• Future of Work
    如何为人力分析专业人士创造职业道路-How to create career paths for people analytics professionals 文/David Green 文章导读 根据德勤于2017年11月发布的“高影响力人力分析研究”(High-Impact People Analytics study), 69%的大型机构(10,000多名员工)现在拥有一个“人力分析团队”。 Geetanjali Gamel在旧金山举行的“人民分析与未来工作会议”(People Analytics & Future of Work Conference)上的演讲这个话题。Geetanjali是默克公司劳动力分析的全球领导者。在2017年9月在费城举行的人民分析与未来工作会议上发言。 为什么要人力分析? 问1、你好,Geetanjali,请解释一下吸引你到人力分析领域的原因。 我工作中最有趣的部分是理解、测量和预测人类行为及其对销售和收入等业务结果的影响。因此,我很自然地被这个机会所吸引,这个机会将科学的方法引入到人们的数据中,并帮助塑造一个组织如何为其投资者带来价值,同时为其员工带来更丰富的经验。 MERCK & CO.的人力分析团队 问2、请您描述一下默克公司的劳动力分析团队的规模和结构,以及它是如何与业务联系起来的。 默克的劳动力分析团队(WFA)拥有15名成员,在全球80多个市场,69000名员工。 这个团队由三个主要支柱组成:咨询、高级分析、报告和数据可视化。 咨询——每个咨询师都与我们的业务部门(如制造、研究、销售等)保持一致。他们与领导者紧密合作,以理解和预见棘手的业务问题,并运用正确的方法解决问题,将分析转化为可操作的观点。 高级分析——高级分析团队是一群灵活的数据科学家和专业人士,他们主要专注于需要高级技术技能或很有意义的项目。它们围绕业务问题进行组织。 报告和数据可视化——他们直接与来自业务各个部门的内部客户合作,以确保合适的人在合适的时间拥有合适的数据。驱动了内部客户满意度。 三个WFA团队紧密合作,以确保识别和利用业务活动之间的协同作用。 创建一个数据驱动的文化 问3、德勤(Deloitte)的“高影响力人物分析”(High-Impact People Analytics)研究发现,在创造高级能力方面,最重要的因素是需要创建数据驱动的文化。你在默克公司是如何做到这一点的? 我们首先在人力资源社区中推广数据,推出了一个基于云的劳动力分析平台。我们还开发和部署了一个能力构建程序,其中的模块主要集中在度量选择、假设测试、数据可视化、推荐开发等方面。 此外,我们一直在利用的另一个渠道,加速人力资源数据驱动文化,是让我们更广泛的人力资源社区的成员成为分析“冠军”。 最后,我们还建立了一个人力资源领导团队,在人力资源中传达建筑数据和分析能力的信息。 高层领导的支持对于人员分析功能的成功至关重要 在人力分析中创造职业道路 问4、您对为人力分析专业人员创建职业发展道路充满热情。 为什么你认为这是如此重要? 我热衷于为那些使人力分析成为可能的人们建立更好的工作体验! 我发现这个团队能够为职业道路,继任计划和大型员工的人才流动等领域做出决策,但经常陷入无处可扩展的境地。 此外,大多数人分析团队都是人力资源部门的一员,而且往往被贴上高度专业化的“人力资源精英”卓越中心(CoE)的标签,这限制了横向或向上进入CoEs或业务部门的其他人力资源角色的机会。 最后,一个能够提供发展和职业发展的组织和领导者,可以成为吸引和留住优秀人才的关键因素。 如果我们能让更多人力分析人才流动起来,就会为人力资源和企业的其他部门增加技能、方法和拓宽视角,为企业创造额外的价值。  一个能够提供发展和职业发展的组织和领导者,可以成为吸引和留住优秀人才的关键因素 问5、关于人才分析团队的职业发展,你在默克制定了什么计划?关于人才分析团队的职业发展,你在默克制定了什么计划? 从我在默克公司工作的第一天起,我的首要任务之一就是了解我的团队的力量和抱负,并将他们的发展与他们的职业目标结合起来。我得出了一个Capability-Capacity-Connectivity模型,为我们的人员分析团队提供一个可持续发展项目。这种模式成功的一个关键驱动力是你的领导的支持和与其他团队的合作。 问6、职业发展计划的主要好处和收获是什么? “3C”方法是围绕解决障碍和为人学分析团队创建促进职业发展的桥梁而构建的。 第一个“C”:能力,能力必须在两个级别上处理。 能力级别1:构建数据、技术和分析精明的客户 能力级别2:提升人员分析团队 第二个“C”:Capacity容纳度 如果没有时间远离日常的活动,就不可能专注于一个人职业生涯的下一步 第三个“C”:连接 将人员分析团队与其他人力资源,数据科学,技术和业务专业人员联系起来,建立对双方不同类型工作的认识和相互欣赏。 英文原文: According to Bersin by Deloitte’s High-Impact People Analytics study, which was published in November 2017, 69% of large organisations (10,000+ employees) now have a people analytics team. It is a surprise then that many organisations overlook the need to develop the careers of their people analytics team. Given the pace of evolution of the field and the high-demand for talent in the space, this is an oversight that needs correction. As such, it was refreshing that the main focus of Geetanjali Gamel’s presentation earlier this year at the People Analytics & Future of Work Conference in San Francisco (see key learnings here) was on this very topic. Geetanjali is the global leader of workforce analytics at Merck & Co., Inc. (NYSE: MRK, known as MSD outside the United States and Canada). I caught up with Geetanjali recently to ask how she has created career development paths for her team as well as discuss other related topics in the people analytics field. Geetanjali Gamel speaking at the People Analytics & Future of Work Conference in Philadelphia in September 2017 WHY PEOPLE ANALYTICS? 1. Hi Geetanjali, please can you introduce yourself, describe your background and explain what attracted you to the people analytics space. Like many of my colleagues in people analytics, I’ve had a non-linear path to my current role. I am a trained economist and began my career in research at the Federal Reserve Bank of St. Louis studying topics like macroeconomic forecasting, unemployment and inflation.  With this foundation in social science methodology and research, I soon transitioned to business forecasting, predictive analysis and scenario-planning to drive customer growth and revenue projections in corporate planning and finance departments in the energy sector. The most intriguing part of my work was in understanding, measuring and predicting human behaviour and its impact on business outcomes such as sales and revenue. So, I was naturally attracted by the opportunity to bring scientific methodology to people data and help shape how an organisation can drive value for its investors along with enhanced experience for its employees. I began by building a predictive analytics function from scratch in HR in my previous role at Mastercard and since 2016 I have led the advanced workforce analytics, consulting and reporting organisation in Merck HR. THE PEOPLE ANALYTICS TEAM AT MERCK & CO. 2. Please can you describe the size and structure of the workforce analytics team at Merck and how it aligns to the business Merck’s workforce analytics team (WFA) has 15 members who support 69,000 employees in over 80 markets worldwide through a rich portfolio of people analytics products. The team consists of three primary pillars; Consulting, Advanced Analytics, and Reporting & Data Visualisation (see Figure 1 below). Figure 1: The Workforce Analytics team at Merck & Co (Source: Geetanjali Gamel) Consulting - Each consultant is aligned to one of our business divisions like manufacturing, research, sales, etc. They work closely with leaders to understand and anticipate burning business questions, utilise the right methodology to find the answers; and convert the analyses into actionable insights. Advanced Analytics - The advanced analytics team is a nimble group of data scientists and specialised professionals who focus mainly on ad hoc projects requiring advanced technical skills and/or initiatives of enterprise level significance. They are organised around business questions and may support several divisions at a time, in contrast to the end-to-end approach that the consultants take with each initiative. Reporting & Data Visualisation – This team forms the backbone of all the amazing work we are able to do, as well as the internal customer satisfaction we drive. They work directly with internal clients from all parts of the business to ensure that the right people have the right data at the right time. The three WFA teams work closely with each other to ensure that any synergies between business initiatives are identified and leveraged. CREATING A DATA-DRIVEN CULTURE 3. The recent Bersin by Deloitte High-Impact People Analytics study found that the single biggest predictor in creating advanced capability is the need to create a data-driven culture. How have you achieved this at Merck particularly with regards to HR Business Partners and the wider HR function? I agree that culture can be the strongest catalyst or impediment for people analytics. It is also ridiculously difficult to identify and alter, particularly because organisations at any given time tend to be collections of sub-cultures. But there are some patterns of