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！
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2018年06月27日
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10 Trends in Workforce Analytics （英文）
Workforce analytics is developing and maturing. These are the 10 major trends for the near future.
1. From one time to real-time
Many workforce analytics efforts start as a consultancy project. A question is formulated (“How do our employees experience their journey?”), many people are interviewed, data is gathered, and with the help of the external consultants a nice report is written and many follow up projects to redesign the employee journey are defined.
A one-time effort is nice, but it might be more beneficial to develop ways to gather more regularly and maybe even real-time feedback from candidates, employees and other relevant groups.
The survey practice is changing. We see organizations using several approaches:
The classic annual or bi-annual employee survey, for a deep dive.
Weekly, monthly or quarterly pulse surveys to gather more frequent feedback. A few questions, often varying the questions per cycle. Some more advanced pulse survey solutions are adaptive: they ask more questions to people when they sense there are issues (“How was your week?”. If the answer is “Very Good”, the survey is finished, if you answer, “Not so good”, there are some follow-up questions). Pulse surveys can also be easily connected to the important “moments that matter” for the employee experience.
Continuous real-time mood measurement. Innovative solutions in this area are still scarce, especially if you want to measure in a passive non-obtrusive way. Keencorp is an example, they analyze aggregated e-mails and can report on the mood (and risks) in different parts of an organization.
In my article Employee mood measurement trends, you can find an extensive overview of mood measurement providers.
2. From people analytics to workforce analytics
Currently, the general opinion seems to be that people analytics is a better label than HR analytics.
Increasingly the workforce is consisting of more than just people. Robots and chatbots are entering the workforce. The first legal discussions have started: who is responsible for the acts of the robots?
If we’re also analyzing robots, we’re moving from people analytics towards workforce analytics. Robot wellbeing and robot productivity is a nice domain for HR to claim.
3. More transparency
This overview of workforce analytics trends cannot be complete without a reference to GDPR. GDPR is fueling a lot of positive developments, one of them being a lot more transparency. About what kind of data is collected, how it is used, and how algorithms are used to make decisions about people.
The issue of data ownership is related. It is expected that employees will no longer accept that they cannot own their own personal data. Employees need to have the possibility to show their data to their potential next employer as evidence for their productivity and engagement.
4. More focus on productivity
In the last years, there has not been a lot of focus on productivity. We see a slow change at the horizon.
Traditionally, capacity problems have been solved by recruiting new people. This has led to several problems. I have seen this several times in fast growing scale-ups.
As the growth is limited by the ability the find new people, the selection criteria are (often unconsciously) lowered, as many people are needed fast. These new people are not as productive as the existing crew. Because you have more people, you need more managers. Lower quality people and more managers lowers productivity.
Another approach is, to focus more on increasing the productivity of the existing employees, instead of hiring additional staff, and on improving the selection criteria.
Using workforce analytics, you can try to find the characteristics of top performing people and teams, and the conditions that facilitate top performance.
These findings can be used to increase productivity and to select candidates that have the characteristics of top performers. When productivity increases, you need less people to deliver the same results.
A related read on this topic are the 3 reasons to stop counting heads.
5. What is in it for me?
A lack of trust can influence many workforce analytics efforts. If the focus is primarily on efficiency and control, employees will doubt if there are any benefits for them.
Overall there is a shift to more employee-centric organizations, although sometimes you can doubt how genuine the efforts are to improve the employee experience.
Asking the question: “How will the employees benefit from this effort?” is a good starting point for most workforce analytics projects. It also helps to create buy-in, which becomes increasingly important with the introduction of the GPDR.
6. From individuals to teams to networks
Many workforce analytics projects today are still focused on individuals. What are the characteristics of our top performers? How can we measure the individual employee experience? How can we decrease absenteeism?
Earlier, I gave an overview to what extend current HR practices are focused on teams.
As you can see in the table, most of the practices are still very focused on the individual. Workforce analytics can help to improve the way teams and networks function in and across organizations. The rise of Organizational Network Analysis is one of the promising signs.
7. Cracks in the top-down approach
The tendency to implement changes top-down, is still common.
We like uniformity and standardization. In our central control room, we look at our dashboard, and we know we need to act when the lights are turning from green to orange.
HR finds it difficult to approach issues in a different way. Performance management is a good example. Changing the performance management process is often tackled as an organization-wide issue, and HR needs to find the new uniform solution.
In line with the trend called “the consumerization of HR”, employees are expected to take more initiative. Employees are increasingly tired of waiting for the organization and HR, and want to be more independent of organizational initiatives.
