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    贵公司是否准备好进行人力资源分析 Is Your Company Ready for HR Analytics? 尽管许多公司一直在大数据和分析方面进行大量投资,但将分析应用于人力资源的成功案例却很少。但这可能即将改变。 作者:Bart Baesens是比利时鲁汶的KU Leuven教授,也是英国南安普顿南安普顿大学管理学院的讲师 大数据和分析在当今的商业环境中无处不在。更重要的是,诸如物联网,不断扩展的在线社交图以及开放的公共数据的出现等新技术只会增加对深层分析知识和技能的需求。许多公司已经投入大数据和分析,以更好地了解客户行为。事实上,由于引入了各种监管指南,一些最成熟的分析应用程序可以在以客户为中心的保险,风险管理和财务欺诈检测领域找到。 但是,如何利用大数据和分析来深入了解贵公司的另一组关键利益相关者:您的员工?虽然我们看到许多公司加大了对人力资源分析的投入,但我们还没有看到该领域的许多成功案例。由于人力资源分析是业务分析应用程序中的“新手”,我们相信其从业者可以从将分析应用于以客户为中心的领域中获得的经验教训中大大受益 - 从而避免了许多新手错误和昂贵的初学者陷阱。 基于我们的研究和我们在以客户为中心的分析方面的咨询经验,我们提供了四个关于如何成功利用人力资源分析来支持您的战略性劳动力决策的课程。更具体地说,我们将客户分析中的一些最新研究和行业见解与人力资源分析并列,并强调四个重要的溢出效应。 第1课:建模,衡量和管理员工的网络动态。在我们自己的研究中,我们发现客户之间的关系(例如社会关系,与同一商家进行的信用卡交易,或公司之间的董事会成员关系)在解释和预测集体行为(如客户流失,客户响应)方面非常有意义。营销外展或欺诈。我们相信,这些原则可以很容易地用于在人力资源分析中收获一些悬而未决的成果。特别是,可以构建一个网络 - 员工作为节点,并根据诸如(匿名)电子邮件交换,联合项目,主机托管和人才相似性等因素与他们之间的链接进行构建,并且可能对最近这样的连接的加权进行加权。然后可以利用该网络来了解新员工融入您的员工网络的顺利程度; 出于同样的原因,在解雇或解雇员工时,了解员工的社会影响和影响非常重要,以防止病毒影响或人才流失发生在您的网络或公司中。在制定解雇决策时,应仔细联系在组织网络中充当社交影响者或社区连接器的员工,以避免在功能上断开网络的基本部分。 第2课:大数据和分析并不神奇。与任何新技术一样,从一开始就设定适当的期望非常重要。虽然它们可以成为有价值的工具,但分析技术并不是解决公司所有关键任务和困难人力资源决策的灵丹妙药。毕竟,几乎只要分析人力资源模型投入生产,它就会变得过时,因为它的生态系统(包括但不限于公司战略,员工组合和宏观经济环境)经常会发生变化。因此,人力资源最终用户使用他或她的商业智慧,经验以及对问题和组织的了解来批判性地解释,反映,调整和操纵分析模型的结果,这一点至关重要。例如,如果您的分析模型告诉您,您的招聘和解雇政策完全没有 - 或甚至是歧视性的,该怎么办?你使用错误的选择标准或正在寻找不可能的?最近客户流失可以追溯到特定员工的离职?任何意外但有效的分析结果都应该以认真和深思熟虑的方式进行。显然,这需要人力资源经理具有既知情又开放的心态。 第3课:分析人力资源模型应该做的不仅仅是提供统计绩效 - 他们应该提供商业见解。在任何业务环境中部署分析模型时,典型的新手错误是对统计性能(如拟合,相关,R平方等)和过于复杂的分析模型的盲目痴迷。统计绩效很重要,但分析性人力资源模型应该做得更多。另外两个重要的绩效标准是模型可解释性和合规性。 可解释性意味着任何基于分析的人力资源决策都应该得到适当的激励,并且可以简单地向所有涉及的利益相关者解释。这种对简单性的追求阻碍了使用过于复杂的分析模型,这些模型更多地关注统计性能而不是正确的业务洞察力。 另一个关键性能标准涉及模型合规性 保护法规,隐私和道德责任对于成功部署HR分析至关重要。这在人力资源应用中尤为重要。应始终谨慎解释分析模型,在选择构建分析HR模型的数据时,应尊重性别平等和多样性。 第4课:回溯测试分析人员模型的影响。在客户分析中,模型的平均寿命为两到三年,我们没有理由相信这在人力资源分析中会有所不同。然而,考虑到人力资源决策对组织和个人的影响,重要的是通过将预测与现实进行对比来不断地对人力资源中的分析模型进行反向测试,以便可以立即注意到任何性能下降并采取行动。例如,从招聘的角度来看,应该不断评估招聘前的有效性(哪些招聘渠道给我们的候选人提供正确的资料?)和招聘后的有效性(招聘渠道给我们最好的候选人?)。 我们相信现在是时候增加您对人力资源分析的投资了。一旦您的人力资源分析工作成熟,我们就会期待组织的下一个变革步骤。我们认为,当组织将人力资源分析的结果与客户分析的结果汇总在一起时,我们就会发生这种情况。然后,公司可以更全面地了解他们的两个关键人力资产组合之间的关系:员工和客户。 关于作者 Bart Baesens是比利时鲁汶的KU Leuven教授,也是英国南安普顿南安普顿大学管理学院的讲师。他还是“ 大数据世界中的分析:数据科学及其应用基本指南”一书的作者(John Wiley&Sons,2014)。Sophie De Winne是KU Leuven的副教授。Luc Sels是KU Leuven的经济学和商业学院教授和院长。 Is Your Company Ready for HR Analytics? Although many companies have been investing heavily in big data and analytics, there have been few success stories in applying analytics to human resources. But that may be about to change.   Big data and analytics are omnipresent in today’s business environment. What’s more, new technologies such as the internet of things, the ever-expanding online social graph, and the emergence of open, public data only increase the need for deep analytical knowledge and skills. Many companies have already invested in big data and analytics to gain a better understanding of customer behavior. In fact, due to the introduction of various regulatory guidelines, some of the most mature analytical applications can be found in customer-focused areas in insurance, risk management, and financial fraud detection. But what about leveraging big data and analytics to gain insights into another group of your company’s key stakeholders: your employees? Although we see many companies ramping up investments in HR analytics, we haven’t seen many success stories in that area yet. Because HR analytics is “the new kid on the block” in business analytics applications, we believe its practitioners can substantially benefit from lessons learned in applying analytics to customer-focused areas — and thus avoid many rookie mistakes and expensive beginner traps. Based upon our research and our consulting experience with customer-focused analytics, we offer four lessons about how to successfully leverage HR analytics to support your strategic workforce decisions. More specifically, we will juxtapose some of our recent research and industry insights from customer analytics against HR analytics and highlight four important spillovers. Lesson 1: Model, measure, and manage your employee network dynamics. In our own research, we have found that ties between customers (such as social ties, credit card transactions made with the same merchants, or board membership ties between companies) are very meaningful in explaining and predicting collective behavior such as customer churn, customer response to marketing outreach, or fraud. It is our belief that these principles can be easily used to harvest some low-hanging fruit in HR analytics. In particular, a network can be constructed — with employees as the nodes and with the links between them based upon factors such as (anonymized) email exchanges, joint projects, colocation, and talent similarity, and possibly weighted for how recent such connections were. This network can then be leveraged to understand how smoothly new hires will blend into your workforce network; it also can be used to quantify the optimal mix, from a performance perspective, between behaviors that bring cohesiveness to the employee network and those that bring diversity.   