stifle team performance (see FIG 18), and provides powerful examples including from Roche, which found that efforts to increase geographic and cross-functional collaboration across teams in 89 countries reaped a direct revenue impact of $500 million in less than two years. FIG 18: How companies rank against the six dysfunctions that stifle team performance (Source: i4CP) DIVERSITY, EQUITY, INCLUSION, AND BELONGING SHUJAAT AHMAD - From Productivity to Prosperity: The AI Shift Leaders Must Embrace The jobs most at risk from AI automation are jobs occupied by women and minority racial groups. In his compelling essay, Shujaat Ahmad argues that this calls for a shift from the current focus on a productivity-only AI model (with an emphasises on cutting costs at the expense of worker well-being and creativity) to a prosperity-first AI model, championing AI's potential to improve human life, promote fairness, and ensure sustainable progress alongside economic growth. Shujaat then breaks down the differences between the two models in four scenarios: software development, product management, go-to market, and recruitment (see FIG 19), as well as outlining three steps for leaders seeking to build a prosperity-first AI strategy: (1) Adopt a Prosperity-First True North and Accountability Measures for AI Adoption. (2) Put Employees in the Pilot Seat. (3) Commit to Responsible AI Development; Integrate AI Ethically and Inclusively. FIG 19: Productivity Only vs. Prosperity First AI – Recruitment (Source: Shujaat Ahmad) BCG - Women Leaders in Tech Are Paving the Way in GenAI A recent BCG study finds that that senior women in technical functions are ahead of their male peers in adopting GenAI—but junior women are lagging behind (see FIG 20). The study identifies three key attributes that correlate with gender disparities in GenAI adoption: (1) Awareness of GenAI’s criticality to job success. (2) Confidence in GenAI skills. (3) Risk tolerance for using GenAI prior to having a company policy. The authors (Maria Barisano Neveen Awad Adriana Dahik Julie Bedard Uche M. Gunjan Mundhra and Katherine Lou) conclude that if the number of senior and junior women with GenAI skills increases, then it’s likely that women’s representation in tech leadership will grow, and call for highly targeted upskilling programs, leadership advocacy and change management. FIG 20: Women leaders in tech are ahead in GenAI adoption (Source: BCG) HR TECH VOICES Much of the innovation in the field continues to be driven by the vendor community, and I’ve picked out a few resources from June that I recommend readers delve into: RICHARD ROSENOW - 6 Emerging People Analytics Trends for a People-Focused Future – Richard Rosenow of One Model shares his observations on shifts in the people analytics field including related to regulation, team structure and focus, and the people data supply chain (see FIG 21). Definitely worth a read. FIG 21: The People Data Supply Chain (Source: One Model) ALICIA ROACH - Want Better Business Planning? Budgeting? Recruiting? Read This! – Alicia Roach of eQ8 provides a treatise on the rising importance of strategic workforce planning: “SWP is a business planning and forecasting process that just happens to be grounded in people.” FIG 22: Source – eQ8 ANNIE DEAN – State of Teams 2024 – Annie Dean presents Atlassian’s newly published report on the State of Teams, which has a plethora of insights including that teams and leaders who use AI are better at collaborating. FIG 23: Team that use AI on a regular basis (Source: Atlassian) SHAY DAVID - From Automation To Augmentation: The Evolution Of HR Tech – Shay David of retrain.ai explains how leveraging HR technology to enhance human capabilities and enrich the employee experience is transforming the workplace. PODCASTS OF THE MONTH In another month of high-quality podcasts, I’ve selected five gems for your aural pleasure: (you can also check out the latest episodes of the Digital HR Leaders Podcast – see ‘From My Desk’ below): REID HOFFMAN - Gen AI: A cognitive industrial revolution - In this episode of At the Edge, Reid Hoffman speaks with McKinsey’s Lareina Yee about the generative AI revolution and how it can teach users to understand and harness its power. PATRICK COOLEN – The Evolution of People Analytics – In a fascinating episode of the Talent Intelligence Collective podcast, Patrick Coolen joins hosts Toby Culshaw Alan Walker