If you want feedback, you can easily organize it yourself, for example with the Slack plug-in Captain Feedback. A simple survey to measure the mood in your team is quickly built with Polly (view: “How to measure the mood in your team with Slack and Polly“). Many employees are already tracking their own fitness with trackers like Fitbit and the Apple Watch.
Many teams primarily use communication tools as WhatsApp and Slack, avoiding the officially approved communication channels. HR might go with the flow, and tap on to the channels used, instead of trying to promote standardized and approved channels.
How can workforce analytics benefit from the data gathered by on their employee’s own devices? If it is clear, what the benefits are for employees to share their data, they might be able to help to enrich the data sets and improve the quality of workforce analytics.
8. Ignoring the learning curve
In their book “Making HR measurement strategic”, Wayne Cascio and John Boudreau presented an often-quoted picture, with the title “Hitting the “Wall” in HR measurement”. The wall was the wall between descriptive and predictive analytics.
There are many more overviews with the people analytics maturity levels. Generally, the highest level is predictive analytics.
Patrick Coolen of ABN AMRO Bank recently mentioned a next level: continuous analytics, and he introduced a second wall, the wall between predictive analytics and continuous analytics.
As predictive analytics seems to be the holy grail, many HR teams want to jump immediately to this level. Let’s skip operational reporting, advanced reporting and strategic analytics. We can leapfrog, ignore the learning curve, and jump to the highest level in one step.
For many teams, ignoring the learning curve does not seem to be a sensible strategy. Maybe it is better to learn walking before you start running.
9. Give us back our time!
Recently I spoke to HR professionals from big multinationals who were involved in a “Give us back our time” projects.
In their organizations, the assignment to all staff groups was: stop using (meant was: wasting) more and more time of the employees and managers, please give us some time back!
An example that was mentioned concerned performance management. In this organization, they calculated that all the work around the performance management process for one employee costed manager and employee around 10 hours (preparation, two formal meetings per year, completing the online forms, meeting with HR to review the results etc.).
By simplifying the process (no mandatory meetings, no forms, no review meetings, just one annual rating to be submitted per employee by the manager), HR could give back many hours to the organization – to the relief of both managers and employees.
Big HR systems generally promise a lot. But before the system can live up to the high expectations, a lot of work needs to be done. Data fields must be defined. Global processes must be standardized. Heritage systems must be dismantled.
This results in a lot of work (and agony), for employees, for managers, for HR and for the implementation partners (who do not mind).
Workforce analytics can help a lot in the “give-us-time-back” projects, for example by some simple time-measurement. Measure the time a sample of managers, employees, and HR professionals spend on different activities, and estimate the value these activities optimizes the core activities of the organization (e.g. serving clients and bringing in new clients).
10. Too high expectations
The expectations of workforce analytics are often too high. Two elements must be considered.
In the first place, human behavior is not so easy to predict, even if you have access to loads of people data.
Even in domains where good performance is very well defined and where a lot of data is gathered inside and outside the field, as for example in football, it is very difficult to predict the future success of young players.
Secondly, the question is to what extend managers, employees and HR professionals behave in a rational way. All humans are prone to cognitive biases, that influence the way they interpret the outcomes of workforce analytics projects. Some interesting articles on this subject are why psychological knowledge is essential to success with people analytics, by Morten Kamp Andersen, and The psychology of people analytics, written by myself.
A more general thought: what if you replaced ‘Workforce analytics’ with ‘Science’? What is the role of science in HR? The puzzle is, that there are many scientific findings that have been available for a long time but that are hardly used in organizations.
Example: it has been proven repeatedly, that the (unstructured) interview is a very poor selection instrument.
But still, most organizations still rely heavily on this instrument (as people tend to overestimate their own capabilities). Why would organizations rely on the outcomes of workforce analytics, when they hardly use scientific findings in the people domain?
An interesting presentation on this topic that I recommend is by Rob Briner, titled evidence-based HR, what is it and is it really happening?
There’s a lot that’s changing in the world of work. These are the 10 trends in workforce analytics that I’m seeing today and that will likely impact the way we work in the near future.
This article is based on a keynote I gave at the Workforce Analytics Forum in Frankfurt, Germany, on April 18, 2018.
by Tom Haak
Tom Haak is the director of the HR Trend Institute The HR (Human Resources) Trend Institute follows, detects and encourages trends. In the people and organization domain and in related areas. Where possible, the institute is also a trend setter. Tom has an extensive experience in HR Management in multinational companies. He worked in senior HR positions at Fugro, Arcadis, Aon, KPMG and Philips Electronics. He holds a master’s degree in Psychology. Tom has a keen interest in innovative HR, HR tech and how organizations can benefit from trend shifts. Twitter: @tomwhaak
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