人力资源分析战略对高绩效企业至关重要的3个原因
文/ Chiradeep BasuMallick
工敬业度和生产力正在发生变化。随着您的员工在多个平台上分享意见，评论和评论，大量数据量涌入其中，充满了丰富的见解。拥有强大的人力资源分析战略可以帮助衡量绩效，调整劳动力管理蓝图，并将雇主推向更高的高度。
如今，全球各地的企业都在使用人力资源分析。对于高绩效雇主而言，智能人力资源分析战略可以带来几项总体优势，同时提高投资回报率。那么人力资源分析如何真正发挥作用，以及它对企业盈利的真正影响是什么？
我们分享了关于良好表达的人力资源分析战略影响的5个原理说明。
提高招聘精度
招聘仍然是公司的一个重要领域，充满了几个波动的挑战。人力资源分析战略可以帮助您查看以前的决策和方法，改进方法，并使招聘更符合特定要求。通过消除浪费在无用应用程序上的时间并专注于真正重要的连接，这使得该过程更快。
提高员工保留率
高员工流动率是现代企业的另一个问题。通过强大的人力资源分析战略，您可以确定员工离开公司的原因，让您重新考虑保留策略。它甚至可以检测员工脱离，这是保留的一个重要区别。
从IBM的案例中汲取灵感：Big Blue利用他们的人力资源分析战略来实时了解员工敬业度。通过分析员工之间的社交媒体数据使用情况，发现可以事先获得48％的员工参与度分数变异性。IBM开发了Social Pulse，一种“社交媒体情绪”工具，作为回应，并创建了一个基于数据的渠道来听取员工的声音。
通过评估员工流失数据，进行离职面谈和满意度调查，您的人力资源分析战略必须超越数字，并明确了解对企业有用的内容以及需要修复的内容。这最终降低了雇用新员工以取代一连串退出的成本，从而提高了底线。
释放人类潜能
招聘，维护和培养表现最佳的人才是整体企业生产力不可或缺的一部分。人力资源分析策略应该旨在消除混乱，并提供关于员工工作记录，满意度，产出以及参与项目或目标的关键见解。
因此，最值得尊敬的员工得到奖励和认可，参与度最佳，个人有能力发挥自己的最大潜能，并改善对公司核心目标的贡献。
SAP SuccessFactors人力资本管理研究高级副总裁Steven Hunt博士在与HR技术专家的对话中说。“在金融危机之后，一家大公司不得不迅速降低总劳动力成本。高级领导人获得了显示不同部门薪资和员工人数的电子表格。
他们确定了一个团队正在研究一种新的但非关键的产品，这种产品的劳动力成本相对较高。但领导者从未查看过显示团队成员能力的数据。
在让团队离开后不久，该公司发现它已经淘汰了几位技术娴熟的工程师。几个月后，该公司不得不重新聘用这些员工担任顾问，其费率远高于他们作为全职员工的薪酬。他们对公司承诺的感觉已经丧失。
这些领导者是聪明的人，他们以错误的方式解释准确的数据并盲目做出决定。他们缺乏的是充分了解其决策背景及其影响的额外数据，无论是积极的还是消极的。
使用人力资源数据的部分技术是以引导人们得出适当见解和结论的方式呈现它。这是关于在正确的背景下提供数据以及有效的分析解释。
总结
在数据是真正的变革代理的情况下，人力资源分析战略可以完全改变您的雇佣方式，员工的承认方式，评估价值和生产力的方式，最后使产出和盈利能力更加精简。所需要的只是实用和个性化的应用。
以上为AI翻译，内容仅供参考。
原文链接：3 Reasons Why an HR Analytics Strategy is Essential for High Performance Companies
人力资源分析
2018年10月22日
人力资源分析
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.
