David Green: People Analytics已经从外围进入到HR的核心
David Green是全球知名的人才分析，数据驱动人力资源和未来工作的作家，演讲者，会议主席和执行顾问。
David在帮助组织开展和加速人员分析之旅方面拥有丰富的经验。在与People Matters的独家互动中，David也将在TechHR 2019上发表演讲，分享他对组织如何利用人员分析来实现积极的业务和员工成果的看法。
全球人力分析浪潮的主要趋势是什么？
自从我六年前第一次进入该领域以来，人们对分析的兴趣水平，采用率和重要性的提升是惊人的。在此期间，我看到人力资本分析从人力资源的外围转移到人力资源中心，现在这种做法已成为劳动力战略，数字化转型以及改善员工体验的核心组成部分。
人员分析的研究一致突出了空间的增长。例如，企业研究论坛2017年战略劳动力分析研究发现，69％的大型组织现在拥有人员分析团队。因此，人才流入该领域，LinkedIn的一项研究发现，到2018年的五年内，仅亚太地区的人力资源专业分析专业人员就增加了70％。也许并不奇怪，人们分析根据我们在于未来人力资源技能的研究，这也是人力资源专业人员在2019年想要学习的最需要的技能。
鉴于有越来越多的证据表明人力资本分析可以从根本上改善公司的利润，我们可以预期未来几年会持续增长。
人力资本分析（和人力资源）领导者应该问自己哪些关键问题可以确保他们在正确的道路上获得业务影响和差异化的员工体验？
在我与Jonathan Ferrar一起开发的Nine Dimensions for People Analytics模型中，作为我们在Insight222工作的一部分，我们确定了七个关键问题，人力资源领导者应该问自己检查他们是否正在通过人力资本分析获得成功的正确途径。
1.您是否有人力资本分析的道德行为准则？
2.您的人力资本分析项目是否已使用适当的治理和标准进行选择和优先排序？
3.您的高级业务主管是否赞助人员分析项目？
4.您的首席人力资源官是否投资人员分析？
5.您的人力资本分析负责人是否具备“完成任务”的技能？
6.您是否衡量人员分析项目的投资回报率？
7.您的人力资源业务合作伙伴是否有能力进行基于证据的讨论？
（1. Do you have an Ethics Code of Conduct for People Analytics?
2. Are your People Analytics projects selected and prioritized using proper governance and criteria?
3. Do your senior business executives sponsor People Analytics projects?
4. Does your Chief Human Resources Officer invest in People Analytics?
5. Does your People Analytics Leader have the skills to “get things done”?
6. Do you measure the ROI of your People Analytics projects?
7. Are your HR Business Partners capable of having evidence-based discussions?）
通过人员分析提供的见解可以解决哪些业务挑战？
人力资本分析为组织提供了巨大的潜力，可以推动业务战略，提高生产力和绩效，以及个性化和增强员工体验。
例如，尼尔森发现，每减少1％的损耗，就可以避免5美元的商业成本。同样的分析将内部流动性确定为保留的关键驱动因素。因此，领导层使员工更容易在公司内部寻求机会，并创建了人力资源计划以促进内部流动。该计划使尼尔森仅在前八个月就节省了超过10万美元的锰，增加了横向移动的数量，并随后在整个公司推出。
这只是人力资本分析可以解决的业务挑战的一个例子。下面的图1说明了我看到公司使用人员分析解决的其他业务挑战的示例。
图1：人力资本分析项目的业务和人员结果示例（来源：David Green）
组织如何利用人力资本分析来实现积极的业务和员工成果，以及改善人力资源的影响？
当Jonathan和我开发上述九维卓越人才分析模型时，我们确定了组织为企业，员工和人力资源部门创造更多影响力和价值所需的因素。
总之，这九个维度分为三个核心领域：基础，资源和价值：
人力资本分析需要强大的基础才能产生影响：
强有力的治理，数据标准和道德规范
强大的方法和优先级 - “从业务问题开始，而不是数据”
支持利益相关者和商业赞助商
需要正确的资源才能使人员分析能够实现成果：
平衡而熟练的团队
有效的技术
高质量的人力资源和业务数据
有影响力的人力资本分析在多个层面创造价值：
员工，经理和领导者的劳动力体验
高管的业务成果
数据驱动的文化和对人力资源的影响增加
在应对数字化转型的挑战时，人力资本分析提供了哪些机会？
我们听到很多关于工作的未来和人力资源在人力资源中使用的大量谈话 - 坦率地说是大肆宣传。
“我看到更多公司积极追求的一个机会是围绕个性化主题 - 为员工提供类似消费者的体验”。
我认为最合适的地方是HR直接与员工合作，共同创建人力资源计划，推动个人化，如组织内的入职，学习和职业道路。这涉及从过去的“一刀切”人力资源计划中激进而受欢迎的转变，不仅对员工更好，而且还支持诸如更好的员工规划和增加员工敬业度和企业保留等举措。
没有数据和分析，这一切都不可能。如何在没有数据的情况下为员工个性化学习和职业发展道路？你不能。这可能是为什么进一步推进数字化人力资源转型的公司也是那些在人员分析方面更先进的公司的最佳指标。数字和分析是密切相关的。
内容来自Peoplematters，作者Shweta Modgil 。感谢他们！
以上由AI翻译完成，仅供参考，如需了解更多可以查看英文：
https://www.peoplematters.in/article/techhrin/analytics-has-moved-from-the-periphery-towards-the-centre-of-hr-david-green-22338
People Analytics
2019年07月26日
People Analytics
英文阅读：PEOPLE ANALYTICS UNCOVER THE OPTIMAL SPAN OF CONTROL英文阅读，HRTechChina每周会选取一篇HR科技专业方向的英文原版文章供大家学习。
The HR-functions of more and more companies have started a journey to establish and use people analytics – as a response to make HR more data-driven and in an effort to combine HR-activities and decisions closer to the top and bottom line.