behaviours, decision-making, and incentive-rewards, which distinguish data driven cultures from others. These behaviours can be purposefully incubated through a combination of upskilling, training and mind-set building. At Merck, we believe that a leading HR function is one where analytics capability is not only for the analytics team, but the whole HR team. This does not imply that every role requires equal depth in analytics, but a new baseline of data interpretation and communication skills is critical to being effective partners to the business. To this end, we started out by democratising data within our HR community by rolling out a cloud based workforce analytics platform. This is helping us drive greater familiarity and reliance on data among our HR users. We have also developed and deployed a capability-building program with modules focused on metric selection, hypothesis testing, data visualisation, recommendation development, and more. Another channel that we have been leveraging to accelerate a data driven culture in HR has been to engage members of our wider HR community as analytics “Champions”. These superheroes are critical to spreading the adoption of data informed insights, since they live and breathe the daily challenges of their colleagues; and can share relatable examples with their counterparts on how data can unlock value. Finally, we also have an HR leadership team that is aligned and strong advocates in relaying the message of building data and analytics capability in HR. Needless to say, sponsorship of senior leaders is imperative to the success of a people analytics function. Sponsorship of senior leaders is imperative to the success of a people analytics function CREATING CAREER PATHS IN PEOPLE ANALYTICS 4. You are passionate on the need to create career paths for people analytics professionals. Why do you believe this is so important? I firmly believe that the goal of people analytics is to drive value for the business as well as provide a better experience of work for employees. So naturally, I am equally passionate about building a better work experience for the people who make people analytics possible! I find a sad irony in the fact that the team which enables decision-making on areas like career pathing, succession planning, and talent movement for the larger workforce, is often stuck in a position of having nowhere to grow. From my discussions with many colleagues in this field, I have learned that the typical people analytics team usually tends to have a group of individual contributors (analysts, data scientists, consultants) and a director or senior director level leader. This leaves only one spot for the entire team to aspire to, at least for upward movement. In addition, most people analytics teams sit within HR and tend to be branded as a highly-specialised “HR-lite” centre of excellence (CoE), which limits the opportunities to move laterally or upward into other HR roles in CoEs or business units. And this reality of being “boxed-in” can be very frustrating for bright, highly-employable individuals. If you are a leader in people analytics, and if you have had to recently recruit new talent for your team, I would guess you are acutely aware of the gaping chasm between talent demand and supply in this field. In my opinion, an organisation and a leader who can offer development and career growth can be a key differentiator in attracting and retaining the best people analytics talent. Broadening that vision, if we enabled more fluid movement of people analytics talent, it would add to the diversity of skills, approaches and perspectives to other parts of HR and the business, and would create additional value for the enterprise. An organisation and a leader who can offer development and career growth can be a key differentiator in attracting and retaining the best people analytics talent 5. What program have you put into place at Merck regarding the career development of the people analytics team? From the first day of my role at Merck, one of my top priorities was to understand the strengths and aspirations of my team and align their development to meet their career goals. After multiple discussions and numerous iterations on ideas, I arrived at a Capability-Capacity-Connectivity model to power a sustainable development program for our people analytics team. The underlying idea is that if we can build the right capability within the analytics team and its clients; reallocate capacity that is being consumed by suboptimal tasks; and drive connectivity between people analytics teams and other parts of the business; then we can potentially discover and create new career paths and opportunities. But please bear in mind that a key driver of success for such a model is sponsorship from your leaders and partnership with other teams. In our case, we were fortunate to have both. This has empowered us to be inventive and co-create development opportunities for our team.   6. Please can you provide more detail on what comprises each of the Capability, Capacity and Connectivity elements of this approach. What have been the key benefits and learnings from the career development program?  The “3C” approach is built around tackling barriers and creating bridges that promote career development for people analytics teams. At the outset we knew that the team was faced with a high volume of requests needing significant manual effort. (see Figure 2 below): Figure 2: Challenges in accelerating maturity in people analytics (Source: Geetanjali Gamel) Since the day-to-day work was time and effort intensive, there was not much room to hone more sophisticated skills or build knowledge sharing relationships with others, leaving the people analytics team stuck in a loop. So, we put careful thought and purpose into adopting the following model. Capability The first “C”, or capability, had to be addressed at two levels. The first was to empower our broader HR team with the right tools and training to have greater autonomy to perform analyses. We moved to an intuitive analytics platform and organised workshops, office hours, and learning sessions to improve data literacy among our internal HR clients. This type of effort is important to free-up time for the people analytics team to build their own skillset (and path to growth), while also creating a greater awareness in other parts of HR about analytics. Figure 3: Capability - Level 1: building data, technology and analytics savvy clients (Source: Geetanjali Gamel) The second area of capability building had a more direct impact on the team. We held a team strategy session where we identified areas that needed focus for internal functional, technical and strategic competency