人工智能如何促进人力资源分析 How AI Can Boost HR AnalyticsMarianne Chrisos
How AI Can Boost HR Analytics
使用AI技术改善您的人力资源报告。
了解如何使用AI来更有效地衡量您的人力资源指标。
随着人工智能技术的不断发展，也许你会想知道在人力资源部门是否有人工智能的地方。人工智能在人力资源分析中的作用是什么？我们花了一些时间专门研究人力资源分析的好处，以及人工智能如何帮助促进人力资源部门的报告和分析，以更多地了解组织的健康和效率。
每位经理应该知道的人力资源分析类型
分析可能不是HR谈到的第一件事。您的具体人力资源需求可能更多地集中于遵守规则条例或员工福利。以下是一些人力资源分析示例，有助于说明为什么报告在每个部门（包括人力资源部门）都很重要。
员工流动率：人力资源部门和企业可能会有一个偶然的想法，即他们的组织内有多少员工流失 - 也就是说，人们退出的频率如何，或者公司必须重新雇佣相同职位的频率。如果一位人力资源经理不断发布需要销售人员的广告，这可能意味着销售人员正在放弃 - 或者销售额在增长，他们需要更多人来满足需求。为了真正了解其原因是由于营业额还是其他原因 - 以及衡量员工翻身的频率，这可能会告诉您关于商业或文化的一些事情 - 您需要使用分析来衡量。
申请人的质量和数量：你的招聘信息有多好？你的企业声誉有多好？你可以找到这些问题的答案 - 并且如果你发现答案“不是很好”，通过分析你的工作发布的申请人数，特别是申请人的质量，可以帮助确保做出调整。使用报告软件可以衡量您的候选人是否符合质量要求，并报告申请人的属性。他们有相同行业或职位的经验吗？他们有帮助组织发展或从事大型项目的历史吗？分析可以帮助您在回答这些问题的同时节省时间。
文化：虽然上述两种分析可以让您对企业文化有所了解，但具体的文化分析对于了解您的企业的健康状况非常重要。使用人力资源工具，如自我报告软件和人力资源调查，您可以编辑和分析数据，分享员工对文化态度的共同点。
人工智能在人力资源分析中的作用
人工智能是一种改变游戏规则的技术，因为它能够分析大量数据并找到模式，甚至做出预测。人力资源分析工具从人工智能中受益，因为人力资源部门有大量原始数据可供使用，人工智能可帮助快速有效地对这些数据进行分类。人才管理系统可以结合人工智能来分析简历关键字和其他指标，以帮助预测潜在招聘信息的最佳人选，从而为人力资源招聘人员节省大量时间。AI还与其他HR数据分析工具一起工作，以推荐培训领域或预测潜在的营业额。
以上AI翻译完成。
作者：Marianne Chrisos
Born in Salem, Massachusetts, growing up outside of Chicago, Illinois, and currently living near Dallas, Texas, Marianne is a content writer as a company near Dallas and contributing writer around the internet. She earned her master's degree in Writing and Publishing from DePaul University in Chicago and has worked in publishing, advertising, digital marketing, and content strategy.
人力资源分析
2018年02月21日
人力资源分析
“人员分析现在可以成为战略性竞争优势”
工业工程师弗雷德里克泰勒在1911年发表了他的报告“ 科学管理”，该报告研究了钢厂工厂工人的流动和行为，从而开始了这一趋势。此后，公司已经部署了数千次参与调查，研究了最高领导者的特征，对留存率和营业额进行了无数次评估，并建立了大量的人力资源数据仓库。所有这些努力都是为了弄清楚“我们能做些什么来让我们的人们获得更多收益？”
那么现在这个域被称为人们的分析，它已经成为一个快速增长的核心业务举措。一项题为“ 高影响力人物分析 ”的研究报告由Deloitte在去年11月由Bersin完成，发现69％的大型组织拥有人员分析团队，并积极构建与人员相关数据的综合存储。
为什么增长和为什么业务势在必行？几个技术和商业因素相互碰撞使这个话题变得如此重要。
首先，组织拥有比以往更多的与人员相关的数据。由于办公生产力工具，员工证章阅读器，脉搏调查，集成的企业资源规划系统和工作中的监控设备的激增，公司拥有大量关于员工的详细数据。
公司现在知道人们与谁交流，他们的地点和旅行时间表，工资，工作经历和培训计划。内置于电子邮件平台中的组织网络分析的新工具可以告诉正在与谁交流的领导者，用于音频和面部识别的新工具识别谁处于压力之下，以及摄像机和热传感器甚至可以确定人们在他们身上花费了多少时间书桌。
可以认为，这些信息大部分都是保密和私密的，但大多数员工并不介意获取这些数据的组织，只要他们知道正在改进他们的工作体验，正如2015年会议委员会的研究所显示的那样，Big数据并不意味着大 哥哥。虽然从5月25日起可执行的欧盟通用数据保护条例标准将会将隐私权和治理责任放在人力资源部门，但雇主正在加紧处理这些数据并小心处理这些数据。
其次，作为获得所有这些数据的结果，公司现在可以学习重要而有力的事情。不仅高管们被迫就多元化，性别薪酬公平和营业额等议题进行报告，而且他们现在还可以使用人员分析来了解生产力，技能差距和长期趋势，这些可能会威胁或创造业务风险。
例如，一个组织发现欺诈和盗窃事件是“具有传染性”，导致同一楼层的其他员工在一定距离内出现类似的不良行为。另一种方法是使用情绪分析软件来衡量组织中的“情绪”，并根据他们的沟通模式来识别具有高风险项目的团队。