and Alison Ettridge to discuss all things people analytics and the factors that drive success. MARCO BURELLI – Shaking Up Silos – Marco Burelli joins the HR Visionaries podcast to walk through the HR transformation journey at TomTom including breaking down old silos to create a more unified and dynamic team structure. JOSH BERSIN - A New, Transformed Role For The HR Business Partner – Josh Bersinoutlines some of his latest research in relation to the new and transformed role of the HR Business Partner, which as Josh says has become pivotal to a company’s successful people strategy. SHUBA GOPAL - Computational Biology Helps People Analytics with Small Samples & More – In another must-listen episode of the Directionally Correct podcast, Shuba Gopal joins hosts Cole Napper and Scott Hines, PhD to discuss how techniques gleaned from computational biology can help in people analytics. VIDEO OF THE MONTH PRASAD SETTY – Tech It Up a Notch: GenAI for HR Leaders In his keynote at the i4CP Next Practices Now Conference, Prasad Setty, formerly Head of People Analytics at Google, breaks down the opportunities and challenges of GenAI in organisations and posits a path forward for HR leaders to champion humanity in the workplace. At Insight222, we’re delighted that Prasad will be speaking at our Global Executive Retreat in Amsterdam in September. The Retreat is exclusively for member companies of the Insight222 People Analytics Program. Click on this link to find out more. BOOK OF THE MONTH SHARNA WIBLEN - Rethinking Talent Decisions: A Tale of Complexity, Technology and Subjectivity In ReThinking Talent Decisions, Sharna Wiblen, presents an uncomfortable truth: Talent decisions are always subjective. Drawing on examples from business, sports, movies and everyday interactions, Sharna emphasises the importance of understanding complexity and encourages deliberate, intentional, and informed decisions and conversations around talent. For a teaser from the book, read my expert interview with Sharna: Rethinking Talent Decisions and Navigating Subjectivity in HR. RESEARCH REPORT OF THE MONTH NICHOLAS BLOOM, RUOBING HAN, AND JAMES LIANG - Hybrid working from home improves retention without damaging performance There are a lot of opinions about the impact of hybrid work and some executives argue that it damages productivity, innovation and career development. But what does the data say? In their study, Nick Bloom Ruobing Han and James Liang find that hybrid working improved job satisfaction and reduced quit rates by one-third. The reduction in quit rates was significant for non-managers, female employees and those with long commutes (see FIG 24). For more from on this topic, listen to Nick Bloom in conversation with me on the Digital HR Leaders podcast: Unmasking Common Myths Around Remote Work. FIG 24: WFH cut attrition by 33% overall, and had a particularly strong effect for non-managers, women and those with longer commutes (Source: Bloom et al) FROM MY DESK June saw the final three episodes of series 39 of the Digital HR Leaders podcast, which was kindly sponsored by our friends at Crunchr. Thank you to Ralf Bovers and Dirk Jonker for your partnership and support. Also included are two articles inspired by series 38 and 39 of the podcast respectively. DIRK JONKER – Driving Business Transformation with Advanced People Analytics - Dirk Jonker, one of the most knowledgeable and passionate leaders in the people analytics field, joins me to discuss how people analytics is enabling HR to play a more active role in business transformation and strategy. ARMAND SOHET - Painting the Future of HR with AI, Analytics and Curiosity - Armand Sohet, Chief Sustainability, HR, and Communications Officer, joins me to discuss how a data-driven approach to HR has led to substantial cost savings and efficiency gains at AkzoNobel. ERIN GERBEC – How Cardinal Health Transformed Their People Analytics Function – Erin Gerbec, Ph.D. shares insights from her three-year journey of transforming the people analytics operating model at Cardinal Health, and how they shifted from a build to a buy strategy for its people analytics platform. DAVID GREEN - Revolutionising Workplace Experience through Employee Insights and Analytics – In this article for myHRfuture, I look at how people analytics and AI is reshaping the employee experience through the eyes of recent guests on the Digital HR Leaders podcast