创新:背调公司Checkr创建动态背调监控工具以提升Uber乘坐的安全性编者注:值得学习和参考,动态的背景调查很重要啊!国内哪家可以跟滴滴等合作起来!
目前背调都是截止调查的当天。而入职或者开始工作后的情况就很难掌握了!
现代和合规背景调查的领先提供商Checkr今天宣布了一项新技术,该技术可持续更新可能影响共乘驾驶员驾驶资格的犯罪记录。Checker Continuous Check由Uber设计,动态识别可能不合格的记录,以帮助确保驾驶员继续满足优步的安全标准。
Checkr首席执行官Daniel Yanisse表示: “ 凭借当今的按需劳动力,我们需要超越静态背景报告,进行动态筛选。通过持续检查,Checkr为共乘产业创造了新的安全标准将提供关于某人背景变化的重要见解,这可能会影响他们的工作资格。“
优步是第一家采用该技术的公司。使用涵盖大多数新刑事犯罪的数据来源,当司机参与犯罪活动时,持续检查会向优步提供通知。然后,优步可以调查任何可能不合格的信息,例如DUI的新费用和未决费用,以确定该驱动程序是否仍有资格与Uber一起驾驶。这项新技术使优步能够在每年重新进行背景调查之间持续执行其安全标准。
“ 安全对优步至关重要,我们希望确保驾驶员持续不断地达到我们的标准,”优步安全与保险副总裁Gus Fuldner说。“ 这种新的连续检查技术将加强我们的筛选过程并提高安全性。”
最初设计用于满足共乘行业的严格要求,2018年秋季将为所有Checkr客户提供持续检查。
关于Checkr
Checkr的使命是通过提高对过去的理解来建立更公平的未来。我们的平台使数以千计的客户每年能够以gig经济的速度轻松雇用数百万人。使用Checkr先进的背景调查技术,各种规模的公司都能更好地了解不断变化的员工队伍的动态,为他们的招聘带来透明度和公平性,最终为员工创造更美好的未来。
Checkr Creates Dynamic Monitoring Tool to Elevate Safety in Ridesharing
Checkr, the leading provider of modern and compliant background checks, today announced new technology that provides continuous updates about criminal records that may affect ridesharing drivers’ eligibility to drive. Checkr Continuous Check, which was designed with Uber, dynamically identifies potentially disqualifying records to help ensure drivers continue to meet Uber’s safety standards.
“With today's on-demand workforce, there's a need to move beyond static background reports to dynamic screenings," said Daniel Yanisse, CEO of Checkr. "Through Continuous Check, Checkr is creating a new standard of safety for the ridesharing industry and beyond that will provide critical insight into changes in someone's background that may affect their eligibility to work."
Uber is the first company to adopt the technology. Using data sources that cover most new criminal offenses, Continuous Check provides notifications to Uber when a driver is involved in criminal activity. Uber can then investigate any potentially disqualifying information, such as a new and pending charge for a DUI, to determine whether the driver is still eligible to drive with Uber. This new technology allows Uber to continuously enforce its safety standards between annual reruns of background checks.
“Safety is essential to Uber and we want to ensure drivers continue to meet our standards on an ongoing basis,” said Gus Fuldner, Vice President of Safety and Insurance at Uber. “This new continuous checking technology will strengthen our screening process and improve safety.”
Designed initially to meet the stringent requirements of the ridesharing industry, Continuous Check will be available to all Checkr customers in Fall 2018.
About Checkr
Checkr’s mission is to build a fairer future by improving understanding of the past. Our platform makes it easy for thousands of customers to hire millions of people every year at the speed of the gig economy. Using Checkr’s advanced background check technology, companies of all sizes can better understand the dynamics of the changing workforce, bring transparency and fairness to their hiring, and ultimately build a better future for workers. For more information please visit: www.checkr.com.
10 Trends in Workforce Analytics (英文)
Workforce analytics is developing and maturing. These are the 10 major trends for the near future.
1. From one time to real-time
Many workforce analytics efforts start as a consultancy project. A question is formulated (“How do our employees experience their journey?”), many people are interviewed, data is gathered, and with the help of the external consultants a nice report is written and many follow up projects to redesign the employee journey are defined.
A one-time effort is nice, but it might be more beneficial to develop ways to gather more regularly and maybe even real-time feedback from candidates, employees and other relevant groups.