building. These focus areas were carefully selected to create dual impact – provide us with a skill or knowledge we could use immediately in our work; and more importantly, help us practice a new behaviour that would develop us as well-rounded professionals. For example, on the technical side, we organised an in-house R-training curriculum, created and delivered by some of our own colleagues to the rest of the team. This helped us build a technical skill we could immediately put to use to do better work, and also built coaching and confidence skills for those who led the program. Another great example was of an external guest speaker series that we launched, which brought recognition to the team for bringing new insights to the company, and also helped the team gain experience in organising an event successfully end-to-end. Figure 4: Capability - Level 2: Upskilling the people analytics team (Source: Geetanjali Gamel) Capacity At first, capacity building measures may not sound like a natural fit with developing career paths. But it is impossible to focus on the next steps in one’s career if there is no time to step away from the daily barrage of activity to have a conversation; listen to a webinar; learn about a new project; or simply, chat with colleagues over lunch. As such creating capacity for the team is critical to allow them to develop their skillset to be more widely applicable, as well as to build the networks they need to find new opportunities. As mentioned before, our journey began with democratising data and providing a range of workforce metrics and even results of our enterprise voice survey in accessible cloud platforms to our HR community. We continue to supplement our efforts to empower our internal clients, and in the process unlock capacity for our team, by forming global communities of practice for analytics. Another effort to scale our analytics delivery and save precious time has been by finding opportunities to utilise process automation on repeatable tasks. It is impossible to focus on the next steps in one’s career if there is no time to step away from the daily barrage of activity Connectivity Despite efforts in building capability and reallocating capacity, there can’t be much career development if there is nowhere to go! This is when the third “C” of connectivity comes into play. In fact, it could just as easily be C for creativity, because we need a great deal of innovative thinking and risk taking to create opportunities where they don’t always exist. We started with small yet effective steps rather than trying to construct huge, formal programs. Connecting the people analytics team with other HR, data science, technology, and business professionals builds an awareness and appreciation for different types of work on both sides. We leveraged opportunities to co-create part-time assignments with other teams, participate in cross functional events, invite guest speakers to team meetings, and collaborate on projects to expose the team to other areas of analytical work. Connecting the people analytics team with other HR, data science, technology, and business professionals builds an awareness and appreciation for different types of work on both sides To create development assignments for the people analytics team we were creative and went with “quasi-experiments”. The first was an opportunity for a team member to take on the role of an HR business partner on a part-time basis for a few, smaller client groups. This gave the individual an opportunity to apply their analytical skillset to the role and get much greater exposure than before to business clients and business issues. Such an experiment has a multiplier effect. Where typically a business partner track is not easily available to a people analytics professional, creating such an opportunity internally can open up a new career path. Moreover, even if the individual does not end up pursuing this new career direction at the end of the experiment, it is still a valuable learning experience for them to be in the shoes of their internal client, i.e., the HR business partner. Finally, it may help to lay the foundation for what I like to call the HRBP 3.0 model. Where the original HRBP role had a heavy component of operational (and even transactional) work, the HRBP 2.0 model that many companies follow today aims at strategic business partners who enable key business decisions. The HRBP 3.0 model takes it a step further by envisioning an analytical HR business partner, who relies on both data driven insight and business acumen to support their client. Another “experiment” in creating new career opportunities was a mini-assignment we created for one of our people analytics team members to lead a large, remote team in the service delivery space. This was a completely different line of work from people analytics, and was heavily focused on operational and organisational skills like identifying and escalating issues on short deadlines, supplier relationship management, building relationships with a variety of HR and non HR stakeholders, and leading a service centre team to drive customer satisfaction. Clearly, this would not be a typical career path for a people analytics professional, but that is exactly why we need to be bold and creative with such experiments. This assignment not only exposed the individual to a different type and pace of work, but also gave them an opportunity to bring their analytical skills to the table to significantly elevate the usage and interpretation of transactional data. While many mature organisations have good-sized people analytics teams, there are still many where the teams are pretty lean. This model may work well for most purposes, but it usually limits the opportunities for team-members to have people management experience. This is not always necessary for upward mobility, but it many cases it is difficult to move upward without some kind of experience of leading a team. Keeping this in mind, we built more depth in our people analytics team, creating enterprise advanced people analytics and data visualisation and reporting sub-teams within the larger group, which are led by two of our team members. Taking a chance on subject matter experts and giving them the opportunity to lead and delegate not only helps to open up doors for them, it also gives them a chance to coach others on their team to be future experts and leaders. Lastly, we also created a new learning analytics role on our people analytics team which is a step toward building greater synergies between people analytics and learning practices, but also our small contribution in creating a new capability (and career path!) that is still evolving in many organisations.
