你能让你的老板把芯片放在你身上吗？-少数员工同意皮下植入但这个想法正在蔓延
Dave Coplin试图向我解释为什么两大洲的人们突然允许他们的雇主将微芯片放在他们的皮肤下。
“我这样对待我的狗 - 我为什么不自己做呢？”科普林说。我不相信，所以他发起了关于地中海派伊维萨岛上一个俱乐部的轶事，人们可以在那里筹码，然后用芯片买饮料。科普林怀疑这是因为他们没有穿很多衣服。
但是，因为你是半裸的而且没有钱包的口袋，所以要让你的雇主给你筹码是非常不同的。那么，我们是怎么来到这里的？
担任Envisioners咨询公司负责人的科普林表示，如果我们只能克服自己的娇气，那么雇主和员工都会受益匪浅。“如果它增加价值，我就是全力以赴，”他说。“今天我们看看人们这样做，感觉有点奇怪，但实际上有一些不可避免的事情。”
Patrick McMullan是威斯康星州三广场市场的总裁。在斯德哥尔摩的瑞典孵化器Epicenter进行实验后，该公司自2015年以来一直在试验切片，他的公司决定进一步开发该技术。当然，作为供应商和开发商，McMullan自己也有一个芯片植入物 - 一个大致相当于拇指和食指之间植入皮肤下的一粒米的大小。它基于近场通信（NFC）技术 - 与非接触式信用卡或移动支付中使用的芯片相同。使用注射器和非常少的血液快速简单地完成植入。
McMullan说，目前的一个限制是，由于芯片是无源器件，因此无法对其进行跟踪。就目前而言，这意味着该芯片用于访问建筑物，登录计算机以及从食堂支付费用。但麦克马伦的员工正在执行“改变世界”的使命，他说，到目前为止，已有70多名员工自愿参与实验。
“我这样对待我的狗 - 我为什么不自己做呢？”
这个想法似乎正在蔓延。除了三坊市场外，至少有160人参加了Epicenter的月度“ 筹码派对”。辛辛那提监控公司CityWatcher.com的一些员工已经获得了芯片，一些人在数字营销公司工作。在比利时称为NewFusion。毫无疑问，这是一个很好的宣传，但削弱倡导者真正相信这将成为未来十年的普遍做法。
McMullan认为，随着技术的进步，芯片将提供更多的好处。“我们正在开发能够监测生命体征的医疗用途。医生将能够主动治疗患者，而不是总是做出反应，“他说。McMullan认为，全球削减员工的数量将在几年内达到数百万，因为低于100美元的芯片的好处可能是巨大的。
自然进步？
McMullan认为没有任何不利因素，尽管人们明显担心，以难以控制或消除的方式与雇主建立密切联系感觉完全是反乌托邦。采用他自己的芯片监控人们健康的想法：未来的嵌入式技术有明显的优势，可以监测胆固醇，血糖水平，甚至只是脱水。
但是，如果某人有一块芯片监测酒精摄入量，作为退出协议的一部分呢？外科医生会被允许拒绝接受手术吗？如果保险公司从车上掉下来，可以提高患者的保费吗？随着芯片变得更先进和更广泛，可以或应该收集哪些信息以及它可能或应该去哪里的问题将变得更加复杂。其他专家也提出了对黑客行为的担忧，以及已知与宠物类似芯片相关的已知健康问题。
“显然，隐私是一个巨大的问题，”科普林补充说。“人们将如何处理这些数据？谁会去看？实际上，我必须携带手机和我的钱包，这已经够糟了。如果这解决了其中一些问题，那我就是为了它。“
尽管存在这些担忧，但很多人似乎都接受了这种情况 - 并且很快就会发生。Lynda Shaw博士，认知神经科学家，Your Brain Is Boss的作者，认为切片是一种自然进展，可能更容易为年轻人所接受。
“If you think of young men, when they’re teenagers, we often think of them as driving too fast, hotheaded,” Shaw explains. “In evolutionary psychology, that’s vital to have in society. In the old days, if a village’s crops failed, they would get the strongest young men to go and find food. They would go and find food by going beyond their usual areas and by being curious.” We may no longer be hunter-gatherers, Shaw’s theory goes, but young people will still test the boundaries, be curious, and do new things; it’s part of what they are.