People analytics is many different things and can do many different things. I will just address one of the many areas where HR-analyses can move the company from one level to the next.
SPAN OF CONTROL
Imagine a company that wants to reduce the number of managers because they want a more flat and hence, agile company structure. At the same time, they want to cut costs by saving on wages to a list of managers.
In other words, the company wants to change span of control, i.e. how many employees the managers supervise.
It can surely be a reasonable decision to work with span of control. But does the intervention also have negative consequences that the company should be aware of?
Here, HR-facts can provide insights so that top-management can make a more intelligent decision on a much better foundation.
RESEARCH THE CONSEQUENCES UP FRONT
The most obvious consequence you do not need analysis to see is that the remaining managers will have more employees to supervise.
But it is less obvious how it will affect the employees’ well-being and productivity and hence, the top line and bottom line of the company.
At Ennova, we have done a number of analyses of span of control across more than 12,000 teams all over the world. A couple of the overall conclusions are:
The more employees a manager supervises, the lower the assessment - particularly immediate manager and top management
The larger team size, the lower engagement and lower willingness to recommend the workplace to others (eNPS)
Span of Control
UNCOVER THE MORE COMPLEX LINKS
However, when we study the numbers a little closer it is much more differentiated.
Some specific types of managers are able to handle a large span of control. For example, in several companies we have seen that managers in units close to the customers can easily supervise more than 15 employees, yet still receive a good score from the employees, and still have a high level of engagement in the team.
On the contrary, some groups of employees, e.g. talents, thrive better in teams with a lower span of control. Among other things, the explanation for this is that this type of employees requires more one-on-one feedback and a closer focus to support the development. This sets a natural limit for the number of employees the manager can handle.
A third example that makes everything even more complex is that companies have different capabilities to handle span of control.
Some companies are capable of having larger teams. The managers there are simply better equipped to handle the challenges that inevitably will occur with more employees on the team.
THERE IS A STRONG BUSINESS CASE FOR SPAN OF CONTROL ANALYSES
Hence, analyzing the specific conditions in your company will produce an invaluable insight into the consequences of employee engagement. And the engagement is a critical factor for both top line and bottom line.
From a number of global analyses we know that engagement is closely related to the risk of voluntary resignations. The lower the engagement in a team, the bigger the risk that your employees resign.
This will naturally affect the company’s top line and bottom line - particularly if key employees and talents resign. Recruitment and onboarding of new employees require a lot of resources. At the same time, there is often a void and deliveries are running at a lower level until the new employee has been fully integrated.
Therefore, span of control analyses are also justified from a people analytics perspective, where the premise is the combination with business outcome.
EVERY ORGANIZATION NEEDS ITS OWN ANALYSIS
Hence, the conclusion is that there is not one optimum span of control. The context is absolutely critical for the number of employees a manager can reasonably handle.
For example, whether the manager is extrovert or introvert has an impact. As mentioned, teams with a lot of talents require a lower span of control. Whether the team consists of employees with high seniority and employees who are more or less “self-managing” also has significance.
Hence, all companies would benefit from conducting span of control analyses. The context in which managers operate is unique and specific. Hence, it is necessary to examine how you can optimize span of control across your particular organization.
Author SØREN SMIT. DIRECTOR
Søren wants to teach companies to use fact-based customer insights instead of gut feelings. He has written a Danish book on how professional customer insights can determine the difference between financial success and failure, and he is in charge of Ennova’s business development.
原文来自：https://www.ennova.com/en/employee-experience-insights/people-analytics-uncover-the-optimal-span-of-control
人员分析:在人员流动模型中建立可解释性文/Ridwan Ismeer
最近，我有幸与来自新加坡理工大学的一群才华横溢的学生一起工作。他们的任务是帮助构建一个非常普通的人员分析应用程序:预测员工流动率(此类应用程序的优点、相关性和伦理值得商榷，可以单独讨论)。
摘要:建立一个能够准确预测员工情绪的模型，在0-6个月，6-12个月和>12个月的时间范围内的周转风险。
这两项不可谈判的要求是:
1.准确性:真阳性高，假阳性低。大多数实践者会强调低假阴性，但我们有理由不这么做。
2.可解释性:在人员分析中，模型的可解释性是采用模型的关键。人员分析的最终用户通常想要理解为什么模型要预测它是什么。事实上，GDPR有新的规定要求人工智能的决定是可解释的。
现在，任何分析实践者都可以很快地指出，这两个需求之间存在一个内在的平衡。精确的模型很少是可解释的。可解释的模型很少是准确的。但我们想检验这个假设的二分法。因为在人员分析中，仅仅精确是不够的——它需要用户能够理解。
除了我们两个严格的要求外，我们还为团队提供了一个强大的人力资源指标列表、一个足够大的数据集以及评估以下算法所需的基础设施:
和往常一样，xgboost在预测营业额方面表现最好(引用Kaggle上最常用的算法之一)。事实上，它的TP和FP速率满足了我们对精度的要求。容易解释的模型，如GLM和逻辑回归只是没有比较。
然而，任何以前使用过这个算法的人都可以证明，要想弄清楚它的黑盒子里发生了什么是多么困难。我们可以告诉股东鲍勃的离职风险很高，但我们无法解释原因。
或者我们可以吗?