许多组织现在都在研究营业额，甚至可以通过监测电子邮件和社交网络行为来预测它，从而使管理人员能够在辞职前指导高绩效员工。组织现在使用分析和人工智能或人工智能来解码职位描述，识别造成偏倚招聘池的单词和短语，并防止性别和种族多样性。制造商使用人员分析来识别可能发生事故的员工，而咨询公司可以预测哪些人可能会因过多的旅行而被烧毁，而汽车公司现在知道为什么某些团队按时完成项目，而其他人则总是迟到。
因此，人工智能进入领域，给予它更多的权力和规模。一个新的基于人工智能的分析工具会向管理人员发送匿名电子邮件，询问简单问题以评估管理技能。通过其精心设计的算法，它为管理人员提供了一套无需赘述的建议，并在短短三个月内将管理效率提高了8％。
据Sierra-Cedar 2017人力资源系统调查显示，对于人力资源部门而言，人员分析现在是公司希望替换或升级人力资源软件的首要原因。
但对于首席执行官，首席财务官和首席运营官来说，这更重要。当一个销售团队落后于其配额实现或者商店的销售数字落后时，为什么领导者不会问“我们可能能够解决的团队中的人员，实践和管理者有什么不同？”或者甚至更大问题是“如果我们想通过收购德国的某家公司来发展我们的业务，文化和组织的影响会是什么？”这些关键的战略问题都可以通过人员分析来解决。
这门学科的历史是战术性的，有点神秘。多年来，工业心理学家领导了这项工作，主要关注员工敬业度和营业额。然而，今天，该行业正在采取新的行动，将其精力重新集中在运营，销售，风险和绩效指标上。技术工具在这里，公司已经有人工智能工程师准备以强大而有预见性的方式分析数据。分析人士表示，这个领域将会持续增长，请记住，对于大多数企业而言，劳动力成本是资产负债表中最大和最可控制的支出。
底线很明显：人们的分析现在可以成为战略竞争优势。专注于这一领域的公司可以出租，淘汰和淘汰竞争对手。
以上由AI自动翻译。
Fredrick Taylor, an industrial engineer, started this trend in 1911 when he published his report Scientific Management, which studied the movement and behaviour of factory workers in steel mills. Since then companies have deployed thousands of engagement surveys, studied the characteristics of top leaders, done countless reviews of retention and turnover, and built massive human resources data warehouses. All in an effort to figure out “what can we do to get more out of our people?”
Well now this domain is called people analytics and it has become a fast-growing, core-business initiative. A study, entitled High-Impact People Analytics and completed last November by Bersin by Deloitte, found that 69 per cent of large organisations have a people analytics team and are actively building an integrated store of people-related data.
Why the growth and why the business imperative? Several technical and business factors have collided to make this topic so important.
Firstly, organisations have more people-related data than ever before. Thanks to the proliferation of office productivity tools, employee badge readers, pulse surveys, integrated enterprise resource planning systems and monitoring devices at work, companies have vast amounts of detailed data about their people.
Companies now know who people are communicating with, their location and travel schedules, their salary, job history and training plans. New tools for organisational network analysis, built into email platforms, can tell leaders who is communicating with whom, new tools for audio and facial recognition identify who is under stress, and video cameras and heat sensors can even identify how much time people spend at their desks.
