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:为什么统计不是浪费时间
文/Erik van Vulpen
许多人力资源从业者都有人力资源管理研究或工业和组织心理学的背景,而这些研究严重依赖于向学生讲授统计数据。作为一名学生,通常很难想象为什么统计数据如此重要。特别是如果你不想成为一名学术研究人员,统计数据会让你感到浪费时间。我们大多数人都希望与人合作,只是“做”人力资源,与统计数据的相关性便开始缺失。
然而,正如大多数人员分析人士所知,人力资源中统计数据的应用是我们称之为人力资源分析的基础。了解统计数据,了解如何以不同方式查看数据,以及在需要时分析数据,有助于我们做出更好的决策。
事实上,这是我经常从统计学的学生那里听到的。在制定更好和基于证据的决策方面,没有什么比对基于统计数据的结论和基本理解更有帮助了。
人员分析统计
聚合多个系统的数据并创建HR指标的仪表板,如使用Excel,Power BI或R来制作可视化数据,是实现人员分析的重要步骤。
但是,如果事实证明您拥有的数据不具代表性,那么您的结论和决定会发生什么?如果您需要轻松检查数据的质量和准确性,并轻松删除偏差结果的错误异常值,该怎么办?能够系统地思考数据对于人员分析至关重要,并且知道如何检查相关性以及因果关系成为人员分析的核心。
统计上显着的异常值
统计数据是人员分析的重要组成部分,适用于各种分析。例如:
如果您的大多数人表现“满意”,您将如何区分好或坏的表现?对数据进行区分,以得出结论并充分理解,是人员分析不可或缺的。
或者,当您启动分析项目时,您是否发现数据有回归到正常平均值的趋势?分析项目通常是对组织中问题的响应,但这个问题可能是由数据中的偶发性异常值引起的。这意味着下次我们进行测量时,这个异常值将降低到正常水平,这被称为回归均值。
另一个例子是问卷的答复率。您上次参与调查是否在组织中的不同群体之间获得了相同的回复率?或者这是你没有检查的东西?要了解某些群体在您的调查中是否过多或不足,您可以使用一些相对简单的统计技术来检查这一点。
对于我们的读者,Daniel Kahneman的书《思考的慢与快》强调了对数据进行深思熟虑和系统思考的重要性。通常我们能够在看到信息后立即快速处理信息,但这会受到我们的偏见和其他情绪的影响。只有采取更加审慎和合乎逻辑的方法,我们才能开始做出更客观的决定。统计学的学生在这方面表现得更好,因为他们知道人们容易受到的许多谬误引导。
以上为AI翻译,内容仅供参考。
原文链接:People Analytics: Why Statistics Is Not a Waste of Time
People Analytics
2018年12月19日
People Analytics
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
员工敬业度的未来:个性化福利和预测分析的好处作者:Prarthana Ghosh
根据一项新的哈里斯民意调查,样本规模为2,257人力资源专业人员和CareerBuilder的招聘经理,错误的候选人选择使普通雇主在2017年花费了14,900美元。此外,10%的参与者表示缺乏足够的工具造成严重影响错误的候选人选择。这些数字说明了无处不在的招聘障碍,并指向甚至更大的保留障碍。难怪今天的每个组织都在升级员工敬业度!公司最终制定(或正在制定)将员工视为数字消费者的转变,他们需要能够以与家中相同的舒适度连接和插入工作。
例如,Deloitte的ConnectMe不仅利用Salesforce的一流CRM云解决方案,还通过富有洞察力的数据挖掘和基于需求的解决方案提供真正数字化工作场所的创建和维护,并改善员工体验,从而更好地参与。
个人风格
随着工作的概念不断发展,今天更多的员工似乎想要在家工作。无论是他们的工作地点,他们使用的工具,还是他们遵循的计划,员工都会寻求一定程度的个性化,使他们更好地与工作联系起来。
随着我们最近的数字飞跃,个性化现在可以达到一个全新的水平,因此是工作场所,行业和地理区域的普遍趋势。今天的体验式员工可能希望“会面”并与世界各地的同事进行面对面的交谈,并与新的AR(增强现实)工作场所不再是虚构的工作场所。员工现在可以在AI助手的帮助下将日程安排从日常任务中解放出来。他们可以通过充分利用直观的软件并使用分析来预测未来的步骤来更好地规划他们的工作。具有讽刺意味的是,非人为干预可以增加人类个人的触觉,这是今天不可避免的必要条件。
以下是不同参与程度的图示。个性化必须扩展到所有这些级别:
个性化创新的含义
因此,今天工作中的个性化不仅仅是允许员工引入他们自己的系统或咖啡杯。它还可以承认员工需要从“无干扰”的位置远程工作。目前,个性化的参与努力正处于从事后的困境转变为常态的过程中。
随着工作文化演变为相互联系,有凝聚力的生态系统,BYOS(自带软件)等新趋势越来越受欢迎。此外,这指向了一个更有趣的转变 - 允许员工自由个性化工作流程的组织 - 选择他们认为最有利于他们的企业应用和软件。
随着软件产品本身逐渐转向智能,个性化,特定和量身定制的体验,每个人都有权在工作中获得自己的个性化品牌。这对软件公司也有影响,因为他们现在不仅要保持领先地位,而且要确保他们的产品能够与客户收听的其他应用和软件相得益彰。对于试图适应这些新趋势的组织而言,这是一个关键的学习点,以便信息和通信在不同平台之间无缝流动。