By the same token, when laying off or firing employees, it is important to understand the social influence and impact of an employee in order to prevent viral effects or talent drain from happening to your network or company. Employees who serve as social influencers or community connectors within your organization’s network should be carefully approached when making firing decisions to avoid functionally disconnecting essential parts of your network. Lesson 2: Big data and analytics are not magic. As with any new technology, it is important to set appropriate expectations from the outset. While they can be valuable tools, analytics techniques are not a panacea for all of your company’s mission-critical and difficult HR decisions. After all, almost as soon as an analytical HR model is put into production, it becomes outdated, since its ecosystem (including but not limited to company strategy, the employee portfolio, and the macroeconomic environment) is constantly subject to change. Hence it is of key importance that the HR end user critically interprets, reflects, adjusts, and steers the outcomes of the analytical models using his or her business acumen, experience, and knowledge of the problem and organization. For example, what if your analytical model tells you that your hiring and firing policy is not at all sound — or is even discriminatory? That you are using the wrong selection criteria or are searching for the impossible? That the recent loss of customers can be traced back to the departure of a specific employee? Any unexpected yet valid analytical findings should be approached in a careful and thoughtful way. Obviously, this requires HR managers with a mindset that is both informed and open. Lesson 3: Analytical HR models should do more than provide statistical performance — they should provide business insights. A typical rookie mistake when deploying analytical models in any business context is a blind obsession with statistical performance (such as fit, correlation, R-squared, etc.) and overly complex analytical models. Statistical performance is important, but analytical HR models should do more. Two other important performance criteria are model interpretability and compliance. Interpretability means that any HR decision based upon analytics should be properly motivated and can be simply explained to all stakeholders involved. This quest for simplicity discourages the use of overly complex analytical models that focus more on statistical performance than on proper business insight. Another key performance criterion concerns model compliance. Safeguarding regulations, privacy, and ethical responsibilities is crucial to successfully deploying HR analytics. This is especially important in HR applications. Analytical models should always be interpreted with caution, and gender equality and diversity should be respected when selecting the data to build your analytical HR models. Lesson 4: Backtest the impact of your analytical workforce models. In customer analytics, the average lifespan of a model is two to three years, and we have no reason to believe that this will be different in HR analytics. However, given the impact of HR decisions on the organization and on individuals, it is important that analytical models in HR are constantly backtested by contrasting the predictions against reality, so that any degradation in performance can be immediately noticed and acted upon. For example, from a hiring perspective, both the pre-hire effectiveness (which recruitment channels give us the candidates with the right profile?) and post-hire effectiveness (which recruitment channels gave us the best candidates?) should be constantly evaluated. We believe the time is right to boost your investments in HR analytics. And once your HR analytics efforts have matured, we look forward to the next transformative step for organizations. That, we think, will take place when organizations can bring together findings from HR analytics with those from customer analytics. Then companies can more fully understand the relationships between their two key sets of human assets: employees and customers. ABOUT THE AUTHORS Bart Baesens is a professor at KU Leuven in Leuven, Belgium, and a lecturer at the University of Southampton School of Management in Southampton, U.K.; he is also the author of the book Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (John Wiley & Sons, 2014). Sophie De Winne is an associate professor at KU Leuven. Luc Sels is a professor and dean of the faculty of economics and business at KU Leuven.