    Future of Work
    2018年07月30日
  • Future of Work
    Workday、Ultimate、Slack的收购关注两因素:生产力和员工体验 文/JOSHBERSIN 如今,人工智能的收购已经很难跟上步伐,仅在2017年,就有超过108亿美元的资金投资于人工智能初创企业。在我所到之处,我发现软件公司都在开发更智能、更有预见性、更智能的工具。 在过去的几周里,我想提到的有三个重要的交易,每个都集中在一个主题上:使用人工智能和对话界面来改善员工体验,并对我们的生产力产生积极的影响。 太多的信息:工作效率正在下降  正如我在过去一年中所写的,生产率落后是一个经济问题,导致工资下降,很多人加班。如今,人们每天有35%的时间在阅读电子邮件,而我们在交流的新工具上花费过多。 我们对超连通职场的研究发现,平均每家公司都有7个不同的沟通系统,70%的高管预计会购买更多。技术供应商正以最快的速度发明它们。 Slack现在有800万用户,微软有20多万家公司使用团队,Facebook有3万家公司使用Workplace, Gmail上有12亿多用户,所有这些用户都可以使用Hangouts。在我们的消费者生活中,它甚至更容易让人分心:统计数据显示有15亿人使用WhatsApp, 13亿人使用Facebook messenger, 10亿人使用微信,3亿人使用Skype。  我们问人们这些新工具是否对他们的工作有帮助,超过三分之二的人告诉我们新工具正在阻碍我们。我们喜欢我们的个人工具,但我们花太多时间处理这些工具。一项相当惊人的研究发现,我们每6分钟检查一次这些系统,而我们40%的人在工作中从未有过30分钟不受干扰的时间。 这是荒谬的。我们的交流模式被打破了。为什么公司中的任何人都有机会在我们向他们发送电子邮件,给我们发送消息或在Slack上提及我们的时候分散我们的工作注意力? 这是不健康的。研究表明,为了应对这一冲击,压力会大幅增加。作为回应,我们现在正在购买“幸福解决方案”,为这个问题贴上“创可贴”的标签。当然,瑜伽、正念和冥想都很好——但真正的原因不正是效率低下的工作场所吗?  生产力成为人力资源的新主题  虽然我知道你们大多数人都有一个专注于“员工体验”的新项目,但我真的认为人力资源的新重点应该放在生产力上。生产力是健康、快乐和工作投入的关键,很多研究都支持这一点。 也许最令人信服的是特里萨·阿玛比尔的《进步原理》一书。通过对员工工作日志的分析,她令人信服地证明,工作中最令人愉快、最有价值的部分是“把事情做完”。所以,让我们把注意力集中在提高人们的工作效率上,我们将看到参与程度、幸福感以及其他衡量标准的提高。 当然,我们必须处理工作场所、管理实践、目标和奖励等问题,但最终如果我们想办法帮助人们完成他们的工作,所有这些项目都有更多的关注和价值。例如,如果你在管理一个研究部门,你的人才战略应该集中在帮助人们进行伟大研究的项目上。销售、市场营销和其他业务部门也是如此。 而这个问题,即简化工作的需要,正导致一些大型的人力资源技术并购。 Workday收购Stories.bi 我要强调的第一个是Workday收购一家名为Stories.bi的增强分析公司。 我刚刚看到这个系统的演示,它让我大吃一惊。 该公司使用人工智能监控和分析公司数据库(现在主要集中在Workday),以识别趋势,数据偏离范围,或与计划的差异。然后,它会用简单的英语(或其他语言)生成一个对话界面,指出它学到的东西。 这是一个例子: 正如你所看到的,这些小卡片准确地告诉你正在发生什么,你不需要进入一个电子表格,点击一个仪表盘,或者雇佣一个统计学家来弄清楚为什么一些商业指标没有朝着正确的方向发展。它是一个人工智能工具,叫做增强分析(Augmented Analytics),但实际上它是为了提高生产率。Workday计划将该系统整合到其平台和新的Workday Prism分析产品中,这将使我们的生活变得更加轻松。 我研究分析学已经有30年了,整个市场仍然是一个工具。虽然许多像Visier这样的高级供应商现在提供开箱即用的解决方案,但是它是像 Stories.bi这样的工具。这将使分析对每个人来说都很容易。我必须相信,这种增长将出现在我们的大多数人力资源产品中。 Slack收购使命 第二个我想指出的是Slack的使命收购,在Slack内部创造工作流程和“员工旅程”。 如果你接受这样一个事实:我们一半的生命都在这些消息平台上度过,为什么我们不利用它们来做更有意义的事情呢? 一群小型初创公司正在构建工具来阅读和解释你的信息,并发送提示、建议和培训提示,使你的工作生活更轻松。 (其中有一个叫迪斯科的,当你对某人说“谢谢”的时候,你会知道,并建议你把这些信息发送给他们的员工记录。) 刚刚获得的产品Slack是帮助人力资源部门(以及其他部门)在消息传递平台上构建员工体验的工具。这种类型的“嵌入式人力资源工作流”正变得非常流行(IBM的认知助手也这么做),而Slack现在正使其成为产品的一部分。 虽然大多数公司还没有把Slack作为企业范围的平台(微软、谷歌和Facebook也都想要这个市场),但我认为这个功能使这个目标更有可能实现。Slack现在被我们称为“员工体验平台”,是一个巨大的新兴快速发展的商业市场。(这里的领导者有ServiceNow、PeopleDoc、Salesforce等。) 在接下来的几个月里,我将会写更多关于这个领域的文章,但从某种意义上说,Slack刚刚“进入这个市场”。 这里的目标是生产力。我们不需要离开我们的“生产力系统”去完成我们的人力资源工作,这是市场上一个巨大的趋势。 Ultimate 收购PeopleDoc  我想指出的第三个交易是我的ERP朋友们正在关注的: Ultimate软件收购PeopleDoc,一个快速增长的员工体验平台。这家公司的总部设在法国,因此它为许多欧洲公司提供服务,在这些公司,单是雇佣合同的管理就令人头疼。 在过去几年,PeopleDoc发现了员工自助服务、案例管理、文档和服务管理软件(我称之为“员工体验平台”市场)的市场,他们开始疯狂扩张。(目前这个市场最大的玩家是ServiceNow,他们正在创造一个市场,随着时间的推移,这个市场可能会变成一个价值数十亿美元的市场。) 虽然我还没有关于Ultimate软件计划的任何细节,但我可以再次向您保证,这项交易也是出于提高生产率和员工工作经验的需要。Ultimate软件公司(Ultimate Software)是市场上管理最好的人力资源软件公司之一,最近收购了Kanjoya(一个基于人工智能(ai)的员工调查和参与工具),这正好符合该公司的战略。 关注人力资源技术的更多信息  秋天即将来临,所以我已经开始着手我的年度“人力资源技术中断”年度研究。我想指出的一个大主题是人力资源软件市场从“参与系统”到“生产力系统”的巨大转变。这三桩交易只是冰山的一角,在接下来的几个月里,我们将拭目以待。 以上内容由AI翻译,仅供参考 原文链接:https://joshbersin.com/2018/07/ultimate-workday-and-slack-acquisitions-focus-on-productivity-and-employee-experience/