将可解释性构建到像XGBoost这样的算法中并非易事，但这是可能的。除了向涉众提供处于风险中的员工的姓名之外，我们还为他们提供了一个交互平台，用于修改现有的功能，并重新运行模型，以指向导致模型将其评为处于风险中的功能。如果鲍勃去年升职了，模特会得出同样的结论吗?是的,它将。如果Bob在一个较小的团队中，模型会得出相同的结论吗?是的,它将。如果他的工资比市场上的要高呢?不。瞧。
由于用户需要进行多次迭代才能更好地理解每个案例，因此需要进行大量的工作，但是它允许我们保持较高的准确性，同时为涉众提供必要的模型内部工作，以使其更易于解释。
一些免责声明:
1.本帖旨在解决可解释性和准确性之间的错误二分法，而不是鼓励使用个人离职模型。事实上，我甚至会说，诸如加薪和提供晋升等行动绝不应以离职风险为基础。这对精英文化来说可能是灾难性的。对一般离职动因的综合分析应该是离职模型所能做到的。
2.首先，关于可解释性的必要性有很多争论。埃尔德研究中心的约翰·埃尔德博士认为，人类过于依赖基于先前经验的确认偏差，无论如何都无法客观地解释模型的结果。辩论还在继续。点击这里了解更多内容。
3.图像中使用的数据完全是基于虚假数据，仅用于说明方法。
4.我有自己的看法。
以上为AI翻译，内容仅供参考。
原文链接：People Analytics: Building for Interpretability in Turnover Models
People Analytics
2018年11月30日
People Analytics
人力资源和工作流程——生产力系统 HR & the flow of work – Systems of Productivity文/J Jerry Moses
文章导读：
在TechHR18会议的第2天，德勤(Deloitte)的Bersin创始人乔什•伯辛(Josh Bersin)对新一代招聘、管理、学习、职业和员工体验工具是如何从根本上扰乱市场进行了分析。
他认为：技术与商业问题相一致，技术才有意义。
以下是Josh Bersin在TechHR 18会议上对人力资源技术趋势的一些见解：
技术、自动化、机器人技术都在发挥作用!
生产力是一个问题!
作社会型企业!
持续的性能管理具有巨大的影响——获取工具
大型人力资源技术供应商没有跟上步伐
人才管理需要工具来处理
我们给员工发工资方式将会被打乱
企业学习才是真正的事情!
员工福利市场具有真正的潜力
软件市场正在成长
员工体验进入工作流程
英文原文：
On Day 2 of TechHR18, Josh Bersin,Founder of Bersin by Deloitte, presents a research-based analysis of how a new generation of recruiting, management, learning, career, and employee experience tools are radically disrupting the marketplace
Micro trends are driving change – changes in the HR technology landscape, the way we work, and particularly, the changes in how organizations are being managed and are managing. The world of HR and HR tech is undergoing a significant shift. HR is now over Cloud, Social and Mobile – this is the time for a new breed of systems - intelligent platform strategies that are making HR and its processes real-time, productive, agile and data-driven.
But “Nothing in technology makes sense unless its aligned with the business problems we are trying to solve” as Josh Bersin says. Here are a few insights on HR tech trends from Josh Bersin’s session at TechHR’18.
Technology, Automation, Robotics are here and they work!
According to Bersin’s research, 45 percent of companies are still focused on basic process automation. The business ecosystem is almost a decade into the economic growth, and has a plethora of generations working together in it. We are living longer, the average career spans 70-75 years, and technology is disrupting where we work along with our daily lives. Most of HCM trends, technology, robotics, AI, automation, is actually becoming real. However, we don’t know what to deal with it all because most companies are still struggling with the challenges of the right skills, structures, organizational design, and rewards systems.
Productivity is an issue!
Productivity is lagging. The real key for HR going forward is becoming the Chief of Productivity. If employees use products and tools that the organizations provide to them, employes will feel better, happier, and engaged. And this is the secret of what is going to happen to HR technology – building systems for the HR that make people productive. With agility, team-centric organizations, burnout is becoming an issue while employee engagement and communication tools are overwhelming employees. This is the time for businesses to build HR software that really improves productivity and helps teams work better together?
Business as a social enterprise!
CEOs are now being asked to take social positions on topics and act on behalf of communities, stakeholders, shareholders, and employees and customers. The future of business is in becoming a socially conscious enterprise and here, the most important thing would to be to develop a technology strategy that provides purpose, meaning, transparency and fairness. Businesses can no longer afford to buy technology that implements practices that someone else coded.
Continuous Performance Management has a huge impact – get the tools
Continuous performance management is transformative. It really and truly works! Ratings will not go anywhere but the crucial part will be to build newer and continuous processes for goal setting, coaching, evaluation, and feedback. This is time for organizations to reconsider performance philosophy. Even with the success of the cloud HCM vendors in the market, a comprehensive solution for performance management is not available. “Team-centric” tools will be the future of HCM market in the future.
Big HR Tech vendors are not keeping up
Most of the ERP vendors are struggling to keep up with the evolution and changes in the business ecosystem. ERP vendors are not getting good marks for ease of use, integration, or value to the end users or employees. There is a stiff competition in the ERP market and it is becoming crowded.
Talent management is done!
The whole idea of Talent Management was about pre-hire to retire. But we don’t work like that anymore. Most of us work at many companies during our careers and organizations are also going through change, disruption and reorganization. Managing employees through the entire lifecycle is not really the problem but managing employees in a new management environment that is about teams, empowerment, mission, purpose, clarity and transparency of goals. It’s a totally different management environment and we need tools to deal with that.
How we pay people will be disrupted
The most disrupted area of HR to come is the way we pay people. Only 1 in 5 companies believes that their rewards system is actually aligned with their corporate strategy. We are still paying people the way we did in the past — salary bands, annul reviews, policies of secrecy and who is getting paid what – all this will be disrupted and we will have a whole new set of tools for employee experience.
Corporate Learning is the real deal!
Platforms like Degreed and Edcast are transforming corporate learning — experience platforms, micro-learning platforms, modernized LMS systems, AI-based systems to recommend learning, find learning, and deliver learning, and Virtual Reality-based learning are giving employees and organizations all the things they need.