在某些方面，这已经是一项成熟的技术，至少在有健康问题的人中是这样。Shaw指出，我们已经使用芯片进行人工耳蜗植入，甚至在脑损伤的情况下绕过大脑的部分区域。她说：“切削人体并不是新闻，但我们总是那些邪恶的一面说这有点过于奥威尔式。” 人们可能会担心生活在他们体内的计算机病毒或者当硬件被破坏时会发生什么。
“它将摆脱身份通行证”
智库快速未来的未来主义者兼首席执行官罗希特·塔尔瓦（Rohit Talwar）认为，削片变得非常迅速，尤其是那些希望证明自己具有前瞻思维的科技公司。
Talwar说，在那些希望获得极高安全性的公司中，人们不会进入系统或者他们不应该建造的部分建筑，以及谁想向客户证明他们在安全方面处于领先地位条款。您可能还会看到它被用作使人们能够在食堂，自动售货机上兑换货币的方式 - 它将摆脱身份通行证。“
Shaw也看到了好处。如果有人生病并且有起搏器或使用抗凝药物，通过快速扫描获得该信息可以挽救他们的生命。但她也指出了对犯罪现场的暗示。在犯罪率高且尸体被肢解的地区，Shaw指出，犯罪分子不需要整个身体来破坏安全，只需要插入芯片的肢体。她说：“你最终可能会无意中煽动比原先考虑的更可怕的罪行。”
塔尔瓦尔认为，反乌托邦是旁观者的眼睛。作为数字原生代出生的一代人可能会认为这是一种自然的进化，塑料传递为过时的，神秘的，当然也无法捕捉到我们身体内的芯片可以捕获的信息，比如健康。
“老一代人可能会认为这是非常具有侵略性的，”塔尔瓦尔说。“我去年参加了一个活动，那里他们只是为了好玩而扒人，而且这些线路正在人们的走廊上等待被破坏 - 为了故事和体验。”
我们与机器对话的一部分
那么，切削在哪里？Talwar认为这是一个不可避免的过程的一部分，在这个过程中，先驱者已经说了一段时间，如果人类要跟上人工智能的步伐，我们就必须加强我们的大脑和身体。
“这只是该过程的起点。你可以很容易地预测你的手机内存被插入你，芯片可以加速你的记忆和你的大脑，“Talwar说。“随着我们加强和扩充自己，进入超人类世界，我们可以看到这方面的巨大加速。”
“你可能最终无意中煽动了比原先考虑的更可怕的罪行。”
Coplin认为切削是关于我们如何与机器相关的对话的一部分。他指出，澳大利亚的一名男子试图从旅行卡中取出芯片并将其嵌入手中失败，因为条款和条件说不会损坏卡。“目前，这感觉很奇怪，”科普林说，“但此刻，我可能会在我的手腕上放置一种可能具有该技术的设备。为什么不在我的皮肤下更远一点？“
社会一直在争论技术的潜力及其所带来的变化。四分之一世纪以前，很少有人预测到手机的出现 - 我们更多地预计会将它们用作相机和音乐中心。现在，技术面临着额外的压力。
“我们真的失去了对处理我们数据的人的信任 - 银行，谷歌，Facebook，”科普林说。“在赢得信任之前，我们会非常担心这种事情。而且我认为这是一个真正的耻辱，因为我们可以获得的好处。“
盖伊克拉珀顿
Guy Clapperton是英国的资深记者，大约30年前开始研究人与技术之间的关系。
以上AI自动翻译完成，仅供参考！
原文
Would You Let Your Boss Put a Chip in Your Body?
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2018年07月17日
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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.
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
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