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.
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?
People Analytics
2018年07月17日
People Analytics
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.
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
People Analytics
扫一扫 加微信
hrtechchina