    Future of Work
    2018年07月23日
  • Future of Work
    研究表明:平均知识工作者每隔6分钟“检查”电子邮件和IM 作者:JORY MACKAY  Jory MacKay是一名作家,内容营销人员和RescueTime博客的编辑。 以下翻译由HRTechChina AI完成,仅供参考交流! 没有必要使用电子邮件和其他通信工具来完成工作这一事实。尽管通过电子邮件取代了我们的生活,但这并不是浪费时间。 工作场所的沟通  很重要。 但是,如果我们不断将注意力转移到检查电子邮件或Slack消息上,我们就没有机会真正专注于我们最重要的工作。 这一点比最近仅通过电子邮件发现的研究更为明显,84%的用户始终在后台打开收件箱,70%的电子邮件在收到后6秒内打开。 我们显然有一个问题不断被纳入沟通工具。虽然我们在前一篇文章中探讨了多任务处理与通信工具的影响,但我们想要更好地了解这一级别的上下文切换在整个平均日期有多糟糕。 这是我们发现的: 平均知识工作者每隔6分钟“检查”电子邮件和IM 对于像知识工作者一样的作家,设计师,开发人员和项目经理 - 现代工作场所使用通信工具。我们的工作越来越依赖于更多的协作,更多的信息访问以及实时回答的更多问题。 然而,虽然通信工具非常适合我们快速访问我们需要的答案,但它们也是我们专注工作中断的持续来源。 但它有多糟糕? 当我们查看50,000多名RescueTime用户的匿名行为数据时,我们发现  普通知识工作者每隔6分钟就会通过通信工具“检入”。   (在这种情况下,“签入”定义为在处理其他生产任务时切换到通信工具的任何时间。) 所有这些都提出了一个巨大的问题:  当我们在回复电子邮件和消息之间只有几分钟时,我们如何期望完成重点工作? 简短的回答是我们不是。 事实上,虽然我们发现的中位数是6分钟,但完全崩溃看起来同样严峻。 我们发现,35.5%的员工每3分钟或更短时间检查一次他们的电子邮件和IM。虽然只有18.6%的人可以超过20分钟而没有进入沟通。 更糟糕的是,我们发现使用Slack的人 - 一种流行的团队沟通工具意味着减少电子邮件的使用 - 实际上更频繁地转向通信工具。而不是简化我们的通信时间,Slack用户平均在通信检查之间只花了5分钟,而非Slack用户可以花8分钟。 40%的知识型员工在一个工作日内从未获得30分钟的专注时间 但等一下。这些只是平均值。我们必须经常使用通信工具和其他长时间关注的工具,对吗? 不完全是。 我们都知道,要做到最好,我们需要长时间的专注时间。不幸的是,我们的数据显示我们很少有时间进行深入的工作。 事实上,我们的研究发现,普通知识工作者在大约40分钟的时间内最大限度地利用通信时间。 再次,这是中位数。当我们挖掘数字时,数据告诉了一个更大的故事。 17%的人甚至无法在没有沟通的情况下直接获得15分钟的专注时间。虽然只有30%的人每天花一小时的专注时间。 40%的知识型员工在工作日的重点时间从未超过30分钟。单击“推文” 如果您觉得自己在工作日努力寻找有吸引力的时间,那么您并不孤单。 All these “check-ins” turn your focus into Swiss cheese 我们认为这些数据清楚地描绘了现代工作场所中通信过载的程度。但要真正了解这些数据的含义,我们希望将其归结为个人层面。 使用RescueTime数据科学家Madison Lukaczyk自己的数据,我们发现在整整一周内,她只有8个半小时的高效工作块,而她没有通过电子邮件或IM登记: 为了获得更深入的外观,让我们放大其中一个小时块: 6月5日上午10点至11点,麦迪逊在12个5分钟的街区中的3个中切换到通信工具。这意味着在她工作的同时,  通讯工具在25小时内出现。 您可能只花费很短的总时间在电子邮件和即时消息上,但这些工具在半天内“存在” 您如何使用电子邮件和通信工具将取决于您的工作性质。但是,我们可能都同意,我们希望更多的时间不间断地集中精力并做有意义的工作。 事实上,在查看麦迪逊的数据时,我们发现虽然她只花了21 %的总时间用在电子邮件和即时通讯上,但这些工具在她44%的时间里都存在。 高出勤率并不一定意味着你被通信分心。但是,如果您的每日通信存在率远高于您在这些工具上花费的总时间,那么您可能会被他们分心。   现代工作场所充满了分心。而且我们不会通过不断检查我们的沟通工具来帮助自己。 虽然我们永远无法从工作日摆脱电子邮件和IM(并且不想)。了解它们的存在程度可以帮助我们更好地选择使用方式。 许多生产力专家建议在白天将通信批量分配到特定的块中。虽然其他人建议在你不太可能需要的时候(比如早上),在没有电子邮件或即时消息的情况下承诺一小时或更长时间的重点工作。 无论对你和你的角色有什么用处,可以肯定地说,在你何时以及如何使用通讯工具办理入手方面更有利于提高你的工作效率和专注力,而不是让他们每天花一半时间。 你是否觉得你很难在工作场所找到专注的时间而没有沟通?请在下面的评论或Twitter上告诉我们您对这些数据的看法。   原文链接:https://blog.rescuetime.com/communication-multitasking-switches/ 
    Future of Work
    2018年07月22日
  • Future of Work