Employee wellbeing market has the true potential
It’s all about the moments that matter. There is a need to improve productivity but there is a significant impetus on employee wellbeing, reducing the cognitive overload and augmenting human performance. This vendor market is moving fast. The new world of work will be about “engagement, productivity, and wellbeing” all in one.
ONA software market is now growing
With the explosion of HRMS data, wellbeing data, networking data, among many other forms of structured and unstructured data, HR is struggling to deal issues of ethics, privacy, and becoming more transparent about the analytics they are doing. The Organizational Network Analytics is growing and so is a new world of “relationship analytics”. People Analytics will guarantee success.
Getting into the Flow of Work
Employee experience is the buzzword and we are trying to reform it in a way that applies and improves the work experience of every individual in an organization. Organizations define employee experience as a project of looking at the moments that matter, transitions, periods of time in career where one is stressed and what can HR do to make that easier. But none of the tools are designed to measure or map something like this. All tools are designed for the HR function, not this. There is a new category of software being built to help HR with the employee experience - to shield employees from the complexities of the backend HR systems and deliver all the different things the HR does in the flow of work.
如何为人力分析专业人士创造职业道路-How to create career paths for people analytics professionals（续）文/David Green
文章导读
往期回顾：
Geetanjali在2017年9月在费城举行的人力分析与未来工作会议上发言要点回顾：
MERCK&CO.的人力分析团队
这个团队由三支柱组成：咨询、高级分析、报告和数据可视化
创建一个数据驱动的文化：高层领导的支持对于人员分析功能的成功至关重要
在人力分析中创造职业道路：一个能够提供发展和职业发展的组织和领导者，可以成为吸引和留住人才的关键因素。
三“C”模式：Capability-Capacity-Connectivity
今日导读：
领导人员分析团队
问7、在谈到你作为一个人分析领导者的角色时，你会对这个角色的新手或者将来想成为一个人分析负责人的人提出什么建议呢?
分享五个我认为普遍适用的特性，并且对于成为这个领域的有效领导者很重要。
优先考虑：对于人员分析领导者来说，学习如何无情地优先考虑团队将花费时间和精力的项目是至关重要的。
位置: 一个好的领导者知道如何找到合适的机会去重新定位、结合和展示这项工作。这不仅对获得声望和对人员分析的认可很重要，而且对提升团队的士气也很重要。
连接: 当你建立起新的职业联系时，你也开始建立友谊，这是一个支持网络，可以帮助你在这个相当模糊的、新的人力分析空间中导航。
与时俱进：作为一个优秀的人员分析领导者，重要的一点是要跟上外部变化的步伐，并将这种学习带回您的业务中
发展:一个有效的领导者需要投入时间和精力来建立自己的内部和外部网络，并与他们的团队分享他们的进
问8、我观察到的一个挑战是，作为一个人分析的领导者，你必须平衡在内部构建能力的重大挑战，同时关注在外部快速发展的领域。作为一名分析人士的领导者，你如何平衡这两个优先事项，以及你如何了解公司外部发生的事情?
尽可能多地阅读各种不同的出版物(博客、文章、白皮书、书籍)，这些内容让我与人力分析的各个方面：从社会科学到人工智能都保持联系。
此外，与来自不同行业的其他从业者建立联系很有帮助，我通过非正式的和正式的对等网络进行联系。
最后，我试着每年参加一些活动来学习新的东西和认识新的人。
人力分析的未来
问9、你认为人力分析的主要趋势是什么?
我认为人力分析中的一些“热点领域”将在未来继续变得“更热”。
我还认为，随着研究的增长和越来越多的组织对这一领域的投资，网络的力量将得到充分的挖掘和释放。
最后，要实现所有这些类型的分析，最重要的领域之一将是关于数据使用、隐私和人员分析领域的安全性的伦理研究。
问10、我们如何平衡我们能做什么以及我们应该做什么？ 谈谈你对道德和隐私等方面的关注。
过度反应或倾向于采用过于保守的方法，这可能会妨碍人员分析领域的一些重要工作。
话虽如此，与适当的实践专家密切合作，就业法律、隐私法律、伦理、通信、业务合作，和工人委员会合作是一个很好的方式,以确保除了工作的合法性。
另一种从道德角度是预先与内部客户分享你分析的可能结果，并向他们清楚地说明在每个场景中他们将采取什么行动。
在人力分析领域工作类型需要把伦理放在最重要的日程上
英文原文：
LEADING THE PEOPLE ANALYTICS TEAM
7. Turning towards your role as a People Analytics Leader, what would your advice be to someone who is new to this role or who aspires to be a Head of People Analytics in the future?
I think everyone has different strengths and experiences, which means their approach will vary with regards to them proving successful as a people analytics leader. But based on my personal experiences and observations of others, I can share five attributes that I think apply universally and are important to being an effective leader in this space.
Prioritise: Whether you have a small or large people analytics team, it will never be big enough to meet all the demands of your clients, particularly as awareness of the team’s capabilities grow. So, it is critical for the people analytics leader to learn (and teach!) how to relentlessly prioritise the projects on which the team will spend its time and effort. A good rule of thumb is to think about the magnitude of business impact that an analysis has the potential to deliver, or a key relationship that it can help build in the business for future collaborations and sponsorship. Many teams even use formal prioritisation grids to help the process, but ultimately the leader needs to ensure that the criteria used to allocate resources to projects aligns with the vision and mission of the people analytics team (which in turn, should align with the objectives of the enterprise).
It is critical for the people analytics leader to learn (and teach!) how to relentlessly prioritise the projects on which the team will spend its time and effort.
Position: A critical skill for a people analytics leader is the ability to effectively position analyses before the right decision-makers at the right time to maximise positive outcomes and build a strong people analytics brand. This is probably one of, if not the most, important part of being a people analytics leader. On many occasions, brilliant workforce analyses have been underutilised in their original scope, but a good leader knows how to find the right opportunities to repurpose, combine and present this work. This is not only important in gaining prestige and recognition for people analytics, but also for boosting the morale of the team.