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    2018年07月16日
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    创新:背调公司Checkr创建动态背调监控工具以提升Uber乘坐的安全性 编者注:值得学习和参考,动态的背景调查很重要啊!国内哪家可以跟滴滴等合作起来! 目前背调都是截止调查的当天。而入职或者开始工作后的情况就很难掌握了! 现代和合规背景调查的领先提供商Checkr今天宣布了一项新技术,该技术可持续更新可能影响共乘驾驶员驾驶资格的犯罪记录。Checker Continuous Check由Uber设计,动态识别可能不合格的记录,以帮助确保驾驶员继续满足优步的安全标准。 Checkr首席执行官Daniel Yanisse表示: “ 凭借当今的按需劳动力,我们需要超越静态背景报告,进行动态筛选。通过持续检查,Checkr为共乘产业创造了新的安全标准将提供关于某人背景变化的重要见解,这可能会影响他们的工作资格。“ 优步是第一家采用该技术的公司。使用涵盖大多数新刑事犯罪的数据来源,当司机参与犯罪活动时,持续检查会向优步提供通知。然后,优步可以调查任何可能不合格的信息,例如DUI的新费用和未决费用,以确定该驱动程序是否仍有资格与Uber一起驾驶。这项新技术使优步能够在每年重新进行背景调查之间持续执行其安全标准。 “ 安全对优步至关重要,我们希望确保驾驶员持续不断地达到我们的标准,”优步安全与保险副总裁Gus Fuldner说。“ 这种新的连续检查技术将加强我们的筛选过程并提高安全性。” 最初设计用于满足共乘行业的严格要求,2018年秋季将为所有Checkr客户提供持续检查。 关于Checkr Checkr的使命是通过提高对过去的理解来建立更公平的未来。我们的平台使数以千计的客户每年能够以gig经济的速度轻松雇用数百万人。使用Checkr先进的背景调查技术,各种规模的公司都能更好地了解不断变化的员工队伍的动态,为他们的招聘带来透明度和公平性,最终为员工创造更美好的未来。 Checkr Creates Dynamic Monitoring Tool to Elevate Safety in Ridesharing Checkr, the leading provider of modern and compliant background checks, today announced new technology that provides continuous updates about criminal records that may affect ridesharing drivers’ eligibility to drive. Checkr Continuous Check, which was designed with Uber, dynamically identifies potentially disqualifying records to help ensure drivers continue to meet Uber’s safety standards. “With today's on-demand workforce, there's a need to move beyond static background reports to dynamic screenings," said Daniel Yanisse, CEO of Checkr. "Through Continuous Check, Checkr is creating a new standard of safety for the ridesharing industry and beyond that will provide critical insight into changes in someone's background that may affect their eligibility to work." Uber is the first company to adopt the technology. Using data sources that cover most new criminal offenses, Continuous Check provides notifications to Uber when a driver is involved in criminal activity. Uber can then investigate any potentially disqualifying information, such as a new and pending charge for a DUI, to determine whether the driver is still eligible to drive with Uber. This new technology allows Uber to continuously enforce its safety standards between annual reruns of background checks. “Safety is essential to Uber and we want to ensure drivers continue to meet our standards on an ongoing basis,” said Gus Fuldner, Vice President of Safety and Insurance at Uber. “This new continuous checking technology will strengthen our screening process and improve safety.” Designed initially to meet the stringent requirements of the ridesharing industry, Continuous Check will be available to all Checkr customers in Fall 2018. About Checkr Checkr’s mission is to build a fairer future by improving understanding of the past. Our platform makes it easy for thousands of customers to hire millions of people every year at the speed of the gig economy. Using Checkr’s advanced background check technology, companies of all sizes can better understand the dynamics of the changing workforce, bring transparency and fairness to their hiring, and ultimately build a better future for workers. For more information please visit: www.checkr.com.
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    2018年07月15日
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    2018候选人体验大奖(中国地区)评选正式启动! 候选人体验大奖 Candidate Experience Awards(CandE Awards) 候选人体验的定义: 用来描述候选人与公司之间关于招聘营销推广和雇佣目的的所有互动的过程感受。 越来越多招聘人员和公司认识到候选人体验的重要性。大家都知道你无法获得第二次机会去赢得第一印象。候选人体验就是这样,帮助企业快速获取更好的人才而不要犯低级的错误。 候选人与公司互动的阶段基本可以分为几个阶段:信息获取、申请流程、沟通交流、面试过程、后续跟进(Offer发放及入职体验) 提升候选人体验的方法有很多,核心的一点是公司全体人员的共识和认知,毕竟招聘录用候选人是一个多部门多人员协同推进的事情,必须建立在公司整体的认知和共识之上才可能建立起广泛的良好体验。 候选人体验大奖流程: 7月1日-7月20日 提交案例,评委会互动沟通阶段 7月27日 体验大奖颁奖典礼 奖项分为: · 候选人体验金奖企业(案例) · 候选人体验卓越团队(案例) · 候选人体验卓越产品(案例) 案例提交指南: 完整的体系或者深度的细节。 丰富的数据支持和结果验证 图片、文字、视频等均可 必须原创且实践 为什么要参加? 1、口碑成就价值。候选人体验大赛作为全中国最权威、最顶级、最具影响力的人力资源赛事之一,将为企业提供展示优秀案例的华丽舞台; 2、豪华阵容的评委团。由各知名企业的HR高管共同组成的评委团,经过对候选名单进行全面、严格、高标准的讨论筛选,共同打造出极具含金量的候选人体验大奖 3、入选的案例将加入HRTechChina 的案例库,并编辑成册,通过几大平台广泛宣传和阅读,持续提升企业曝光率,加强其在细分领域内的卓越表现的影响力; 4、同行业间交流学习的绝佳时机。在八月校招前,您可以观摩借鉴同行的独特经验,第一时间掌握2018中国地区最先进的候选人体验提升的经验,短时间内了解更多有创意、有实效的想法,更好地为接下来的校招做准备; 5、线上线下参与主办方的分享活动,获得全方位的曝光机会; 6、最大化利用评选结果,进行对公司的对外市场宣传,为您吸引更多需要的优秀人才和提升企业知名度;   提交方式: cande@hrsalonchina.com  用于附件提交 联系我们:021-51083646   在线提交,请点击这里: http://hrday.com/survey/survey.php?id=98C728F8-5A22-40E3-B911-83A410DAE743 主办单位: HRSalon  &  HRTechChina 协办单位: 招聘兄弟会、人力资源杂志、人力资源开发与管理杂志、HR研究网    