including Loren I. Shuster Nickle LaMoreaux and Craig Starbuck, PhD. DAVID GREEN - How can HR leaders use people analytics to uncover and address inclusivity gaps? – A round up of series 39 of the Digital HR Leaders podcast, with insights from Daisy Auger-Domínguez (she/her/ella), Kate Bravery, and Ilya Bonic as well as Dirk, Armand, and Erin. PETER SCHULZ-RITTICH AND DAVID GREEN – D as in Data Analytics – In June, I also had the pleasure of joining Dr. Peter Schulz-Rittich Caroline Amalie Allard and Christina May on ISS’s A People and Culture Podcast to discuss the power of people analytics within HR, where we are today – and where we are going. LOOKING FOR A NEW ROLE IN PEOPLE ANALYTICS OR HR TECH? I’d like to highlight once again the wonderful resource created by Richard Rosenow and the One Model team of open roles in people analytics and HR technology, which now numbers over 500 roles. THANK YOU Thomas Kohler for including the May edition of Data Driven HR Monthly in his round-up of resources for HR professionals Amit Mohindra (here), Oliver Kasper (here) and Michelle Deneau (here) for sharing details on the Insight222 People Analytics Trends survey for 2024 Neeru Monga (here), Agostina Verni (here), and Tristan Hack (here) for sharing takeaways from the recent Insight222 webinar on Transforming HR and People Analytics with AI. Sharon Saldanha for sharing her key learnings on the Digital HR Leaders podcast episode with Kate Bravery and Ilya Bonic on the importance of skills and trust Similarly, Olimpiusz Papiez also highlighted the relationship between trust and employee engagement, productivity and retention, which Ilya, Kate and I discussed in the Digital HR Leaders podcast episode: Navigating the Future of Work: AI, Skills, and Trust in the Modern Workforce. Marcela Niemeyer for recommending and sharing her key learnings on the Digital HR Leaders podcast episode with Nickle Lamoreaux on How IBM uses AI to transform HR. Harisenin.com for including me in their list of 12 people to follow for HR professionals on LinkedIn. Aurélie Crégut for sharing her key takeaways (here) from the Digital HR Leaders podcast episode with Dirk Jonker: Driving Business Transformation with Advanced People Analytics Alicia Roach for posting about the fifth anniversary of the Digital HR Leaders podcast (see here), which included her episode, How Strategic Workforce Planning Contributes to Business Success, ranking in the top 10 most listened to episodes! Sonali Kumar for sharing her learnings on the Digital HR Leaders podcast episode with Piyush Mehta, How to Create Personalised Employee Experiences. Thank you to everyone that contributed to the amazing video celebrating ten years of the Data Driven HR monthly: Sue, Jeremy. Eden, Kalifa, Lexy, Greg, Adam, Dawn, Chris, John, Jonathon, Kris, Greg, Paul, Anna, Cole, Shannon, Al, Toby, Thomas, Dirk, Antony, Alan, Michael, Ian, Chris, Craig, Andrew, Alexis, Patrick, Sanja, Dan, Mark, Ben, Sukumaran, Stela, David, Christopher, Daisy, Serena, Tony, Chad, Pietro, Kathi, Casey, Gabe, Lydia, Mark, Allen, Nicole, Nicholas, Stephanie, Andrew, Ramesh, Hallie, Dave, Roxanne, Matt, Max, Stacia, Travis, Jordan, Kelly, Ethan, Bethany, Rob, Anthony, Meg, Shawn, RJ, Jordan, Justin, Tanmay, Jon, Christine, Nick, Madhura, Brian, Raja, Henrik, Ben, Ben, Prasad, Maja, Stacey, Courtney, Buddy, Kristin, Shujaat, Gary, Alicia, Fabian, Philipp, Irene, Nick, Ryan Hammond, Amit Mohindra Gregor Teusch and of course Richard Rosenow. Finally, a huge thank you to the following selection of people who shared the May edition of Data Driven HR Monthly. It's much appreciated: Viktoriia Kriukova (Вікторія Крюкова) Juan Antonio Vega Davina Erasmus Dan Riley Danielle Farrell, MA Ugur Zel (Prof. / ACC) Veronika Birkheim Chris Louie Jaqueline Oliveira-Cella Ganchimeg Gantulga EDLIGO Talent Analytics and Learning Analytics Ken Oehler Nick Lynn Sohil Varshney Jackson Roatch Graham Tollit RADICL Adam Tombor (Wojciechowski) Reshma Mawji Jeremy Carpenter, M.S., MPA Terri Horton, EdD, MBA, MA, SHRM-CP, PHR Bilal Laouah Catriona Lindsay Ayomide Ebietomiye Irada Sadykhova Caroline Arora Lawson Iduku German Arciniegas Brandon Merritt Johnson Jim de Vries Dave Millner Aravind Warrier Terrance Edwards David Simmonds FCIPD Stefano Di Lauro Francesca Caroleo, SHRM-SCP, ICF-ACC Emmanuel Dominick Chris Long Cedric Borzee Maria Alice Jovinski Aurélien GOZET Aizhan