The survey practice is changing. We see organizations using several approaches:
The classic annual or bi-annual employee survey, for a deep dive.
Weekly, monthly or quarterly pulse surveys to gather more frequent feedback. A few questions, often varying the questions per cycle. Some more advanced pulse survey solutions are adaptive: they ask more questions to people when they sense there are issues (“How was your week?”. If the answer is “Very Good”, the survey is finished, if you answer, “Not so good”, there are some follow-up questions). Pulse surveys can also be easily connected to the important “moments that matter” for the employee experience.
Continuous real-time mood measurement. Innovative solutions in this area are still scarce, especially if you want to measure in a passive non-obtrusive way. Keencorp is an example, they analyze aggregated e-mails and can report on the mood (and risks) in different parts of an organization.
In my article Employee mood measurement trends, you can find an extensive overview of mood measurement providers.
2. From people analytics to workforce analytics
Currently, the general opinion seems to be that people analytics is a better label than HR analytics.
Increasingly the workforce is consisting of more than just people. Robots and chatbots are entering the workforce. The first legal discussions have started: who is responsible for the acts of the robots?
If we’re also analyzing robots, we’re moving from people analytics towards workforce analytics. Robot wellbeing and robot productivity is a nice domain for HR to claim.
3. More transparency
This overview of workforce analytics trends cannot be complete without a reference to GDPR. GDPR is fueling a lot of positive developments, one of them being a lot more transparency. About what kind of data is collected, how it is used, and how algorithms are used to make decisions about people.
The issue of data ownership is related. It is expected that employees will no longer accept that they cannot own their own personal data. Employees need to have the possibility to show their data to their potential next employer as evidence for their productivity and engagement.
4. More focus on productivity
In the last years, there has not been a lot of focus on productivity. We see a slow change at the horizon.
Traditionally, capacity problems have been solved by recruiting new people. This has led to several problems. I have seen this several times in fast growing scale-ups.
As the growth is limited by the ability the find new people, the selection criteria are (often unconsciously) lowered, as many people are needed fast. These new people are not as productive as the existing crew. Because you have more people, you need more managers. Lower quality people and more managers lowers productivity.
Another approach is, to focus more on increasing the productivity of the existing employees, instead of hiring additional staff, and on improving the selection criteria.
Using workforce analytics, you can try to find the characteristics of top performing people and teams, and the conditions that facilitate top performance.
These findings can be used to increase productivity and to select candidates that have the characteristics of top performers. When productivity increases, you need less people to deliver the same results.
A related read on this topic are the 3 reasons to stop counting heads.
5. What is in it for me?
A lack of trust can influence many workforce analytics efforts. If the focus is primarily on efficiency and control, employees will doubt if there are any benefits for them.
Overall there is a shift to more employee-centric organizations, although sometimes you can doubt how genuine the efforts are to improve the employee experience.
Asking the question: “How will the employees benefit from this effort?” is a good starting point for most workforce analytics projects. It also helps to create buy-in, which becomes increasingly important with the introduction of the GPDR.
6. From individuals to teams to networks
Many workforce analytics projects today are still focused on individuals. What are the characteristics of our top performers? How can we measure the individual employee experience? How can we decrease absenteeism?
Earlier, I gave an overview to what extend current HR practices are focused on teams.
As you can see in the table, most of the practices are still very focused on the individual. Workforce analytics can help to improve the way teams and networks function in and across organizations. The rise of Organizational Network Analysis is one of the promising signs.
7. Cracks in the top-down approach
The tendency to implement changes top-down, is still common.
We like uniformity and standardization. In our central control room, we look at our dashboard, and we know we need to act when the lights are turning from green to orange.
HR finds it difficult to approach issues in a different way. Performance management is a good example. Changing the performance management process is often tackled as an organization-wide issue, and HR needs to find the new uniform solution.
In line with the trend called “the consumerization of HR”, employees are expected to take more initiative. Employees are increasingly tired of waiting for the organization and HR, and want to be more independent of organizational initiatives.
If you want feedback, you can easily organize it yourself, for example with the Slack plug-in Captain Feedback. A simple survey to measure the mood in your team is quickly built with Polly (view: “How to measure the mood in your team with Slack and Polly“). Many employees are already tracking their own fitness with trackers like Fitbit and the Apple Watch.