    招聘自动化后,Sourcing工作将是人类的价值体现!--Sourcing Is the New Recruiting 文/Mike Wolford 我有个好消息要告诉你。Sourcing是今天人才招聘的好方法!传统上人们所熟知的招聘正在消失。越来越多的公司采用招聘过程自动化,这意味着即使对人才的需求增加,对传统全生命周期招聘的需求也会减少。面试和评估技术的改进将使公司能够自动完成大部分招聘人员目前所做的工作。 聊天机器人不仅能提高求职者的经验,还能提高进入我们各自的求职者跟踪系统的人数。自动面试将及时取代招聘人员筛选面试。自动评估和调度助理将从那里接管这一过程。这一变化将极大地减少填表时间,因为自动系统全天是可用的,原则上,候选人可以在几个小时内从应用程序转到安排面试。 对招聘人员来说,好消息是,只有最优秀的候选人才能以一种完全自动化的方式通过筛选过程。那些被认为65%匹配程度的候选人仍然需要人工审查。然而,即使在这些情况下,候选人和招聘人员之间也不太可能需要广泛的沟通。相反,招聘人员可能会检查一份文件,查看考试成绩,然后做出判断。2020年及以后的招聘人员将很少真正与应聘者交谈。 我能听到你在考虑我,迈克是一个源程序,而不是一个招聘人员,这对我来说意味着什么? 它意味着一些事情。首先,我们要做的是改变。在更高的层次上,源程序今天所做的是四个主要的活动。我们的工作是在面试过程中识别,参与,鉴定和提交候选人,否则他们将无法自行申请。 到2020年,源程序将主要集中在两项活动上。识别和参与。一旦招聘过程被自动化,就不需要招聘方来筛选候选人。面试过程将筛选候选人,而源程序不会向招聘人员或招聘经理提交候选人,他们会直接将候选人引入管道,开始评估,最有可能的方式是参加某种形式的视频面试。今年早些时候,在拉斯维加斯的SourceCon网站上,格伦·卡西(Glen Cathey)说得既准确又有预言性,“sourcing101是销售101。” 未来的源程序人员需要成为优秀的销售人员,因为他们的主要工作将是识别高潜力人才,并邀请他们进入自动招聘流程。对我们来说,在程序方面,特别是在IT程序方面,市场的声音将会更大。这意味着,参与将变得更具挑战性和关键。2020年的目标不仅是成为一个有说服力的、坚持不懈的销售人员,而且是一个出色的营销人员。采购和招聘营销将融合成一种新的、强有力的组合。 人们很容易忽视人工智能对其他行业的影响,但忽视人工智能正在改变市场营销的方式,以及这种改变将如何影响整个招聘,是不明智的。例如,Facebook最近之所以成为新闻,是因为它们对我们这个时代的政治产生了一定的影响。不管你的政治观点如何,这对我们大家都是一个教训。有针对性的社交媒体和聊天机器人在广告和参与方面的应用是强大而有效的。既然人工智能正在被应用于市场营销,那么人才收购进入这种广告渠道只是时间问题。有智慧的人会看到后职和祈祷正在被目标和参与所取代。 有时很难看到森林中的树木,但我记得我从经济学中学到了这一课。20世纪的定义是大规模生产。21世纪将由大规模定制所定义。这一说法对源程序有一定的影响。 一些公司已经意识到这一点,并采取了复杂的营销活动,但这只是一个例外,而不是规则。原因有很多,但我在这里想告诉你的是,一旦实际的申请和面试过程基本上是自动化的,公司将有时间和资源来集中精力把目标申请者填满职位空缺。作为一个销售人员,这意味着你不仅需要学习如何销售,还需要理解如何像营销人员一样思考。 作为一个源程序,我相信这对你来说意味着什么。今天,我们确认、参与、资格和提交。我们与招聘伙伴密切合作,有时还会与招聘经理合作。在未来,源程序将首先确定潜在人才的目标市场。从那时起,参与就变成了一种双管齐下的方式。 作为长期战略的一部分,招聘营销人员将负责建立品牌并将EVP销售到目标市场。他们将为目标市场提供令人兴奋和引人入胜的内容,而源程序将与这些目标市场中的特定个人进行接触,并邀请他们应用于特定的角色。当前的招聘人员/源程序伙伴关系将会及时被招聘市场/源程序的关系所取代。 我们源程序的底线是。我们的工作正在发生变化,但在所有与人才获取有关的专业人士中,我们的工作最有可能出现显著增长。 我给你的建议是完善你的布尔值,并挑选一些关于销售和数字营销的书籍。如果今天你是招聘人员,我的建议是训练你的采购技能或开始思考另一条线的工作,因为在过程自动化变成了标准的50% +全生命周期的工作正在消失,“招聘”将在很大程度上成为另一个人力资源管理功能。 以上内容由AI翻译,仅供参考 原文链接:https://www.sourcecon.com/sourcing-is-the-new-recruiting/
    Future of Work
    2018年07月19日
  • Future of Work
    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?
    Future of Work
    2018年07月17日
  • Future of Work
    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.
    Future of Work
    2018年07月15日
  • Future of Work
    7个精彩的员工内推计划示例 员工推荐 - 以及员工推荐计划 - 正在增加。并且有充分的理由:提到新员工往往更好(文化),他们更积极,更不容易离开,更有成效。在本文中,我们将介绍7个员工推荐计划示例。   在创建卓越的员工推荐计划时,需要记住一些要素。理想情况下,每个计划包括: 奖励 - 现金或非现金,如额外假期,甚至只是一个简单的谢谢。 易于使用 - 尽量让您的员工尽可能轻松地使用您的员工推荐计划。 反馈 - 一如既往,让您的员工了解他们的推荐状态。他们的候选人是否在招聘过程中取得了进展?给他们一个快速的电子邮件或消息。 认可 - 赞美有利于员工士气。想想一种很好的方式,可以让员工在成功转介某人时获得应有的认可。   那里有很多员工推荐计划的好例子。下面,您将找到各种各样的程序,以便您可以看到不同的可能性,并将灵感用于您自己的程序。   7个精彩的员工推荐计划示例 1.纯粹 PURE(一家美国财产保险公司)的推荐率很高:其员工的40%至60%来自转介。   他们的秘密?   不要浪费任何时间。一旦新员工在公司的第一个星期完成了这项工作,他们就会被问到是否知道其他任何能够适合公司的人。   这种切入追逐推荐策略有两个主要好处:1)它立即导致更多推荐; 2)它强调推荐的重要性。   2. Salesforce的欢乐时光 美国云计算巨头Salesforce以其丰厚的奖励而闻名 - 无论是货币奖励还是其他奖励 - 它为员工提供奖励。而且不仅仅是顺便推荐成功; Salesforce员工可以随意使用思维室,在社区中获得志愿者的报酬,当他们在没有晋升的情况下离开18个月时,他们会帮助找到新的挑战。   推荐方面,Salesforce员工已获得总计不低于550万美元的推荐奖金。   但Salesforce如何进行推荐计划?   该公司组织招聘欢乐时光,聚会,员工可以邀请他们想要推荐的人。这是招聘人员熟悉潜在候选人的一种非常非正式的方式。