Connect: There is a small, but growing, community of people analytics leaders globally who collectively have a spectacular amount of experience and knowledge. Fortunately, this community is inclusive and generous, in terms of sharing their knowledge and connections with others in the field. The group is a great resource to learn about new technologies, techniques, vendors, and also receive tips and tricks that can help a new leader to avoid mistakes and grab the right opportunities. Most importantly, as you build new professional connections you also begin building friendships that are a support network to help you navigate this fairly ambiguous, new(ish) space of people analytics.
Evolve: Since a people analytics leader needs to have some depth in analytical methods, it is always a good idea to read, listen and learn. Thanks to social media there are amazing resources available, many of them free, that any analytics leader can and should leverage to keep oneself updated and evolving. There are some extremely prolific writers (like David Green!) who share both original and curated content on various forums including LinkedIn. Whether you are looking for detailed tutorials on advanced data science methods or want to learn about the latest technological breakthrough and its application to people data, there is a publication, podcast, or video out there on it. Another reason why this mind set of curiosity and awareness is important is because the people analytics space is sensitive primarily due to ethics and privacy reasons; and keeping a handle on that also demands a leader who keeps their eyes and ears open. An important part of being a strong people analytics leader is to keep up with the pace of change externally and bring that learning back to your business.
An important part of being a strong people analytics leader is to keep up with the pace of change externally and bring that learning back to your business
Develop: Last, but certainly not the least, a critical part of being a good people analytics leader is simply being a good leader. This implies being someone who invests in the development of their team. It is of particular importance because it is a space that has attracted a lot of exceptional talent, but still has somewhat limited opportunities for advancement. Therefore, an effective leader needs to invest time and effort in building their own internal and external network; and share it with their teams for their advancement. They should also be committed to actively finding or creating opportunities for their team members to learn new skills and develop themselves as multi-faceted professionals.
An effective leader needs to invest time and effort in building their own internal and external network; and share it with their teams for their advancement
8. One of the challenges I’ve observed in being a people analytics leader is that you have to balance the significant challenge of building capability internally whilst keeping an eye externally on what is a fast-developing field. As a people analytics leader, how do you juggle these two priorities, and how do you keep abreast of what is happening outside the organisation?
I strive to practice the same behaviours that I would advise new people analytics leaders to try. For example, I follow and subscribe to content by certain thought leaders in people analytics and read as many varied publications as possible (blogs, articles, whitepapers, books) which keep me connected to the different aspects of people analytics; from social science to artificial intelligence.
In addition, it really helps to connect with other practitioners in the field from different industries, which I do via both informal and formal peer networks. This helps to broaden one’s worldview, spark new ideas, and offers a forum to ask questions of your peers. Most likely, if you are facing a people analytics quandary, there is a leader out there who has faced it too and would be willing to share their experience.
Finally, there are a plethora of great conference events out there, and the quality and number of these keeps rising every year. I try to participate in at least a few such events every year to learn new things and meet new people.
THE FUTURE OF PEOPLE ANALYTICS
9. What do you believe will be the main trends moving forward in people analytics?
I think that a number of “hot areas” in people analytics will continue to get “hotter” in the future. The idea of employee experience will grow even wider with focus on the end-to-end experience all the way from being a prospective candidate stage to becoming an alumni of the company. This is likely to grow simultaneously with the focus on managing and optimising a new, fluid workforce that may at any one time be full-time and freelance, human and robotic.
I also think that the power of networks will be fully explored and unleashed as research grows and more organisations invest in this space. The applications of network analysis are so varied and relevant, that it should continue to gather steam in the future.
Finally, from my perspective to enable all these types of analyses, one of the most critical areas that will grow in importance will be the study of ethics relating to data use, privacy and security in the space of people analytics.
10. Finally, how do we balance what we can do with what we should do? How concerned are you about areas such as ethics and privacy?
This is a great question, and a difficult one to answer. The frontiers of what is possible are being pushed at a break-neck speed thanks to ever larger datasets being at our disposal faster, and at cheaper cost. And that pace makes it tough to process the implications in real time. In fact, this often leads to an overreaction or the inclination to adopt an overly conservative approach that can hamper some great work in the people analytics space.
That being said, I believe that an extremely important fact to understand about the space we work in is that we should not do something just because it is possible. Besides being legally compliant, the type of work being undertaken in this field needs to put ethics at the very top of the agenda even before beginning work on an analysis. Working closely with the appropriate experts in the practices of employment law, privacy law, ethics, communications, business partners and workers councils is a good way to ensure that besides the legality of the work, its potential impact on people is also being considered through the lens of ethics, privacy, and empathy. Most established organisations have extensive reviews involving these types of stakeholders already in place.
Another way to pressure test the approach from an ethics lens is to share possible outcomes of an analysis with the internal clients beforehand and ask them to articulate what actions they would take in each scenario. Obviously, this method is not possible in every situation, but when applicable it can be a useful “stop and reflect” moment.
The type of work being undertaken in the people analytics field needs to put ethics at the very top of the agenda
People Analytics
2018年07月31日
People Analytics
如何为人力分析专业人士创造职业道路-How to create career paths for people analytics professionals文/David Green
文章导读
根据德勤于2017年11月发布的“高影响力人力分析研究”(High-Impact People Analytics study), 69%的大型机构(10,000多名员工)现在拥有一个“人力分析团队”。
Geetanjali Gamel在旧金山举行的“人民分析与未来工作会议”(People Analytics & Future of Work Conference)上的演讲这个话题。Geetanjali是默克公司劳动力分析的全球领导者。在2017年9月在费城举行的人民分析与未来工作会议上发言。
为什么要人力分析?