Tursunbayeva, PhD, GRP Susan Knolla Markus Graf Matt Elk Robert Newry Anil Saxena Fresia Jackson Conor Gilligan Alexandra Nawrat Hanadi El Sayyed Kannu Priya Arora Patrick Svensson Phil Inskip Jennifer Moore John Gunawan Ann-Marie Clayton Johnson Roshaunda Green, MBA, CDSP, Phenom Certified Recruiter Rebecca Thielen Shilpa Shah Tom Morehead PCC,MBA,SPHR David Balls (FCIPD) Meghan M. Biro Sebastian Kolberg Olivier Bougarel Catherine Coppinger Aimee Wilkinson Andrew Bamber Matt Higgs MBA FCIPD Chandresh Natu David Duewel Nicola Wood Andrew Pitts Kerrian Soong Andrés García Ayala Sanja Licina, Ph.D. Jeremy Shapiro Chris Lovato Tatu Westling Ken Clar Brandon Roberts David van Lochem Placid Jover Ohad Geron Carly Fordham Tobias W. Goers ツ Dave Fineman Laura Thurston Higor Gomes Kirandeep Chakrabarti Stephen Hickey Liz Mackay Lina Makneviciute David McLean ABOUT THE AUTHOR David Green ?? is a globally respected author, speaker, conference chair, and executive consultant on people analytics, data-driven HR and the future of work. As Managing Partner and Executive Director at Insight222, he has overall responsibility for the delivery of the Insight222 People Analytics Program, which supports the advancement of people analytics in over 90 global organisations. Prior to co-founding Insight222, David accumulated over 20 years experience in the human resources and people analytics fields, including as Global Director of People Analytics Solutions at IBM. As such, David has extensive experience in helping organisations increase value, impact and focus from the wise and ethical use of people analytics. David also hosts the Digital HR Leaders Podcast and is an instructor for Insight222's myHRfuture Academy. His book, co-authored with Jonathan Ferrar, Excellence in People Analytics: How to use Workforce Data to Create Business Value was published in the summer of 2021. MEET ME AT THESE EVENTS I'll be speaking about people analytics, the future of work, and data driven HR at a number of upcoming events in 2024: June 25-26 - Insight222 North American Peer Meeting (Minneapolis, US) - exclusively for member organisations of the Insight222 People Analytics Program July 24 - LinkedIn Live - Skills-powered organizations in the Age of AI, with Ravin Jesuthasan and Tanuj Kapilashrami September 16-19 - Workday Rising (Las Vegas) September 24-26 - Insight222 Global Executive Retreat (Colorado, US) - exclusively for member organisations of the Insight222 People Analytics Program October 2-3 - People Analytics World (New York) October 16-17 - UNLEASH World (Paris) October 22-23 - Insight222 North American Peer Meeting (hosted by Workday in Pleasanton, CA) - exclusively for member organisations of the Insight222 People Analytics Program November 12-14 - Workday Rising EMEA (London) November 19-20 - Insight222 European Peer Meeting (hosted by Merck in Darmstadt, Germany) - exclusively for member organisations of the Insight222 People Analytics Program More events will be added as they are confirmed.
    Data privacy
    2024年07月03日
  • Data privacy
    美国劳工部发布职场人工智能使用原则,保护员工权益(附录原文) 今天5月16日,美国劳工部发布了一套针对人工智能(AI)在职场使用的原则,旨在为雇主提供指导,确保人工智能技术的开发和使用以员工为核心,提升所有员工的工作质量和生活质量。代理劳工部长朱莉·苏在声明中指出:“员工必须是我们国家AI技术发展和使用方法的核心。这些原则反映了拜登-哈里斯政府的信念,人工智能不仅要遵守现有法律,还要提升所有员工的工作和生活质量。” 根据劳工部发布的内容,这些AI原则包括: 以员工赋权为中心:员工及其代表,特别是来自弱势群体的代表,应被告知并有真正的发言权参与AI系统的设计、开发、测试、培训、使用和监督。这确保了AI技术在整个生命周期中考虑到员工的需求和反馈。 道德开发AI:AI系统应以保护员工为目标设计、开发和培训。这意味着在开发AI时,需要优先考虑员工的安全、健康和福祉,防止技术对员工造成不利影响。 建立AI治理和人工监督:组织应有明确的治理体系、程序、人工监督和评估流程,确保AI系统在职场中的使用符合伦理规范,并有适当的监督机制来防止误用。 确保AI使用的透明度:雇主应对员工和求职者透明地展示其使用的AI系统。这包括向员工说明AI系统的功能、目的以及其在工作中的具体应用,增强员工的信任感。 保护劳动和就业权利:AI系统不应违反或破坏员工的组织权、健康和安全权、工资和工时权以及反歧视和反报复保护。这确保了员工在AI技术的应用下,其基本劳动权益不受侵害。 使用AI来支持员工:AI系统应协助、补充和支持员工,并改善工作质量。这意味着AI应被用来提升员工的工作效率和舒适度,而不是取代员工或增加其工作负担。 支持受AI影响的员工:雇主应在与AI相关的工作转换期间支持或提升员工的技能。这包括提供培训和职业发展机会,帮助员工适应新的工作环境和技术要求。 确保负责任地使用员工数据:AI系统收集、使用或创建的员工数据应限于合法商业目的,并被负责地保护和处理。这确保了员工数据的隐私和安全,防止数据滥用。 这些原则是根据拜登总统发布的《安全、可靠和可信赖的人工智能开发和使用行政命令》制定的,旨在为开发者和雇主提供路线图,确保员工在AI技术带来的新机遇中受益,同时避免潜在的危害。 拜登政府强调,这些原则不仅适用于特定行业,而是应在各个领域广泛应用。原则不是详尽的列表,而是一个指导框架,供企业根据自身情况进行定制,并在员工参与下实施最佳实践。通过这种方式,拜登政府希望能在确保AI技术推动创新和机会的同时,保护员工的权益,避免技术可能带来的负面影响。 这套原则发布后,您认为它会对贵公司的AI技术使用和员工权益保护产生怎样的影响? 