It could be argued that much of this information is confidential and private, but most employees don’t mind organisations capturing this data, as long as they know it is being done to improve their work experience, as shown in 2015 Conference Board research, Big Data Doesn’t Mean Big Brother. While European Union General Data Protection Regulation standards, enforceable from May 25, will put the burden of privacy and governance on HR departments, employers are stepping up to this and treating such data with great care.
Secondly, as a result of having access to all this data, companies can now learn important and powerful things. Not only are executives being forced to report on topics such as diversity, gender pay equity and turnover, but they can also now use people analytics to understand productivity, skills gaps and long-term trends that might threaten or create risk in their business.
One organisation, for example, found incidents of fraud and theft were “contagious”, causing similar bad behaviour among other employees on the same floor within a certain distance. Another is using sentiment analysis software to measure “mood” in the organisation and can identify teams with high-risk projects just from the patterns of their communication.
Many organisations now study turnover and can even predict it before it occurs by monitoring email and social network behaviour, enabling managers to coach high performers before they resign. Organisations now use analytics and artificial intelligence or AI to decode job descriptions, identifying words and phrases that create biased recruitment pools and prevent gender and racial diversity. Manufacturers use people analytics to identify workers who are likely to have accidents, while consulting firms can predict who is likely to be burnt out from too much travel and automotive companies now know why certain teams get projects done on time when others are always late.
AI is, therefore, entering the domain, giving it even more power and scale. A new AI-based people analytics tool sends anonymous emails to a manager’s peers asking simple questions to assess managerial skills. Through its carefully designed algorithms, it gives managers an unthreatening set of recommendations and has improved managerial effectiveness by 8 per cent in only three months.
For human resources departments, people analytics is now the number-one reason companies want to replace or upgrade their HR software, according to the Sierra-Cedar 2017 HR Systems Survey.
But for chief executives, chief financial officers and chief operating officers, it’s even more important. When a sales team is behind its quota attainment or a store’s sales numbers fall behind, why wouldn’t a leader ask “what’s different about the people, practices and managers at those teams that we may be able to address?” Or an even bigger question is “if we want to grow our business by acquiring a given company in Germany, what will the cultural and organisational impact be?” These critical strategic questions can all be answered by people analytics.
The history of this discipline is tactical and somewhat arcane. For years industrial psychologists led the effort and focused primarily on employee engagement and turnover. Today, however, the industry is taking on a new light, refocusing its energy on operational, sales, risk and performance measures. The technology tools are here and companies have AI engineers ready to analyse the data in a powerful and predictive way. And analysts say this domain will grow for years to come; remember that for most businesses, labour costs are the largest and most controllable expense on the balance sheet.
The bottom line is clear: people analytics can now become a strategic competitive advantage. Companies that focus in this area can out-hire, out-manage and out-perform their competitors.