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    2018年07月02日
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    人力资本分析高端峰会--People Analytics Forum 上海 8月3日 Digital HR系列论坛: 人力资本分析高端峰会--People Analytics Forum 时间:8月3日 13:00-17:00 地点:上海来福士广场办公楼 37楼 3702 会议室(黄浦区西藏中路268号,2楼坐电梯) 这是面向未来的人力资源趋势,聪明的你不容错过! 让我们用数据来驱动人力资源,驱动商业变革!尤其适合中高级人力资源管理者! 特别邀请国内长期关注数据分析,人才分析的大咖为你答疑解惑! 分享话题: 如何通过人力资源数据讲故事?以终为始,有效利用人力资源数据推动业务价值实现    数据重塑影响力     -由外而内的价值创造思维; -数据驱动的效能计量系统; -先于业务的战略支持维度 组织人效管理的任督二脉 - 构建人效管理仪表盘的核心逻辑 - 如何通过数据分析诊断组织健康问题 - 组织效能优化路径和案例探讨 费用: 7月15日前  398元/人  三人同行 980元 具体可参考海报   (function (i, s, o, g, r, a, m) { i['BagEventIFrameResize'] = r; i[r] = i[r] || function () { (i[r].q = i[r].q || []).push(arguments) }; a = s.createElement(o), m = s.getElementsByTagName(o)[0]; a.async = 1; a.src = g; m.parentNode.insertBefore(a, m) })(window, document, 'script', '//www.bagevent.com/resources/js/iframeResizer/iframeResizer.min.js', 'bfr'); bfr('iFrameResize', {checkOrigin: false, heightCalculationMethod: 'taggedElement'}, "#promote_ticket_iframe"); 或点击报名:https://www.bagevent.com/widget/ticket/1624573   联系我们: 小沙  186 1681 8246 微信:hrsalon 邮件:xiaosha@hrsalonchina.com
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    2018年07月02日
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    猎聘香港上市报道--发行价33元港币,市值163.53亿港币,上市净融资额为27.66亿港元 猎聘上市数据一览: 发行价定为33港元(不含1%的经纪佣金 0.0027%的证监会交易费用和0.005%的联交所交易费) 上市融资净额为27.66亿港币。 共收到2998份申购意向表格。 —————— · 9:47 32.8港币  市值163 亿港币 换手率3.82% 6亿港币 · 9:33  31.7港币,市值157亿港币,换手率2.17& 3.4亿港币 ·9.30-开盘价格31.6元,最低29.95港币  市值151亿港币,换手率1.66% 金额2.6亿港币 · 9.25分--目前猎聘盘前 31.6港币,市值 156.6亿港币! 上市现场图集:https://gallery.vphotos.cn/vphotosgallery/index.html?vphotowechatid=6772E305D093148ADCC2AD78E845DFB7   相关信息可以查看:http://www.hrtechchina.com/search/?keywords=%E7%8C%8E%E8%81%98 背景信息: 猎聘6月29日在港股上市(06100), 拟集资25-31亿港元 ,发行价28.5-35.5之间,市值18-21亿美金。    
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    2018年06月28日
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    10 Trends in Workforce Analytics (英文) Workforce analytics is developing and maturing. These are the 10 major trends for the near future. 1. From one time to real-time Many workforce analytics efforts start as a consultancy project. A question is formulated (“How do our employees experience their journey?”), many people are interviewed, data is gathered, and with the help of the external consultants a nice report is written and many follow up projects to redesign the employee journey are defined. A one-time effort is nice, but it might be more beneficial to develop ways to gather more regularly and maybe even real-time feedback from candidates, employees and other relevant groups. The survey practice is changing. We see organizations using several approaches: The classic annual or bi-annual employee survey, for a deep dive. Weekly, monthly or quarterly pulse surveys to gather more frequent feedback. A few questions, often varying the questions per cycle. Some more advanced pulse survey solutions are adaptive: they ask more questions to people when they sense there are issues (“How was your week?”. If the answer is “Very Good”, the survey is finished, if you answer, “Not so good”, there are some follow-up questions). Pulse surveys can also be easily connected to the important “moments that matter” for the employee experience. Continuous real-time mood measurement. Innovative solutions in this area are still scarce, especially if you want to measure in a passive non-obtrusive way. Keencorp is an example, they analyze aggregated e-mails and can report on the mood (and risks) in different parts of an organization. In my article Employee mood measurement trends,  you can find an extensive overview of mood measurement providers. 2. From people analytics to workforce analytics Currently, the general opinion seems to be that people analytics is a better label than HR analytics. Increasingly the workforce is consisting of more than just people. Robots and chatbots are entering the workforce. The first legal discussions have started: who is responsible for the acts of the robots? If we’re also analyzing robots, we’re moving from people analytics towards workforce analytics. Robot wellbeing and robot productivity is a nice domain for HR to claim. 