因为他们可以一起享用一些饮料,所以员工和他们的推荐都很有趣。   3. InMobi的自行车 当InMobi--一个全球移动广告和发现平台 - 迫切需要工程经理时,其招聘团队知道他们必须想出一些非凡的东西。   所以他们做到了。   他们想出了一个国家和文化特定的奖励员工奖励。在印度,这意味着皇家恩菲尔德自行车(印度的哈雷戴维森)和在美国意味着Vespa。   为了让员工了解他们的推荐游戏,两辆车都停在了办公室的入口处。每次成功推荐,InMobi的员工都可以选择全新的自行车和巴厘岛之旅。   事实证明,定制您的员工推荐计划需要一些本地知识:InMobi的推荐率从一个900人的公司的20%上升到50%。   4. Fiverr的游戏化方法 自由职业市场Fiverr希望通过跟踪社交工作分享并通过提供分享工作和推荐朋友的积分来增加游戏化来增加员工推荐。   因此,Fiverr决定使用Zao,一家提供员工推荐计划的公司。该软件为推荐候选人添加了一个竞争元素,因为它为员工提供了他们所采取的所有行动的积分和信誉 - 让我们不要忘记这一点 - 它会通知他们他们推荐的状态变化。   根据他们在Zao推荐排行榜上获得的积分,顶级Fiverr员工每季度和每年都会收到礼物。   Zao的排行榜 为每位与推荐相关的行动提供员工信誉。   5.谷歌的尖锐问题 谈到员工推荐计划的例子,这个例子精美地展示了简约的力量。在谷歌,招聘人员所做的一件事就是向员工询问诸如“谁是你在波士顿认识的最好的软件开发人员?”之类的问题。   提出这种尖锐的问题会自动推动人们对他们所指的人更难思考。   6.埃森哲的情感奖励 荷兰咨询和IT公司埃森哲采用了不同的方法。该公司的员工推荐计划是基于这样一个事实:推荐人让你感觉良好; 或者是因为你帮助了一个朋友,让他或她找到了一份工作和/或因为你找到了一位出色的新员工来帮助公司。   为了最大化这种“做得好”的感觉,埃森哲让员工有可能将他们推荐奖金的一部分捐赠给他们选择的慈善机构。最重要的是,公司匹配了这个数额。   7.英特尔的双倍奖金 英特尔找到了一种简单的方法,一举击中两只众所周知的鸟类。为了在多元化招聘中保持领先地位,当员工成功转介女性和少数族裔时,这家美国科技巨头将其推荐奖金加倍。   有创意 那么,你有它,7个精彩的员工推荐计划的例子。   当然,还有无数其他精彩的节目,但这个选择应该让你对你拥有的无限可能性有一个公平的想法。本文的三个主要内容是: 在涉及到员工推荐计划时要具有创造性 - 您不一定需要大预算才能产生影响(尽管金钱奖励当然有帮助)。 为您的员工(全公司)提供认可。 让他们了解他们推荐的状态。   与许多事情一样,没有保证成功的神奇公式。其中一部分只是反复试验,以便找出在您的组织和员工中运作良好的方法。通过测试各种推荐方法,您可以淘汰那些无法获得理想结果的方法,从而更接近适合该法案的员工推荐计划。   作者:  Neelie Verlinden  Neelie是Digital HR Tech的联合创始人兼主编。她是一位经验丰富的自由撰稿人,具有国际背景,并撰写了大量关于人力资源技术的文章。   以上由AI翻译完成,仅供参考!
    Future of Work
    2018年07月14日
  • Future of Work
    人力资本分析:组织网络分析和未来的工作 来源/trustsphere.com 文/Antony Ebelle 简介 在当今的超级协作组织中,经常被忽视但重要的洞察力来源是员工培养获取知识,分享信息,创新和创造工作价值的无数关系网络。 这些协作网络也被称为组织的社会资本,对于工作实际完成,企业成长和人员成功至关重要。这意味着,通过将网络维度应用于组织内的不同员工组,HR可以以更相关的方式为企业生产力,人才实践和组织效率做出贡献。 ONA如何衡量社会资本 传统上,人力资源数据侧重于人力资本方面,如员工人口统计,资格,经验和技能,并且始终只在个人层面进行衡量。 图一、完美组合 社会资本由员工在团队中,整个组织内部以及与外部各方在工作过程中发展的非正式关系组成。 事实上,正是通过这些关系和网络他们的大部分工作实际上都已完成。 在今天的网络化团队组织中,社会资本比以往任何时候都更加重要。 除了在个人层面进行测量外,还可以针对团队和部门进行测量。 组织中的社会资本表现在六种不同类型的关系网络中: 工作网络:员工与他们交流信息,作为日常工作的一部分。 创新网络:员工与谁合作或启动新想法。 社交网络:与员工建立友好关系,无论在工作或下班,知道员工发生了何种事情。 学习网络:员工与谁合作改进现有流程或方法。 专业网络:员工转向专业化或就工作相关问题提供建议。 战略网络:员工向谁寻求未来的建议。 社会资本的资源可以通过个人和商业网络获得。 社会资本的有效性取决于这些个人和商业网络的规模,质量和多样性,如上面六个确定的组成部分所述。但除此之外,如果您通过网络与他们间接联系,社会资本还取决于您不知道的人。 证据表明在商业环境中,社会资本显著提高了生产力,效率和绩效。与不能或不愿释放社会资本力量的同行相比,建立和使用社会资本的个人获得更好的工作,更好的薪酬,更快的晋升,并且更有影响力和有效性。 组织也是如此。 以下案例研究是社会资本如何增加价值的例子。 它是如何应用的? 识别HiPo员工 有效的高潜力(HiPo)开发计划的好处是众所周知的,很明显,识别HiPos对于组织的持续成功至关重要。 证据令人信服: HiPos比普通员工多贡献了21%的工作量,比核心员工产生的价值高出91%,并且作为未来领导者的成功率是其三倍。 然而,识别和保留HiPos的难度也很复杂: 排名前四分之一的员工中只有29%是HiPo员工。 25%的HiPos计划在明年内离开公司,而另外75%的HiPos比其他员工离开的可能性高10%。 HiPo员工的愿望,能力和敬业度都很少见。 这些概念是无形的,难以可视化。 因此,在传统的过程中无法识别和量化它们。 绩效评估: 协作:他们知道信息所在的位置,并可以与其他人一起访问。 能量:HiPos一直有动力改善自己,其他人则受其影响精力充沛。 勇气:冒险是他们节奏的自然组成部分,他们不怕面对艰难的挑战。 生产力:他们在更短的时间内完成更多的工作。 影响:HiPos可以通过让人们喜欢的方式与其他人交谈,从而提高社交网络技能。 许多HiPo特征都存在于员工的社会资本中,通过测量和分析网络行为,ONA使组织能够发现这些特征并识别HiPo员工。 必须指出的是,这种衡量只能通过社会资本视角来实现。 要得到应该考虑到员工的全貌,传统的人力资本措施和社会资本措施。 