英文如下: Department of Labor's Artificial Intelligence and Worker Well-being: Principles for Developers and Employers Since taking office, President Biden, Vice President Harris, and the entire Biden-Harris Administration have moved with urgency to harness AI's potential to spur innovation, advance opportunity, and transform the nature of many jobs and industries, while also protecting workers from the risk that they might not share in these gains. As part of this commitment, the AI Executive Order directed the Department of Labor to create Principles for Developers and Employers when using AI in the workplace. These Principles will create a roadmap for developers and employers on how to harness AI technologies for their businesses while ensuring workers benefit from new opportunities created by AI and are protected from its potential harms. The precise scope and nature of how AI will change the workplace remains uncertain. AI can positively augment work by replacing and automating repetitive tasks or assisting with routine decisions, which may reduce the burden on workers and allow them to better perform other responsibilities. Consequently, the introduction of AI-augmented work will create demand for workers to gain new skills and training to learn how to use AI in their day-to-day work. AI will also continue creating new jobs, including those focused on the development, deployment, and human oversight of AI. But AI-augmented work also poses risks if workers no longer have autonomy and direction over their work or their job quality declines. The risks of AI for workers are greater if it undermines workers' rights, embeds bias and discrimination in decision-making processes, or makes consequential workplace decisions without transparency, human oversight and review. There are also risks that workers will be displaced entirely from their jobs by AI. In recent years, unions and employers have come together to collectively bargain new agreements setting sensible, worker-protective guardrails around the use of AI and automated systems in the workplace. In order to provide AI developers and employers across the country with a shared set of guidelines, the Department of Labor developed "Artificial Intelligence and Worker Well-being: Principles for Developers and Employers" as directed by President Biden's Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, with input from workers, unions, researchers, academics, employers, and developers, among others, and through public listening sessions. APPLYING THE PRINCIPLES The following Principles apply to the development and deployment of AI systems in the workplace, and should be considered during the whole lifecycle of AI – from design to development, testing, training, deployment and use, oversight, and auditing. The Principles are applicable to all sectors and intended to be mutually reinforcing, though not all Principles will apply to the same extent in every industry or workplace. The Principles are not intended to be an exhaustive list but instead a guiding framework for businesses. AI developers and employers should review and customize the best practices based on their own context and with input from workers. The Department's AI Principles for Developers and Employers include: [North Star] Centering Worker Empowerment: Workers and their representatives, especially those from underserved communities, should be informed of and have genuine input in the design, development, testing, training, use, and oversight of AI systems for use in the workplace. Ethically Developing AI: AI systems should be designed, developed, and trained in a way that protects workers. Establishing AI Governance and Human Oversight: Organizations should have clear governance systems, procedures, human oversight, and evaluation processes for AI systems for use in the workplace. Ensuring Transparency in AI Use: Employers should be transparent with workers and job seekers about the AI systems that are being used in the workplace. Protecting Labor and Employment Rights: AI systems should not violate or undermine workers' right to organize, health and safety rights, wage and hour rights, and anti-discrimination and anti-retaliation protections. Using AI to Enable Workers: AI systems should assist, complement, and enable workers, and improve job quality. Supporting Workers Impacted by AI: Employers should support or upskill workers during job transitions related to AI. Ensuring Responsible Use of Worker Data: Workers' data collected, used, or created by AI systems should be limited in scope and location, used only to support legitimate business aims, and protected and handled responsibly.