3. More transparency This overview of workforce analytics trends cannot be complete without a reference to GDPR. GDPR is fueling a lot of positive developments, one of them being a lot more transparency. About what kind of data is collected, how it is used, and how algorithms are used to make decisions about people. The issue of data ownership is related. It is expected that employees will no longer accept that they cannot own their own personal data. Employees need to have the possibility to show their data to their potential next employer as evidence for their productivity and engagement. 4. More focus on productivity In the last years, there has not been a lot of focus on productivity. We see a slow change at the horizon. Traditionally, capacity problems have been solved by recruiting new people. This has led to several problems. I have seen this several times in fast growing scale-ups. As the growth is limited by the ability the find new people, the selection criteria are (often unconsciously) lowered, as many people are needed fast. These new people are not as productive as the existing crew. Because you have more people, you need more managers. Lower quality people and more managers lowers productivity. Another approach is, to focus more on increasing the productivity of the existing employees, instead of hiring additional staff, and on improving the selection criteria. Using workforce analytics, you can try to find the characteristics of top performing people and teams, and the conditions that facilitate top performance. These findings can be used to increase productivity and to select candidates that have the characteristics of top performers. When productivity increases, you need less people to deliver the same results. A related read on this topic are the 3 reasons to stop counting heads. 5. What is in it for me? A lack of trust can influence many workforce analytics efforts. If the focus is primarily on efficiency and control, employees will doubt if there are any benefits for them. Overall there is a shift to more employee-centric organizations, although sometimes you can doubt how genuine the efforts are to improve the employee experience. Asking the question: “How will the employees benefit from this effort?” is a good starting point for most workforce analytics projects. It also helps to create buy-in, which becomes increasingly important with the introduction of the GPDR. 6. From individuals to teams to networks Many workforce analytics projects today are still focused on individuals. What are the characteristics of our top performers? How can we measure the individual employee experience? How can we decrease absenteeism? Earlier, I gave an overview to what extend current HR practices are focused on teams. As you can see in the table, most of the practices are still very focused on the individual. Workforce analytics can help to improve the way teams and networks function in and across organizations. The rise of Organizational Network Analysis is one of the promising signs. 7. Cracks in the top-down approach The tendency to implement changes top-down, is still common. We like uniformity and standardization. In our central control room, we look at our dashboard, and we know we need to act when the lights are turning from green to orange. HR finds it difficult to approach issues in a different way. Performance management is a good example. Changing the performance management process is often tackled as an organization-wide issue, and HR needs to find the new uniform solution. In line with the trend called “the consumerization of HR”, employees are expected to take more initiative. Employees are increasingly tired of waiting for the organization and HR, and want to be more independent of organizational initiatives. If you want feedback, you can easily organize it yourself, for example with the Slack plug-in Captain Feedback. A simple survey to measure the mood in your team is quickly built with Polly (view: “How to measure the mood in your team with Slack and Polly“). Many employees are already tracking their own fitness with trackers like Fitbit and the Apple Watch. Many teams primarily use communication tools as WhatsApp and Slack, avoiding the officially approved communication channels. HR might go with the flow, and tap on to the channels used, instead of trying to promote standardized and approved channels. How can workforce analytics benefit from the data gathered by on their employee’s own devices? If it is clear, what the benefits are for employees to share their data, they might be able to help to enrich the data sets and improve the quality of workforce analytics. 