问1、你好，Geetanjali，请解释一下吸引你到人力分析领域的原因。
我工作中最有趣的部分是理解、测量和预测人类行为及其对销售和收入等业务结果的影响。因此，我很自然地被这个机会所吸引，这个机会将科学的方法引入到人们的数据中，并帮助塑造一个组织如何为其投资者带来价值，同时为其员工带来更丰富的经验。
MERCK & CO.的人力分析团队
问2、请您描述一下默克公司的劳动力分析团队的规模和结构，以及它是如何与业务联系起来的。
默克的劳动力分析团队(WFA)拥有15名成员，在全球80多个市场，69000名员工。
这个团队由三个主要支柱组成：咨询、高级分析、报告和数据可视化。
咨询——每个咨询师都与我们的业务部门(如制造、研究、销售等)保持一致。他们与领导者紧密合作，以理解和预见棘手的业务问题，并运用正确的方法解决问题，将分析转化为可操作的观点。
高级分析——高级分析团队是一群灵活的数据科学家和专业人士，他们主要专注于需要高级技术技能或很有意义的项目。它们围绕业务问题进行组织。
报告和数据可视化——他们直接与来自业务各个部门的内部客户合作，以确保合适的人在合适的时间拥有合适的数据。驱动了内部客户满意度。
三个WFA团队紧密合作，以确保识别和利用业务活动之间的协同作用。
创建一个数据驱动的文化
问3、德勤(Deloitte)的“高影响力人物分析”(High-Impact People Analytics)研究发现，在创造高级能力方面，最重要的因素是需要创建数据驱动的文化。你在默克公司是如何做到这一点的?
我们首先在人力资源社区中推广数据，推出了一个基于云的劳动力分析平台。我们还开发和部署了一个能力构建程序，其中的模块主要集中在度量选择、假设测试、数据可视化、推荐开发等方面。
此外，我们一直在利用的另一个渠道，加速人力资源数据驱动文化，是让我们更广泛的人力资源社区的成员成为分析“冠军”。
最后，我们还建立了一个人力资源领导团队，在人力资源中传达建筑数据和分析能力的信息。
高层领导的支持对于人员分析功能的成功至关重要
在人力分析中创造职业道路
问4、您对为人力分析专业人员创建职业发展道路充满热情。 为什么你认为这是如此重要？
我热衷于为那些使人力分析成为可能的人们建立更好的工作体验！ 我发现这个团队能够为职业道路，继任计划和大型员工的人才流动等领域做出决策，但经常陷入无处可扩展的境地。
此外，大多数人分析团队都是人力资源部门的一员，而且往往被贴上高度专业化的“人力资源精英”卓越中心(CoE)的标签，这限制了横向或向上进入CoEs或业务部门的其他人力资源角色的机会。
最后，一个能够提供发展和职业发展的组织和领导者，可以成为吸引和留住优秀人才的关键因素。
如果我们能让更多人力分析人才流动起来，就会为人力资源和企业的其他部门增加技能、方法和拓宽视角，为企业创造额外的价值。
一个能够提供发展和职业发展的组织和领导者，可以成为吸引和留住优秀人才的关键因素
问5、关于人才分析团队的职业发展，你在默克制定了什么计划?关于人才分析团队的职业发展，你在默克制定了什么计划?
从我在默克公司工作的第一天起，我的首要任务之一就是了解我的团队的力量和抱负，并将他们的发展与他们的职业目标结合起来。我得出了一个Capability-Capacity-Connectivity模型，为我们的人员分析团队提供一个可持续发展项目。这种模式成功的一个关键驱动力是你的领导的支持和与其他团队的合作。
问6、职业发展计划的主要好处和收获是什么?
“3C”方法是围绕解决障碍和为人学分析团队创建促进职业发展的桥梁而构建的。
第一个“C”：能力，能力必须在两个级别上处理。
能力级别1：构建数据、技术和分析精明的客户
能力级别2：提升人员分析团队
第二个“C”：Capacity容纳度
如果没有时间远离日常的活动，就不可能专注于一个人职业生涯的下一步
第三个“C”：连接
将人员分析团队与其他人力资源，数据科学，技术和业务专业人员联系起来，建立对双方不同类型工作的认识和相互欣赏。
英文原文：
According to Bersin by Deloitte’s High-Impact People Analytics study, which was published in November 2017, 69% of large organisations (10,000+ employees) now have a people analytics team.
It is a surprise then that many organisations overlook the need to develop the careers of their people analytics team. Given the pace of evolution of the field and the high-demand for talent in the space, this is an oversight that needs correction.
As such, it was refreshing that the main focus of Geetanjali Gamel’s presentation earlier this year at the People Analytics & Future of Work Conference in San Francisco (see key learnings here) was on this very topic.
Geetanjali is the global leader of workforce analytics at Merck & Co., Inc. (NYSE: MRK, known as MSD outside the United States and Canada). I caught up with Geetanjali recently to ask how she has created career development paths for her team as well as discuss other related topics in the people analytics field.
Geetanjali Gamel speaking at the People Analytics & Future of Work Conference in Philadelphia in September 2017
WHY PEOPLE ANALYTICS?
1. Hi Geetanjali, please can you introduce yourself, describe your background and explain what attracted you to the people analytics space.
Like many of my colleagues in people analytics, I’ve had a non-linear path to my current role. I am a trained economist and began my career in research at the Federal Reserve Bank of St. Louis studying topics like macroeconomic forecasting, unemployment and inflation. With this foundation in social science methodology and research, I soon transitioned to business forecasting, predictive analysis and scenario-planning to drive customer growth and revenue projections in corporate planning and finance departments in the energy sector. The most intriguing part of my work was in understanding, measuring and predicting human behaviour and its impact on business outcomes such as sales and revenue. So, I was naturally attracted by the opportunity to bring scientific methodology to people data and help shape how an organisation can drive value for its investors along with enhanced experience for its employees. I began by building a predictive analytics function from scratch in HR in my previous role at Mastercard and since 2016 I have led the advanced workforce analytics, consulting and reporting organisation in Merck HR.