人力资源分析
2018年02月13日
人力资源分析
7个原因告诉你：为何公司仍旧在与人力资源分析作斗争
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大多数人力资源领导人已经意识到了运用人力资源科技和分析技术的优势，但是有多少人能完全利用上这门技术？
来自马来西亚、新加坡以及香港的107个专业HR进行了一次投票。一份新的报告由CIPD进行研究，其最重要的原因便是限制机构已经意识到了人力资源分析的有利之处。
这份报告显示，组织认为人力资源分析非常重要且可能成为潜在的变革。他们承认这种分析能力存在于早期阶段，并且会被一系列因素所阻碍。
很多机构已经开始了他们人力资源分析的旅程，但现在仍处于初期阶段。那些有意把人力资源分析作为战略目标的机构，将会朝着可持续性、有竞争性、获得优势等方向迈进。
以下是7条报告中所展示的原因：
缺乏商业投资和人力资源分析的专业性。57%的人力资源专家说人力资源分析技巧缺少实用性。
在全球形势中，人力资源分析的应用在部门或公司内是分散的。
将近一半（48.5%）的机构缺乏先进的人力资源技术，而这已满足了业务需要，并且只有23.4%的机构使用了综合性的人力资源分析软件。
这些约束力，例如 投资、领导力、理解力和人力资源整体成熟性将阻碍战略发展层面，而在操作层面技术和分析能力将抑制它。
标准化是一个重大的挑战——人力资源专家并不确定是应该为他们的公司开发特殊措施还是发展标准措施。
大多数的数据可以用于反思和洞察,而不是使用开发指标来预测数据。
组织仍然把那些以文化方面做为着眼点的公司作为他们数据的来源，很少关于其他战略方面的活动被评估和报道，例如生产力和绩效。
“进化的脚步也许会非常的慢，但是这次研究表明，把人力资源分析加入到商业中或不同地域中将是一件非常有潜力的事情。”
“小型项目能通过平均计分卡，清晰的展示出商业洞察力，这也是一种让人力资源分析能够获得广泛购买的一种方式。并可以获得更多关于技术以及技能的投资，来支持这一重要的活动。”他补充道。
这个报告同时也包含了四个重要的点，来帮助组织机构通过人力资源分析获得更多的利益。
1、人力资源部门应该多吸引一些有分析才能的人加入这个行业，并完善人力资源专家对于分析的基础理解。
2、人力资源专家不光要从事组织内部的业务，也要与业内同行建立良好的时间方法。
3、人力资源领导应该鼓励发展更好的度量标准，并分析其他业务领导，利用循证分析项目来推动关于人力资源分析的投资。
4、不能仅仅发展高级专业人士的分析技能，这些技能应该同事被运用到人力资源外的部分，从初级人员到高级都该运用学习。
7 reasons why firms are still struggling with HR analytics
Most HR leaders may have realised the advantaged of using HR technology and analytics, but how many are fully equipped to leverage on the benefits of such technology?
Polling 107 HR professionals from Malaysia, Singapore, and Hong Kong, a new report by CIPD delved into the biggest reasons which are limits organisations from realising the benefits of HR analytics.
It concluded that while organisations view HR analytics as hugely important and potentially transformative, they admit that these type of analytics are at the very early stages of capability and are being held back by a range of factors.
“Many organisations have begun their HR analytics journey but it is still in it’s very early days. Those organisations that are able to use HR analytics to be more strategic are likely to make significant steps towards sustainable, competitive advantage,” commented Edward Houghton, CIPD’s research adviser.
Here are seven such reasons which were identified in the report:
1. A lack of business investment and HR analytics expertise, with 57% of HR professionalssay there is a lack availability when it comes to analytical skills.
2. Reflecting the global situation, the application of HR analytics is fragmented, in sectors and organisations.
3. Almost half (48.5%) of organisations lack sophisticated HR technology which meets the needs of the business and only 23.4% are currently using a fully integrated HR analytics IT programme.
4. Constraints such as investment, leadership understanding and overall HR maturity are holding back development at a strategic level while at an operational level, technology and the analytic skill set are inhibiting the development of the function
5. Standardisation is a significant challenge – HR professionals are unsure whether to develop context specific measures for their organisation or standard measures for benchmarking purposes
6. The majority of data is used as a reflective insight, rather than using indicators to develop forecast data
7. Organisations continue to focus their attention of engagement and data around cultural aspects of the firm as their main sources of people data. Far less is being measured and reported with regards to other strategic activities such as productivity and performance.
“The pace of evolution may be fairly slow, but this study shows that there is real potential for HR analytics to add value across businesses and across the region,” Houghton stated.
Small projects that show clear business insights through a balanced scorecard are an essential way to get wider buy-in from the business on the value of HR analytics, and can open the door for greater investment in technology and skills to support this critical activity,” he added.
The report also included four key recommendations for organisations to reap more benefits from HR analytics.
Firstly, HR functions should attract more analytics talent into the profession and develop HR experts with a foundation of analytical understanding.
Secondly, HR professionals in the region should look to working across the business functions in their organisations as well as externally with industry peers to establish best practice methods.
Thirdly, HR leaders should champion better metrics and analytics to other senior leaders in the business and drive investment in HR analytics using evidence-based projects.
Lastly, instead of only developing analytical skills in senior professionals, these skills should be developed throughout the HR function, from junior-level professionals to senior-level professionals.
来源：humanresources
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