8. Ignoring the learning curve In their book “Making HR measurement strategic”, Wayne Cascio and John Boudreau presented an often-quoted picture, with the title “Hitting the “Wall” in HR measurement”. The wall was the wall between descriptive and predictive analytics. There are many more overviews with the people analytics maturity levels. Generally, the highest level is predictive analytics. Patrick Coolen of ABN AMRO Bank recently mentioned a next level: continuous analytics, and he introduced a second wall, the wall between predictive analytics and continuous analytics. As predictive analytics seems to be the holy grail, many HR teams want to jump immediately to this level. Let’s skip operational reporting, advanced reporting and strategic analytics. We can leapfrog, ignore the learning curve, and jump to the highest level in one step. For many teams, ignoring the learning curve does not seem to be a sensible strategy. Maybe it is better to learn walking before you start running. 9. Give us back our time! Recently I spoke to HR professionals from big multinationals who were involved in a “Give us back our time” projects. In their organizations, the assignment to all staff groups was: stop using (meant was: wasting) more and more time of the employees and managers, please give us some time back! An example that was mentioned concerned performance management. In this organization, they calculated that all the work around the performance management process for one employee costed manager and employee around 10 hours (preparation, two formal meetings per year, completing the online forms, meeting with HR to review the results etc.). By simplifying the process (no mandatory meetings, no forms, no review meetings, just one annual rating to be submitted per employee by the manager), HR could give back many hours to the organization – to the relief of both managers and employees. Big HR systems generally promise a lot. But before the system can live up to the high expectations, a lot of work needs to be done. Data fields must be defined. Global processes must be standardized. Heritage systems must be dismantled. This results in a lot of work (and agony), for employees, for managers, for HR and for the implementation partners (who do not mind). Workforce analytics can help a lot in the “give-us-time-back” projects, for example by some simple time-measurement. Measure the time a sample of managers, employees, and HR professionals spend on different activities, and estimate the value these activities optimizes the core activities of the organization (e.g. serving clients and bringing in new clients). 10. Too high expectations The expectations of workforce analytics are often too high. Two elements must be considered. In the first place, human behavior is not so easy to predict, even if you have access to loads of people data. Even in domains where good performance is very well defined and where a lot of data is gathered inside and outside the field, as for example in football, it is very difficult to predict the future success of young players. Secondly, the question is to what extend managers, employees and HR professionals behave in a rational way. All humans are prone to cognitive biases, that influence the way they interpret the outcomes of workforce analytics projects. Some interesting articles on this subject are why psychological knowledge is essential to success with people analytics, by Morten Kamp Andersen, and The psychology of people analytics, written by myself. A more general thought: what if you replaced ‘Workforce analytics’ with ‘Science’? What is the role of science in HR? The puzzle is, that there are many scientific findings that have been available for a long time but that are hardly used