THE PEOPLE ANALYTICS TEAM AT MERCK & CO.
2. Please can you describe the size and structure of the workforce analytics team at Merck and how it aligns to the business
Merck’s workforce analytics team (WFA) has 15 members who support 69,000 employees in over 80 markets worldwide through a rich portfolio of people analytics products.
The team consists of three primary pillars; Consulting, Advanced Analytics, and Reporting & Data Visualisation (see Figure 1 below).
Figure 1: The Workforce Analytics team at Merck & Co (Source: Geetanjali Gamel)
Consulting - Each consultant is aligned to one of our business divisions like manufacturing, research, sales, etc. They work closely with leaders to understand and anticipate burning business questions, utilise the right methodology to find the answers; and convert the analyses into actionable insights.
Advanced Analytics - The advanced analytics team is a nimble group of data scientists and specialised professionals who focus mainly on ad hoc projects requiring advanced technical skills and/or initiatives of enterprise level significance. They are organised around business questions and may support several divisions at a time, in contrast to the end-to-end approach that the consultants take with each initiative.
Reporting & Data Visualisation – This team forms the backbone of all the amazing work we are able to do, as well as the internal customer satisfaction we drive. They work directly with internal clients from all parts of the business to ensure that the right people have the right data at the right time.
The three WFA teams work closely with each other to ensure that any synergies between business initiatives are identified and leveraged.
CREATING A DATA-DRIVEN CULTURE
3. The recent Bersin by Deloitte High-Impact People Analytics study found that the single biggest predictor in creating advanced capability is the need to create a data-driven culture. How have you achieved this at Merck particularly with regards to HR Business Partners and the wider HR function?
I agree that culture can be the strongest catalyst or impediment for people analytics. It is also ridiculously difficult to identify and alter, particularly because organisations at any given time tend to be collections of sub-cultures. But there are some patterns of behaviours, decision-making, and incentive-rewards, which distinguish data driven cultures from others. These behaviours can be purposefully incubated through a combination of upskilling, training and mind-set building.
At Merck, we believe that a leading HR function is one where analytics capability is not only for the analytics team, but the whole HR team. This does not imply that every role requires equal depth in analytics, but a new baseline of data interpretation and communication skills is critical to being effective partners to the business. To this end, we started out by democratising data within our HR community by rolling out a cloud based workforce analytics platform. This is helping us drive greater familiarity and reliance on data among our HR users. We have also developed and deployed a capability-building program with modules focused on metric selection, hypothesis testing, data visualisation, recommendation development, and more.
Another channel that we have been leveraging to accelerate a data driven culture in HR has been to engage members of our wider HR community as analytics “Champions”. These superheroes are critical to spreading the adoption of data informed insights, since they live and breathe the daily challenges of their colleagues; and can share relatable examples with their counterparts on how data can unlock value.
Finally, we also have an HR leadership team that is aligned and strong advocates in relaying the message of building data and analytics capability in HR. Needless to say, sponsorship of senior leaders is imperative to the success of a people analytics function.
Sponsorship of senior leaders is imperative to the success of a people analytics function
CREATING CAREER PATHS IN PEOPLE ANALYTICS
4. You are passionate on the need to create career paths for people analytics professionals. Why do you believe this is so important?
I firmly believe that the goal of people analytics is to drive value for the business as well as provide a better experience of work for employees. So naturally, I am equally passionate about building a better work experience for the people who make people analytics possible! I find a sad irony in the fact that the team which enables decision-making on areas like career pathing, succession planning, and talent movement for the larger workforce, is often stuck in a position of having nowhere to grow. From my discussions with many colleagues in this field, I have learned that the typical people analytics team usually tends to have a group of individual contributors (analysts, data scientists, consultants) and a director or senior director level leader. This leaves only one spot for the entire team to aspire to, at least for upward movement.
In addition, most people analytics teams sit within HR and tend to be branded as a highly-specialised “HR-lite” centre of excellence (CoE), which limits the opportunities to move laterally or upward into other HR roles in CoEs or business units. And this reality of being “boxed-in” can be very frustrating for bright, highly-employable individuals.
If you are a leader in people analytics, and if you have had to recently recruit new talent for your team, I would guess you are acutely aware of the gaping chasm between talent demand and supply in this field. In my opinion, an organisation and a leader who can offer development and career growth can be a key differentiator in attracting and retaining the best people analytics talent.
Broadening that vision, if we enabled more fluid movement of people analytics talent, it would add to the diversity of skills, approaches and perspectives to other parts of HR and the business, and would create additional value for the enterprise.
An organisation and a leader who can offer development and career growth can be a key differentiator in attracting and retaining the best people analytics talent
5. What program have you put into place at Merck regarding the career development of the people analytics team?
From the first day of my role at Merck, one of my top priorities was to understand the strengths and aspirations of my team and align their development to meet their career goals. After multiple discussions and numerous iterations on ideas, I arrived at a Capability-Capacity-Connectivity model to power a sustainable development program for our people analytics team. The underlying idea is that if we can build the right capability within the analytics team and its clients; reallocate capacity that is being consumed by suboptimal tasks; and drive connectivity between people analytics teams and other parts of the business; then we can potentially discover and create new career paths and opportunities. But please bear in mind that a key driver of success for such a model is sponsorship from your leaders and partnership with other teams. In our case, we were fortunate to have both. This has empowered us to be inventive and co-create development opportunities for our team.
6. Please can you provide more detail on what comprises each of the Capability, Capacity and Connectivity elements of this approach. What have been the key benefits and learnings from the career development program?