ONA可以根据经验测量以下HiPo特性: 网络关系和力量: 员工与同事之间的关系有多强? 这些关系越强烈,价值创造的越好。 网络覆盖面: 员工的网络覆盖范围有多广,他们与同龄人,高级和初级员工,不同团队,部门和不同物理位置的关系如何? 影响力: HiPos对于信息流的影响至关重要。组织,通常是将网络连接在一起的关键连接器。这标识了可以推动和支持组织变革,创新和专业知识共享的关键员工。 图2、HiPos构建40%以上与普通员工的关系 协作:员工之间的关系和网络证明了他们在团队内部以及其他团队和部门中施加影响的能力。 测量完成后,可以通过两种方式使用此HiPo数据: 1、测量当前HiPo库的质量 现有HiPo库员工的社会资本可用于验证其社会资本的强度,并在验证库的质量时,为未来的HiPo库成员建立基准和目标。 2、通过组织识别隐藏的人才 通过使用社会资本数据开始识别过程,可以以无偏见和标准化的方式选择候选人,从组织中的任何地方凭经验识别具有高水平社会资本的员工,并通过偏见和弱流程减少问题,这意味着许多HiPo 库的潜力并不高。 结论 随着工作的未来变得更加协作,组织转向敏捷团队网络,了解个人和团队如何工作和建立关系的能力对于业务成功变得越来越重要。 ONA利用实时社会资本洞察力增强传统的人力资本数据,使前瞻性组织能够支持数据驱动的决策,减少无意识的偏见,加强各种人力资源流程,并为企业提供基于证据的观察 增强本能,实现更高绩效,更具包容性和更高效的组织。 以上内容由HR Tech China AI翻译,仅供参考。
    Future of Work
    2018年07月10日
  • Future of Work
    Google Hire重大更新!全面AI技术支持,简历筛选安排面试将大幅节约时间 综合来源/ gadgets google hire blog等 更新要点 Google Hire通过更新获得了新的AI驱动的工具 Google Hire可以更快地安排面试,并在简历中突出显示关键字 雇用1000人以下的美国企业适用Google Hire 随着去年推出Google Hire,Google通过将招聘过程整合到招聘人员,已经花费大量时间去查工具(如Gmail,Google日历和其他G-Suite应用程序),来简化招聘流程。旨在帮助中小型企业有效招聘。招聘人员表示,Hire从根本上改善了他们的工作方式,减少了应用程序之间的上下文切换。 实际上,当他们衡量用户活动时,他们发现Hire减少了完成日常招聘任务的时间 - 比如审查应用程序或安排面试 - 节省时间高达84%。     Google启动AI 通过整合Google AI,Hire现在可以减少重复耗时的任务,如安排面试,进入一键式交互。 这意味着招聘团队可以在后勤上花费更少的时间,更多的时间与人交流。 Hire中的新功能使招聘人员可以做到如下几点:   在几秒钟内安排面试: 招聘人员和招聘协调员花费大量时间在后勤管理 - 查找日历上的可用时间,预订房间,并将正确的信息汇集到预备面试官处。为了简化这一过程,Hire现在使用AI来自动建议面试者和理想时间段,从而将面试计划减少到几次点击。 通过整合Google AI,Hire现在可以将重复耗时的任务减少为一键互动。这意味着招聘团队可以在后勤上花费更少的时间,更多的时间与人交流” 谷歌在其博客文章中表示。 自推出以来,Google Hire带有G Suite集成功能,可让应用程序与Gmail和Google日历等其他应用程序同步工作。Google声称Hire可以减少招聘团队招募任务的时间达84%。 最新的更新基本上整合了Google AI,以减少做任务时的点击次数,让AI建议发挥作用。 Google Hire自动提供面试官和理想时间段,将面试安排减少到几次点击。操作如下: Photo: Google 它试图减少手工查看日历空闲时间,为您查看并提供理想的时间段。此外,如果面试官最后一分钟取消,Hire不只是提醒你,它还推荐可用的面试官,并可以很容易且快速地邀请面试官。 所以我们可以看到国内外面试安排都是一个复杂而且繁琐的事情,面试管理这块的需求也日益突出。   自动突出显示简历重点 相当一部分招聘人员的时间花在审查简历上(我们都知道这一点)。有人告诉我,当团队正在观看与Hire进行互动的人时,他们发现客户经常使用“Ctrl + F”,通过简历扫描搜索正确的面试者的技能 - 这是一项重复的手动任务,可以轻松实现自动化。 另一个常见的招聘难题是在简历中查找关键字。 Hire的AI现在通过分析工作岗位描述,或搜索查询术语并在简历中突出显示相关单词(包括同义词和缩略词)来节省手动搜索它们的时间,自动为招聘人员找到这些单词。 Photo: Google   点击致电候选人: 无论他们是筛选候选人,进行面试还是跟进录用信,招聘人员每天都会有数十次电话交谈。现在通过点击通话功能简化每个电话对话,并自动记录通话,以便团队成员知道与候选人通话的人员。它是如何工作的,Derek? 很高兴你问这样的问题! 系统会拨打您要给求职者的电话,然后当您拿起电话时,系统会向求职者拨打该号码。且您永远不会丢失您的收件箱内容,电话会录音,并且您可以在电话中记笔记。我问是否有发信息功能,市场表明,大约98%的人回复短信,很少听到语音信箱或回复他们不认识的号码。 他们向我保证,这个过程非常简单,并且您电话辛苦获取的宝贵数据将会轻松转移。   最后,现在通过点击通话功能简化每个电话对话,并自动记录通话,以便团队成员知道谁已经与候选人通话,而不是多次拨打同一个候选人。 所有那些雇员不足1000人的美国企业都可以购买Hire服务。在中国不行~~     关于Google Hire 从去年7月推出,旨在帮助中小型企业有效招聘。它允许招聘人员将工作发布到多个工作现场,跟踪申请,安排面试,甚至可以在一个平台上获得面试反馈。现在,在一年之后,谷歌已经更新了招聘人工智能驱动工具,以实现“更聪明,更快速的招聘方式”。此更新带来的新功能可以加快日程安排访问速度,为日志记录提供简单的工作,并简化相关简历,从而减少耗时。 “通过整合谷歌AI,服务现在减少重复,耗时的任务,进入一键式的互动。这意味着雇佣团队可以花费更少的时间与物流和更多的时间与人联系” 以上由HRTechChina 综合编译,仅供参考!  
    Future of Work
    2018年06月27日
  • Future of Work
    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
    Future of Work
    2018年06月27日