in organizations. Example: it has been proven repeatedly, that the (unstructured) interview is a very poor selection instrument. But still, most organizations still rely heavily on this instrument (as people tend to overestimate their own capabilities). Why would organizations rely on the outcomes of workforce analytics, when they hardly use scientific findings in the people domain? An interesting presentation on this topic that I recommend is by Rob Briner, titled evidence-based HR, what is it and is it really happening? There’s a lot that’s changing in the world of work. These are the 10 trends in workforce analytics that I’m seeing today and that will likely impact the way we work in the near future.   This article is based on a keynote I gave at the Workforce Analytics Forum in Frankfurt, Germany, on April 18, 2018. by Tom Haak Tom Haak is the director of the HR Trend Institute The HR (Human Resources) Trend Institute follows, detects and encourages trends. In the people and organization domain and in related areas. Where possible, the institute is also a trend setter. Tom has an extensive experience in HR Management in multinational companies. He worked in senior HR positions at Fugro, Arcadis, Aon, KPMG and Philips Electronics. He holds a master’s degree in Psychology. Tom has a keen interest in innovative HR, HR tech and how organizations can benefit from trend shifts. Twitter: @tomwhaak
    头条
    2018年06月27日
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    人工智能:人力资源行业真正的杀手应用程序 对人工智能的炒作和期望现在是天高。我们很快就会和我们的电脑通话。无人机将提供我们的杂货; 我们的汽车会开车自己; 而大多数白领工人将会是监控机器。这一切都是真的吗?这真的会发生吗? 作为一位遵循技术几十年的行业分析师和工程师,我想说我们正处于一个有趣的阶段:一方面,炒作远远超出了现实; 另一方面,上涨幅度可能比我们想象的要大得多。在人力资源方面,价值的机会是巨大的。 虽然几乎所有人力资源供应商现在都在建立人工智能团队,我们都希望我们的系统更加智能和实用,但我相信这个市场还很年轻,所以我想指出一些需要考虑的事情。 上周我参加了一个关于招聘自动化的会议,我们听取了奥克兰A的总经理比利比恩谈到钱球。在对Sabermetrics的历史和数据如何改变了棒球比赛的精彩讨论之后,他告诉我们,他现在在他的名单上有六位博士学位机器学习工程师,“拥有最多博士学位的团队真的很难被击败。”这就是业务中发生的事情。 人工智能在人力资源和管理中的作用 让我们认识到AI不是一些神奇的电脑化角色; 它是一个广泛的算法和机器学习工具,可以快速注入数据,识别模式,并优化和预测趋势。系统可以理解言语,识别照片,并使用模式匹配来获取关于情绪,诚实,甚至个性的信号。这些算法不像人类那样“直观”,但它们速度很快,因此它们可以在几秒钟内分析数百万条信息,并快速将它们与模式关联起来。 统计学上的AI系统可以通过绘制可能结果的曲线来“预测”和“学习”,然后根据许多标准优化决策。所以你可以想象一个人工智能系统,可以查看可能的人口统计数据,工作经历以及与候选人面试的问题,然后“预测”他们在工作中的表现如何。(HiredScore,Pymetrics,HireVue,IBM和其他公司正在研究这方面的工作。) 虽然这比听起来更复杂,但这是一项重要且高尚的举措。几周前,当我被问到这个问题时,我回答说:“我们今天制定的大多数管理决策都是由我们的裤子所在位置完成的。如果这些系统让我们变得更聪明一些,我们可以极大地改善我们的运营。“ 是的,有很多风险和障碍需要处理,但潜力非常大。 我们可以期望看到哪些杀手级应用程序? 让我列出我们可以看到突破性成果的许多领域中的一小部分。 在招聘中,我们做出了很多关于直觉的决定。一项研究表明,大多数招聘经理会在候选人的头60秒内对候选人做出决定,通常基于外表,握手,着装或演讲。我们是否真的知道什么样的特征,经历,教育和人格特质能够保证某个角色的成功?不,我们不。经理和人力资源专业人士使用数十亿美元的评估,测试,模拟和游戏来雇佣人员 - 但很多人告诉我他们仍然有30-40%的人选错了。 基于人工智能的算法可以清除简历,找到优秀的内部候选人,分析高绩效人员,甚至解码视频访谈,并告诉我们关于谁可能成功的信号。我们的一个客户现在使用Pymetrics的基于人工智能的游戏化评估来筛选其市场和销售角色的候选人,他们的成功率提高了30%以上,同时消除了固有的所有“面试偏见”和“教育谱系偏见”当前的过程。AI在招聘中将是巨大的。(7月27日 在上海,招聘科技创新大会中将会有谈到类似的话题,值得关注) 顺便说一句,尽管我们都担心工作技能(软件技能,销售技能,数学技能等),但大多数研究表明,技术技能只占一个人成功的一小部分。在我们最近关于高影响力人才招聘的研究中,我们发现,那些从招聘中获得最高财务回报的4级成熟公司将其招聘标准的近40%分配到情感和心理特征,如抱负,学习敏捷性,激情和目的感。人工智能也会发现这一点吗?也许。 在员工发展和学习,我们真的不知道如何完美地“训练”人。全球L&D行业超过2000亿美元,大多数学习专业人士告诉我们,至少有一半是浪费(遗忘,不恰当的应用,或只是浪费人们的时间)。但我们不知道这是哪一半!作为一个人,你是否知道你需要学习什么以更好地工作?我们都有一个非常好的主意,但是如果我们有算法来监控和研究团队中表现最好的成员的技能,行为和活动,然后告诉我们如何更像他们呢?这类“类似Netflix”的算法现在正在进入学习平台的世界,使得学习与观看有线电视一样有用和有趣。市场还很年轻,但机会很大。我们的研究表明,普通员工每周少于25分钟的时间进行培训和学习; 如果我们让这个时间更相关,每个人都会表现得更好。 在管理和领导方面,我们像禅师一样运作。我们阅读书籍,去讲习班,复制我们敬佩的老板,并且荣耀当天成功的领导者。我们真的了解领导科学吗?我建议这是一个短暂的话题。今年我们专注于目标,使命和追随者。就在几年前,这就是“仆役式领导”,而我年轻的时候就是“执行力和财务智慧”。大多数研究发现,有几十种管理和领导特质定义了成功,我们每个人都带来了一点点不同他们的独特组合。 AI现在可以帮助解码这个。我知道有三家供应商构建了“基于AI”的辅导工具,需要反馈的系统,阅读评论以及员工和团队的直觉情绪。他们使用这些数据来将这些个人和团队的问题与高绩效团队进行匹配,并利用这些数据为经理和主管“推动”如何做得更好。一位客户告诉我,在使用这个工具仅仅3个月的时间里,他们的领导团队仅仅基于小的行为推动就显示出企业价值提高了25%。 在欺诈和合规方面,机会是巨大的。一项研究发现,窃取或实施犯罪的员工对同龄人(与他们一起工作的人有不良习惯)具有“传染性”。人工智能可以查看组织网络数据(电子邮件流量,评论意见)并确定压力领域,可能存在道德失范的领域以及许多其他形式的合规风险,并指出人力资源部或合规官员的“红色区域”他们可以在不良行为发生之前进行干预。 在福祉方面,人工智能现在被用来识别导致工作表现差的行为。在安全方面,人工智能可以识别导致事故的行为和经历。在销售中,人工智能可以通过微调和真实数据向专家提供初级销售人员的提示。该名单继续下去。 有风险吗?人们分析会发生什么? 所有这些应用程序都是新的应用程序,并且看起来很令人兴奋,但有很多风险需要担心。最大的挑战在于,如果没有“训练数据”,人工智能就无法工作。换句话说,算法会从过去学习。如果你目前的管理实践存在偏见,歧视性,惩罚性或过度等级化,你可能会把所有你讨厌的事情制度化。我们需要透明和“可调整”的AI,以便我们可以检查这些模块以确保它们正在做正确的事情。就像早期的汽车并不总是直行,我们早期的算法将需要“保险杠”和“调音旋钮”,以便我们学习如何使它们更加精确。 这些系统可以将偏见制度化。假设你的公司从未雇用女性工程师,并且拥有很少的非洲裔美国人工程师。人工智能招聘系统自然会得出结论:女性和黑人工程师不太可能进入管理层。这种类型的偏差必须从算法中仔细去除,并且需要时间才能做到这一点。 数据暴露和无意滥用也有风险。考虑一种常用的分析方法,我们试图预测高绩效员离开公司的可能性。