Many teams primarily use communication tools as WhatsApp and Slack, avoiding the officially approved communication channels. HR might go with the flow, and tap on to the channels used, instead of trying to promote standardized and approved channels.
How can workforce analytics benefit from the data gathered by on their employee’s own devices? If it is clear, what the benefits are for employees to share their data, they might be able to help to enrich the data sets and improve the quality of workforce analytics.
8. Ignoring the learning curve
In their book “Making HR measurement strategic”, Wayne Cascio and John Boudreau presented an often-quoted picture, with the title “Hitting the “Wall” in HR measurement”. The wall was the wall between descriptive and predictive analytics.
There are many more overviews with the people analytics maturity levels. Generally, the highest level is predictive analytics.
Patrick Coolen of ABN AMRO Bank recently mentioned a next level: continuous analytics, and he introduced a second wall, the wall between predictive analytics and continuous analytics.
As predictive analytics seems to be the holy grail, many HR teams want to jump immediately to this level. Let’s skip operational reporting, advanced reporting and strategic analytics. We can leapfrog, ignore the learning curve, and jump to the highest level in one step.
For many teams, ignoring the learning curve does not seem to be a sensible strategy. Maybe it is better to learn walking before you start running.
9. Give us back our time!
Recently I spoke to HR professionals from big multinationals who were involved in a “Give us back our time” projects.
In their organizations, the assignment to all staff groups was: stop using (meant was: wasting) more and more time of the employees and managers, please give us some time back!
An example that was mentioned concerned performance management. In this organization, they calculated that all the work around the performance management process for one employee costed manager and employee around 10 hours (preparation, two formal meetings per year, completing the online forms, meeting with HR to review the results etc.).
By simplifying the process (no mandatory meetings, no forms, no review meetings, just one annual rating to be submitted per employee by the manager), HR could give back many hours to the organization – to the relief of both managers and employees.
Big HR systems generally promise a lot. But before the system can live up to the high expectations, a lot of work needs to be done. Data fields must be defined. Global processes must be standardized. Heritage systems must be dismantled.
This results in a lot of work (and agony), for employees, for managers, for HR and for the implementation partners (who do not mind).
Workforce analytics can help a lot in the “give-us-time-back” projects, for example by some simple time-measurement. Measure the time a sample of managers, employees, and HR professionals spend on different activities, and estimate the value these activities optimizes the core activities of the organization (e.g. serving clients and bringing in new clients).
10. Too high expectations
The expectations of workforce analytics are often too high. Two elements must be considered.
In the first place, human behavior is not so easy to predict, even if you have access to loads of people data.
Even in domains where good performance is very well defined and where a lot of data is gathered inside and outside the field, as for example in football, it is very difficult to predict the future success of young players.
Secondly, the question is to what extend managers, employees and HR professionals behave in a rational way. All humans are prone to cognitive biases, that influence the way they interpret the outcomes of workforce analytics projects. Some interesting articles on this subject are why psychological knowledge is essential to success with people analytics, by Morten Kamp Andersen, and The psychology of people analytics, written by myself.
A more general thought: what if you replaced ‘Workforce analytics’ with ‘Science’? What is the role of science in HR? The puzzle is, that there are many scientific findings that have been available for a long time but that are hardly used in organizations.
Example: it has been proven repeatedly, that the (unstructured) interview is a very poor selection instrument.
But still, most organizations still rely heavily on this instrument (as people tend to overestimate their own capabilities). Why would organizations rely on the outcomes of workforce analytics, when they hardly use scientific findings in the people domain?
An interesting presentation on this topic that I recommend is by Rob Briner, titled evidence-based HR, what is it and is it really happening?
There’s a lot that’s changing in the world of work. These are the 10 trends in workforce analytics that I’m seeing today and that will likely impact the way we work in the near future.
This article is based on a keynote I gave at the Workforce Analytics Forum in Frankfurt, Germany, on April 18, 2018.
by Tom Haak
Tom Haak is the director of the HR Trend Institute The HR (Human Resources) Trend Institute follows, detects and encourages trends. In the people and organization domain and in related areas. Where possible, the institute is also a trend setter. Tom has an extensive experience in HR Management in multinational companies. He worked in senior HR positions at Fugro, Arcadis, Aon, KPMG and Philips Electronics. He holds a master’s degree in Psychology. Tom has a keen interest in innovative HR, HR tech and how organizations can benefit from trend shifts. Twitter: @tomwhaak