工作中个性化面临的主要问题是安全性和合规性。监督的基本格式要求在保护公司信息,遵守不公开和其他此类协议方面进行变更。每个组织都要权衡允许BYOS环境的好处是否会抵消风险。
参与重新定义
今天有相当大比例的员工会选择生活津贴而不是更大的薪酬。劳动力行为方面的这些重大变化有助于引领我们今天在行业中看到的思想复兴。现代员工希望他/她各自的组织为他们的生活增加更多的财务价值。承认,个人和职业发展,幸福和健康,工作与生活的平衡是工作的其他方面,在参与和保留方面越来越重要。
根据Forrester Research的研究,员工体验为2017年的工作未来提供动力,除了因工作努力而得到认可外,员工还寻求技术驱动的体验式,身临其境的流程以及个性化福利等切实变革。美国海斯在2017年进行的调查显示,71%的参与者表示,他们希望接受较低的工资,以便在过去的经验,现有需求和未来计划方面实现更大的角色协调。此外,可定制的福利似乎对员工忠诚度产生直接和积极的影响,如MetLife,2017年第15期美国员工福利趋势研究报告所示。
预测分析:行为的水晶球?
LifeWorks的分析,关注:如何开发和支持今天的员工,2017年,列举了组织如果未能调整他们的战略以提供个性化和真正吸引人的员工体验,他们将面临失去优质员工风险的风险。这就是使用预测分析的礼物发挥作用的地方。员工是人,分析人类行为往往造成困难,因为它的活力和需要考虑到个体差异。这些数据点不仅有助于跟踪工资单,福利登记或增长预测,还可以预测员工的成长和寿命。
“如果你有分析能够帮助你根据他们过去的表现,他们的技能水平,他们的个性和他们的文化契合来预测候选人的成功,那么它可以更好地描绘出他们如何适应你的公司,”Michael Fauscette说。 ,G2 Crowd的首席研究官。“如果分析能够预测候选人的成功,那么这对招聘流程来说可能是一个巨大的好处,如果合适,那么留住员工是一个巨大的好处。”
与工作中的任何技术组合一样,行为分析也伴随着一系列法律和道德问题,因为监控员工行为有其复杂性并需要得到承认。因此,它需要一定程度的员工教育,让所有员工了解他们的数据如何被使用以及用于何种目的。这也有助于更好地分析法律影响。此外,在组织实现转变之前,各级领导层和人力资源部门必须允许个性化和预测分析的渗透。
虽然员工参与空间会改变,变异和发展,但目睹未来的变化将会很有趣。公司是否会继续使用反应方法来回顾它,或者我们是否准备好进行直观,预测和主动的行动?
以上为AI翻译,仅供参考学习~
原文如下:
The Future of Employee Engagement: Perks of Personalization and Predictive analytics
According to a new Harris Poll with a sample size of 2,257 HR professionals and recruitment managers for CareerBuilder, the wrong choice of candidate cost the average employer a steep $14,900 in 2017. Moreover, 10% of the participants stated the lack of adequate tools contributed severely to wrong candidate choices. These numbers speak of a ubiquitous recruitment hurdle and point towards and even greater retention obstacle. No wonder every organization today is upgrading their employee engagement methods! Companies have finally made (or are in the process of making) the shift to regarding their employees as digital consumers who need to be able to connect and plug into work with the same comfort level they have at home.
ConnectMe at Deloitte for example, not only utilizes the best in class CRM cloud solution by Salesforce but also provides for the creation and maintenance of a truly digital workplace through insightful data mining and need-based solutions and improve employee experience and thus look at better engagement.