如果我们告诉管理者“这个人很有可能离开”我们实际上可能会制造错误的行为 - 管理者可能会忽视这个人,或者以不同的方式对待他或她。我们必须认真学习如何运用行为经济学,所以我们不要让AI偶然变成“HAL”(电影2000)。人工智能是一种建议和改进的“工具” - 而不是今天的独立解决方案系统。 我本周与Entelo的一位AI领导交谈过,并且我们讨论了创建“解释性”和“透明”AI系统的必要性。换句话说,每当系统做出决定时,它应该告诉我们为什么做出这个决定,所以我们人类可以决定它使用的标准是否仍然准确。他告诉我这是新工具最重要的标准之一,不幸的是,今天大多数AI系统都是一个完整的黑匣子。 考虑一下当自动驾驶车辆发生碰撞时会发生什么。我们花了很多时间来诊断它是如何发生的,视觉或算法系统失败,以及可能导致事故的条件。如果人工智能对候选人,薪资调整或管理层干预进行错误推荐,该怎么办?我们会发现吗?我们会诊断它吗?我们会注意到它太迟了吗?我们还有很多工作要做,并且学习如何“训练”我们的基于管理的人工智能系统才能正常工作。 人工智能将成为人力资源解决方案的差异化特征吗? 现在围绕人工智能的炒作处于历史新高。每个人力资源软件供应商都希望您相信他们拥有一个机器学习团队和一流的AI解决方案。是的,这些功能对这个行业非常重要,但不相信炒作。 人力资源工具的成功取决于许多因素:算法的准确性和完整性,系统的易用性,但比提供所谓的“狭窄人工智能”的能力更重要 - 或者非常具体的解决方案解决你的问题。这只能在供应商拥有大量数据(以训练其系统)时才能完成,并且他们对其工作效果获得大量反馈。所以我相信进入障碍将成为关注焦点,业务战略和客户亲密关系,而不仅仅是拥有优秀的工程师。 除非你可以在公司证明这一点,否则不要购买一个黑盒子系统。每个公司的管理和人员决策往往都是基于文化的,所以我们必须花时间在现实世界中尝试这些系统并调整以充分利用它们。例如,IBM花费数年时间为公司,文化和商业模式优化其基于AI的薪酬和职业解决方案。他们现在将这些工具引入企业客户,并发现每个实现都会教授IBM关于算法的新内容,以使其更好地适应该行业,文化或组织需求。 尽管存在这些问题,上行是巨大的 尽管存在这些挑战和风险,但其优势是巨大的。公司将40-60%的收入用于工资,其中大部分的巨额费用是由我们对直觉感到的管理决定所驱动的。随着人力资源管理系统变得更加智能化,更加成熟,并且更专注于具体问题,我相信我们将看到生产力,绩效和员工福利的显着提升。我们必须耐心,警惕,愿意投资。 以上由hrtech  AI 翻译完成,仅供参考! 作者:JOSH BERSIN
    头条
    2018年06月25日
  • 头条
    员工希望在自己的时间获得福利,但人力资源技术不足以支持 来源/ Valerie Bolden-Barrett 简介: 一项新的Paychex研究发现,大多数全职员工(73%)希望自助获取信息,例如工资,保险和退休等,但超过一半(500%或更少员工)的雇主(53%)不提供此类服务服务。   “随时随地:员工自助服务简化人力资源,节省资金,参与员工”的结果也表明公司规模与自己动手(DIY)访问人力资源相关服务之间的关联。   员工最想做的DIY操作是更新个人信息,管理带薪休假(PTO),查看或下载工资数据,进入和退出工作时间以及查看工作时间表。   另外,80%的受访者平均更喜欢使用桌面或移动设备在网上完成这些任务,而不是在纸上。   Paychex公司战略和产品管理副总裁汤姆哈蒙德表示,让员工拥有自力更生的工具可以让人力资源经理有时间处理更多关键问题,例如遵守就业法,并赢得人才大战。   观点: 员工期望在他们的工作生活中具有相同的独立性和授权能力,以便技术为他们的个人生活提供支持。     工人们越来越期望他们的工作技术像消费者技术一样容易获得,而不知道这一点的雇主可能会落后于他们的同行。   例如,过时的薪资系统可能会耗尽雇主资源并阻碍参与。   DIY服务对于需要更多关注人力资本管理(HCM)战略方面的人力资源管理人员以及希望获得权力并控制影响其就业职能的员工而言,是一项双赢的建议。   数字化转型通常是目前首席执行官的真正关注点,部分原因在于这些问题。   例如,Paychex最近与Facebook的Workplace平台合作,为其HCM套件用户提供社交媒体体验。通过在Workplace中使用Flex,用户将可以访问Paychex Flex Assistant,这是一种虚拟工具,可以解答员工的常见问题。   这些工具正在成为市场上的参与工具,也是人力资源经理日常压力的一种方式。
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    2018年06月20日
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    人力资源软件如何减轻您的完整和最终(F&F)结算流程   无论什么原因,当一个人离开公司时,肯定会为该公司的人力资源部门启动大量新工作。正确的做法是,管理所有适当的记录,以回复与辞职条款有关的信息查询,但应该管理所有信息。 、 这就是人力资源软件出现的原因,它可以减轻员工分离的巨大负担。   例如,具有完整和最终结算内置模块的智能人力资源软件将帮助人力资源部门快速响应与雇员与公司分离有关的所有请求,同时保持从头到尾的跟踪流程。   以下是一个好的人力资源软件如何协助人力资源管理最常见但敏感的方面。   智能和自动F&F计算 由于HR人员是启动完整和最终结算流程的人员,拥有人力资源软件是一件聪明的事情,因为它以智能和准确的方式计算员工的全额和最终应付款以及应收账款。 人力资源软件可以计算所有关键薪资方面,如未付薪水,医疗,LTA,小费,余额留存,奖金等。最重要的是,人力资源软件将自动扣除员工从其全额和最终结算费用中支付的税额,并相应地更新工作表格。   计算可回收物品 员工退出公司时,有权在退出期间申请重要补偿。由于F&F解决是一项繁重的任务,因此拥有良好的人力资源解决方案不仅仅是对人力资源人员的掩饰,它会在几秒钟内计算出所有可回收物品,从而为离职员工更快,更平稳地退出公司铺平道路。   员工文件的一站式中心 即时访问正确的记录是完整和最终结算过程中的关键。人力资源软件确保您有正式的辞职副本以及必要的文件。如果员工被解雇,则必须保持文件和通信。   您可以使用人力资源软件以及退出面试文件,跟踪与特定员工裁员有关的所有相关文件和表格。您始终可以选择将相关注释/注释添加到这些文档中。   长话短说,人力资源软件为您提供完整和最终结算流程的鸟瞰图。最重要的是,它提供了一份信息丰富的F&F信函,这封信函介绍了整个结算流程。
    头条
    2018年06月15日
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    小型企业选择云端人力资源部门的6个理由   全球各地的企业都在快速唤醒云计算的想法及其提供的诸多好处。无论是公共还是私有云计算,大量企业都以某种方式使用云。 随着许多关键业务操作以极快的速度转移到云端,人力资源部也不例外。事实上,组织通过将其人力资源流程转移到云生态系统来节省一笔财富。 是的,实施基于云计算的工资软件或人力资本管理解决方案可以解决与员工和人力资本管理有关的各种问题。 这里有六个原因说明为什么云中的人力资源部门对小型企业来说是完全合理的。   1.人力资源聊天机器人 所以,我们都知道'Siri'(臭名昭着的聊天机器人/虚拟助手)及其在我们生活中的角色! 现在想象一下聊天机器人为您附近的人力资源部门提供动力。是的,大多数基于云计算的工资软件解决方案都配备了智能HR聊天机器人,作为人力资源部门的扩展部分。 员工向人力资源部门提出的大多数问题都由使用机器学习和人工智能算法与人类交互的人力资源聊天机器人来解答。这些机器人将执行世俗和常规的与人力资源相关的任务,例如在旅途中进行员工调查,收集员工信息,提供工资细节等。     2.可扩展和预算友好 是的,基于SaaS的订阅计划使云工资软件成为初创企业和小企业的完美选择。简而言之,“即用即付”功能使基于云计算的人力资源软件可以充分扩展至中小型企业。此外,它还消除了为维护和实施目的而增加IT设备和人员的需求。     3.统一的HR数据库 基于云计算的工资软件可作为所有重要员工数据的综合集中中心,从而确保每个人都处于同一页面上。没有更多的工资和出勤冗余或不一致。 最重要的是,基于云计算的工资软件可以即时访问人力资源数据库,例如离职,休假详情,病假等等。       4.时间和出勤 基于云的人力资源软件可以帮助您通过其自动时间跟踪功能保持准时和旷工。它可以让您创建和处理多个工作班次,员工时间安排,离职/出勤等等。     5.自助服务 将人力资源运营转移到云计算中最令人兴奋的部分就是这里。 员工自助服务可以让公司的员工从他们的手持设备上即时检查叶子,申请叶子,查看税务/工资细节等,而无需打开人力资源管理系统。由于它允许员工自己检查和更新他们的个人信息,因此HR不必这样做,从而为他们节省时间和精力。     6.人力资源分析 无论您的人力资源部门是否使用数以百计的预格式化图表或报告,您都可以将它们与云工资软件进行整合。找到了!智能HR随时随地从任何设备上报告。享受人工访问直观的人力资源分析和管理信息系统报告,进行一系列与人力资源相关的活动,例如人员配备,出勤率,绩效管理,招聘等。   就其核心而言,云计算工资软件随着您的小企业而增长。易于实施和使用,对于正在成长的公司来说,投资于基于云计算的人力资源解决方案当然是有意义的。   除了节省时间和口袋友好之外,当我们谈论创新时,云薪水软件是一个福音。是的,自动系统升级和更新使云HR解决方案相对更容易保持有用和前沿。   以上内容基于AI翻译,仅供参考
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    2018年06月14日
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