The “3C” approach is built around tackling barriers and creating bridges that promote career development for people analytics teams. At the outset we knew that the team was faced with a high volume of requests needing significant manual effort. (see Figure 2 below):
Figure 2: Challenges in accelerating maturity in people analytics (Source: Geetanjali Gamel)
Since the day-to-day work was time and effort intensive, there was not much room to hone more sophisticated skills or build knowledge sharing relationships with others, leaving the people analytics team stuck in a loop. So, we put careful thought and purpose into adopting the following model.
Capability
The first “C”, or capability, had to be addressed at two levels. The first was to empower our broader HR team with the right tools and training to have greater autonomy to perform analyses. We moved to an intuitive analytics platform and organised workshops, office hours, and learning sessions to improve data literacy among our internal HR clients. This type of effort is important to free-up time for the people analytics team to build their own skillset (and path to growth), while also creating a greater awareness in other parts of HR about analytics.
Figure 3: Capability - Level 1: building data, technology and analytics savvy clients (Source: Geetanjali Gamel)
The second area of capability building had a more direct impact on the team. We held a team strategy session where we identified areas that needed focus for internal functional, technical and strategic competency building. These focus areas were carefully selected to create dual impact – provide us with a skill or knowledge we could use immediately in our work; and more importantly, help us practice a new behaviour that would develop us as well-rounded professionals. For example, on the technical side, we organised an in-house R-training curriculum, created and delivered by some of our own colleagues to the rest of the team. This helped us build a technical skill we could immediately put to use to do better work, and also built coaching and confidence skills for those who led the program. Another great example was of an external guest speaker series that we launched, which brought recognition to the team for bringing new insights to the company, and also helped the team gain experience in organising an event successfully end-to-end.
Figure 4: Capability - Level 2: Upskilling the people analytics team (Source: Geetanjali Gamel)
Capacity
At first, capacity building measures may not sound like a natural fit with developing career paths. But it is impossible to focus on the next steps in one’s career if there is no time to step away from the daily barrage of activity to have a conversation; listen to a webinar; learn about a new project; or simply, chat with colleagues over lunch. As such creating capacity for the team is critical to allow them to develop their skillset to be more widely applicable, as well as to build the networks they need to find new opportunities.
As mentioned before, our journey began with democratising data and providing a range of workforce metrics and even results of our enterprise voice survey in accessible cloud platforms to our HR community. We continue to supplement our efforts to empower our internal clients, and in the process unlock capacity for our team, by forming global communities of practice for analytics. Another effort to scale our analytics delivery and save precious time has been by finding opportunities to utilise process automation on repeatable tasks.
It is impossible to focus on the next steps in one’s career if there is no time to step away from the daily barrage of activity
Connectivity
Despite efforts in building capability and reallocating capacity, there can’t be much career development if there is nowhere to go! This is when the third “C” of connectivity comes into play. In fact, it could just as easily be C for creativity, because we need a great deal of innovative thinking and risk taking to create opportunities where they don’t always exist.
We started with small yet effective steps rather than trying to construct huge, formal programs. Connecting the people analytics team with other HR, data science, technology, and business professionals builds an awareness and appreciation for different types of work on both sides. We leveraged opportunities to co-create part-time assignments with other teams, participate in cross functional events, invite guest speakers to team meetings, and collaborate on projects to expose the team to other areas of analytical work.
Connecting the people analytics team with other HR, data science, technology, and business professionals builds an awareness and appreciation for different types of work on both sides
To create development assignments for the people analytics team we were creative and went with “quasi-experiments”. The first was an opportunity for a team member to take on the role of an HR business partner on a part-time basis for a few, smaller client groups. This gave the individual an opportunity to apply their analytical skillset to the role and get much greater exposure than before to business clients and business issues. Such an experiment has a multiplier effect. Where typically a business partner track is not easily available to a people analytics professional, creating such an opportunity internally can open up a new career path. Moreover, even if the individual does not end up pursuing this new career direction at the end of the experiment, it is still a valuable learning experience for them to be in the shoes of their internal client, i.e., the HR business partner. Finally, it may help to lay the foundation for what I like to call the HRBP 3.0 model.
Where the original HRBP role had a heavy component of operational (and even transactional) work, the HRBP 2.0 model that many companies follow today aims at strategic business partners who enable key business decisions. The HRBP 3.0 model takes it a step further by envisioning an analytical HR business partner, who relies on both data driven insight and business acumen to support their client.
Another “experiment” in creating new career opportunities was a mini-assignment we created for one of our people analytics team members to lead a large, remote team in the service delivery space. This was a completely different line of work from people analytics, and was heavily focused on operational and organisational skills like identifying and escalating issues on short deadlines, supplier relationship management, building relationships with a variety of HR and non HR stakeholders, and leading a service centre team to drive customer satisfaction. Clearly, this would not be a typical career path for a people analytics professional, but that is exactly why we need to be bold and creative with such experiments. This assignment not only exposed the individual to a different type and pace of work, but also gave them an opportunity to bring their analytical skills to the table to significantly elevate the usage and interpretation of transactional data.
While many mature organisations have good-sized people analytics teams, there are still many where the teams are pretty lean. This model may work well for most purposes, but it usually limits the opportunities for team-members to have people management experience. This is not always necessary for upward mobility, but it many cases it is difficult to move upward without some kind of experience of leading a team. Keeping this in mind, we built more depth in our people analytics team, creating enterprise advanced people analytics and data visualisation and reporting sub-teams within the larger group, which are led by two of our team members. Taking a chance on subject matter experts and giving them the opportunity to lead and delegate not only helps to open up doors for them, it also gives them a chance to coach others on their team to be future experts and leaders.
Lastly, we also created a new learning analytics role on our people analytics team which is a step toward building greater synergies between people analytics and learning practices, but also our small contribution in creating a new capability (and career path!) that is still evolving in many organisations.
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
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