A personal touch
With the concept of work having evolved, more employees today seem to want to feel at home at work. Whether it is the location they work out of, the tools they use or even the schedule they follow, employees look for a certain level of personalization that makes them relate better to work.
With our recent digital leaps, personalization is now possible at a whole new level and is thus a pervasive trend across workplace, industries and geographies. The experiential employee of today might want to “meet” and have a face-to-face conversation with colleagues across the world and with the new AR (Augmented Reality) enabled workplaces that is no longer fiction. Employees can now free their schedules off routine tasks with the help of AI assistants. They could plan their work better by making the most of software that is intuitive and use analytics to predict the steps ahead. It’s ironic that non-human interventions could increase the essentially human personal touch that is an unavoidable requisite today.
The following is a pictorial depiction of the different levels of engagement. Personalization must be extended across all these levels:
Implications of personalized innovation
Personalization at work today is thus more than just allowing employees to bring in their own systems or coffee mugs. It could also be acknowledging the need of an employee to work remotely from a “distraction-free” location. At the moment, personalized engagement endeavours are in the middle of moving from being an afterthought to being the norm.
With work cultures evolving into connected, cohesive ecosystems, new trends like BYOS (Bring Your Own Software) is gaining popularity. Moreover, this points towards a more intriguing shift – organizations allowing employees the freedom to personalize work processes – to choose enterprise apps and software that they feel benefit them the most.
With software offerings themselves moving toward intelligent, personalized, specific and tailored experiences themselves, everyone is entitled to their own slice of personalized brand of reality at work. This has implications for software companies too since they now not only have to stay ahead of the curve but at the same time, ensure that their offerings play nice with the other apps and software that their customers tune into. This is a key learning point for organizations who are trying to adapt to these new trends so that information and communication flows seamlessly across platforms.
Primary issues that confront personalization at work are that of security and compliance. The basic format of monitoring then calls for a change with regard to protection of company information, compliance with non-disclosure and other such agreements. It is for each organization to weigh out whether the benefits of allowing a BYOS environment negate the risks.
Engagement redefined
There is a sizable percentage of employees today who would choose lifestyle perks over a bigger pay package. Such crucial changes in terms of workforce behavior have been instrumental in leading the thought renaissance that we see around the industry today. The modern employee wants his/her respective organizations to add more than financial value to their lives. Recognition, personal and career development, happiness and wellness, work-life balance are among the other aspects of work that are of mounting importance when it comes to engagement and retention.
According to Forrester Research, Employee Experience Powers the Future of Work, 2017, besides being recognized for their effort at work, employees seek technology-driven experiential, immersive processes and tangible changes like personalizing benefits. The Hays US What People Want Survey conducted in 2017 revealed that 71% of the participants indicated that they would be keen to accept lower pay for a job that allowed greater role-alignment in terms of what their past experience, present needs and future plans. Furthermore, customizable benefits seem to have a direct and positive influence on employee loyalty as seen in the MetLife, 15th Annual U.S. Employee Benefit Trends Study, 2017.
Predictive analytics: the crystal ball of behaviour?
An analysis by LifeWorks, Taking Care: How to Develop and Support Today’s Employees, 2017, enumerates how organizations run the risk of losing quality workers if they fail to tweak their strategies in order to provide employee experiences that are personalized and truly engaging. That is where using the gifts of predictive analytics come into play. Employees are human and analyzing human behavior often poses difficulties due to its dynamism and the need to take into consideration individual differences. These data points not only help in tracking payroll, benefits enrollment or growth projection but also allow for the prediction of growth and longevity of an employee.
“If you had analytics that could help you predict the success of a candidate based on their past performances, their skill levels, their personality and their cultural fit it could paint a better picture of how they will fit into your company,” says Michael Fauscette, chief research officer for G2 Crowd. “If the analytics can predict the success of a candidate, then it could be a huge benefit to the hiring process, and if that fits, then it is a huge benefit to retaining an employee.”
As with any technological incorporation at work, behavioural analytics too comes with its set of legal and ethical concerns since monitoring employee behaviour has its complexities and that needs to be acknowledged. It thus needs a certain level of employee education where all employees are made aware of how their data is being used and for what purposes. This would also help in analyzing legal repercussions better. Moreover, before the organization is enabled in making the shift, all levels of leadership and of the HR function must allow a permeation of personalization and predictive analytics.
While the employee engagement space modifies, mutates and evolves, it would be interesting to witness the changes yet to come. Would companies continue to work towards it in retrospect with reactive methods or are we ready for intuitive, predictive and proactive moves?