    Data privacy
    2024年05月16日
  • Data privacy
    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/
    Data privacy
    2024年04月15日
  • Data privacy
    Workday: It’s Time to Close the AI Trust Gap Workday, a leading provider of enterprise cloud applications for finance and human resources, has pressed a global study recently recognizing the  importance of addressing the AI trust gap. They believe that trust is a critical factor when it comes to implementing artificial intelligence (AI) systems, especially in areas such as workforce management and human resources. Research results are as follows: At the leadership level, only 62% welcome AI, and only 62% are confident their organization will ensure AI is implemented in a responsible and trustworthy way. At the employee level, these figures drop even lower to 52% and 55%, respectively. 70% of leaders say AI should be developed in a way that easily allows for human review and intervention. Yet 42% of employees believe their company does not have a clear understanding of which systems should be fully automated and which require human intervention. 1 in 4 employees (23%) are not confident that their organization will put employee interests above its own when implementing AI. (compared to 21% of leaders) 1 in 4 employees (23%) are not confident that their organization will prioritize innovating with care for people over innovating with speed. (compared to 17% of leaders) 1 in 4 employees (23%) are not confident that their organization will ensure AI is implemented in a responsible and trustworthy way. (compared to 17% of leaders) “We know how these technologies can benefit economic opportunities for people—that’s our business. But people won’t use technologies they don’t trust. Skills are the way forward, and not only skills, but skills backed by a thoughtful, ethical, responsible implementation of AI that has regulatory safeguards that help facilitate trust.” said Chandler C. Morse, VP, Public Policy, Workday. Workday’s study focuses on various key areas: Section 1: Perspectives align on AI’s potential and responsible use. “At the outset of our research, we hypothesized that there would be a general alignment between business leaders and employees regarding their overall enthusiasm for AI. Encouragingly, this has proven true: leaders and employees are aligned in several areas, including AI’s potential for business transformation, as well as efforts to reduce risk and ensure trustworthy AI.” Both leaders and employees believe in and hope for a transformation scenario* with AI. Both groups agree AI implementation should prioritize human control. Both groups cite regulation and frameworks as most important for trustworthy AI. Section 2: When it comes to the development of AI, the trust gap between leaders and employees diverges even more. “While most leaders and employees agree on the value of AI and the need for its careful implementation, the existing trust gap becomes even more pronounced when it comes to developing AI in a way that facilitates human review and intervention.” Employees aren’t confident their company takes a people-first approach. At all levels, there’s the worry that human welfare isn’t a leadership priority. Section 3: Data on AI governance and use is not readily visible to employees. “While employees are calling for regulation and ethical frameworks to ensure that AI is trustworthy, there is a lack of awareness across all levels of the workforce when it comes to collaborating on AI regulation and sharing responsible AI guidelines.” Closing remarks: How Workday is closing the AI trust gap. Transparency: Workday can prioritize transparency in their AI systems. Providing clear explanations of how AI algorithms make decisions can help build trust among users. By revealing the factors, data, and processes that contribute to AI-driven outcomes, Workday can ensure transparency in their AI applications. Explainability: Workday can work towards making their AI systems more explainable. This means enabling users to understand the reasoning behind AI-generated recommendations or decisions. Employing techniques like interpretable machine learning can help users comprehend the logic and factors influencing the AI-driven outcomes. Ethical considerations: Working on ethical frameworks and guidelines for AI use can play a crucial role in closing the trust gap. Workday can ensure that their AI systems align with ethical principles, such as fairness, accountability, and avoiding bias. This might involve rigorous testing, auditing, and ongoing monitoring of AI models to detect and mitigate any potential biases or unintended consequences. User feedback and collaboration: Engaging with users and seeking their feedback can be key to building trust. Workday can involve their customers and end-users in the AI development process, gathering insights and acting on user concerns. Collaboration and open communication will help Workday enhance their AI systems based on real-world feedback and user needs. Data privacy and security: Ensuring robust data privacy and security measures is vital for instilling trust in AI systems. Workday can prioritize data protection and encryption, complying with industry standards and regulations. By demonstrating strong data privacy practices, they can alleviate concerns associated with AI-driven data processing. SOURCE Workday
    Data privacy
    2024年01月11日