Workday People Analytics：利用人工智能、机器学习和增强分析的优势
文/Pete Schlampp
有人说，数据是新石油。但是几乎在所有公司，其生成的数据远远超过他们能够分析利用的数据。而在很长一段时间里，Workday的目标都是帮助公司从数据中汲取有价值的见解。从内置报告和分析开始，随着Workday Prism Analytics和Workday Data-as-a-Service的推出，随着数据量、速度和种类的增长，Workday扩大了产品范围，帮助客户充分利用他们的数据。
Workday Prism Analytics致力于开放性和将非Workday的数据引入系统，是您的财务和人力资源团队的数据中心。今年夏初，Workday通过收购增强分析的市场领导者Stories.bi，在分析之旅中又向前迈进了一步。
今天，我们很高兴地宣布Workday People Analytics，是一个全新的应用程序，它将向高管、组织领导人和人力资源业务合作伙伴提供关于他们的员工队伍中最关键的趋势视图，以及了解趋势的最可能的驱动因素。它将利用强大的人工智能(AI)、机器学习和增强分析技术，提供动态创建的关键指标，并伴有解释性叙述——我们称之为故事。
How We Got Here
首先需要一些背景。注意让Workday People Analytics与我们现有的产品一起工作。由于Power of One，Workday有一个数据模型，因此我们的应用程序能够非常轻松地处理有关人员的数据。对于Workday人力资本管理(HCM)的客户来说，Workday People Analytic将利用这些数据进行开箱即用。即使对于那些不使用Workday HCM的客户，他们也可以通过Workday Prism Analytics从任何HCM系统中引入外部数据，因此这些见解仍将可用。
That’s Great, Now What?
其次，我们必须以更好的方式将有价值的信息交给高管。因此，Workday利用了增强分析将许多应用于企业问题的AI功能集合在一起，包括：
自动模式检测功能，可以查找人类可能看不到的重要变化
图形处理以查找大量数据集之间的连接
机器学习预测最重要的问题供您查看
用自然语言来解释一个简单的故事中发生的事情
Workday的人工智能将搜索数百万种可能的数据场景，并确定优先级，以故事形式自动向高管推送个性化见解。故事为正在发生的事情提供了一种自然的语言解释。洞察力可以是积极的，也可以是消极的——它们只是你应该知道的事情。它们会自动地对你的数据进行更深入的挖掘，并告诉你为什么会这样。这为领导者提供了在做业务决策时所需要的基本信息。
See What Matters Most
Workday People Analytics是我们第一个使用增强分析的地方。它将为管理人员，组织领导者和人力资源业务合作伙伴提供可操作的指导，将动态创建的故事与静态内容相结合，涵盖组织构成、多样性、招聘、保留和人员流失以及人才和绩效等方面。
您将看到最重要的事情，以便您可以在最短的时间内做出最佳决策。这将使得组织的行动，创新和学习速度更快。
Workday People Analytics不是自动生成针对特定问题的预测，而是提供一种叙述，以指导管理人员在一个广泛的领域中找到聚焦点——无论是具体的团队、位置、客户还是产品线。它使用机器学习来预测和展示真正重要的东西。换句话说，Workday People Analytics会告诉您需要了解的内容。
例如，一位人事主管可能会收到一条消息，表明新员工流动总体上有所增加，他们不仅应该关注伦敦的销售组织，还要考虑薪酬以及特定的招聘经理。该应用程序可帮助领导者专注于影响其业务的最重要问题，并回答以下高价值问题：
招聘过程中的瓶颈是什么？
该组织多样性的五大趋势是什么？我们作为一个社区如何发展？
整个组织可以从哪些卓越的领域中学习？
我们在哪里看到异常高的磨损？它背后的驱动力是什么？
因此，信息负载减少了1000倍——你会发现什么是最重要的，这样你就能在最短时间内做出最佳决策。组织行动、创新、学习更快，形成良性循环。
Future’s So Bright…
Workday People Analytics只是一个开始，我们很高兴能够进入数据的新时代，超越自助服务，进入人工智能能够有效预测的世界。未来，我们将在所有Workday的产品中应用增强分析。Workday People Analytics将于明年秋季提供给早期用户，通常在2019日历年末提供。单独销售给Workday HCM客户，它将作为Workday Prism Analytics的一部分提供。我们确信好戏还在后面。
以上为AI翻译，观点仅供参考。
原文链接:Announcing Workday People Analytics: Leveraging the Strength of AI, Machine Learning, and Augmented Analytics
HR data analytics
2018年10月06日
HR data analytics
HR Data Analytics – Case Use by HR Organziations作者：William Chin 授权发布
The Chapman Consulting Group just completed their Spring HR leaders meeting in Beijing on May 15. This time Lenovo hosted the session at their Beijing headquarter office.
The topic for this round is centered around Managing Global HR in the age of ‘Big Data’
What companies are doing to optimise talent and HR systems in parallel with the advent of global and regional Centres of Excellence;
The increased use of data and analytics as another tool of Global HR management; and
The effect this is having on the type of HR Leader progressing within the profession.
This theme is consistent with their #1 trending HR focus areas for 2014.
I have captured key points from the meeting below.
Lenovo – Shared Services
Lenovo, the world’s leader in the PC industry, had just implemented a global HR system, making the switch by eliminating several disparate systems into a global solution. While they have done all the requisite change management requirements with organisation stakeholders, they are seeing that people still like to do things the “old way.” How true! People hate to change.
While Lenovo made a clear stand that all everyone need to adopt and utilise the new system they do have a VIP process for their top key executives. The VIP allows for telephone hotline and/or email communication to a HR professional for assistance. However, everyone else is expected to utilise the new self-service model.
The benefit of going global with their new HR system is now they have the ability to manage their workforce under one roof. Previously, HR was unable to access “real time” data and instead, was managing people with spreadsheets.
Pfizer – Improving Retention
Employee retention is a huge risk in the pharmaseutical industry in China. Industry average is around 25-30% turn-over each year.
Pfizer is the global largest pharmaceutical player and is also the largest in China. Even Pfizer is not immune to the high turnover rate. In fact, competitor companies target their employees, because they are the largest.
To combat turnover and improve retention they turned to “big data” to better understand drivers of turnover – they created an employee profile of turn-over drivers. The profile Pfizer developed is employee specific with a “risk score.”
Pfizer partnered with a consulting company to develop the analysis tool combining existing employee data and against employees who left the company. By looking at former employee profiles they then were able to map those to existing employee enabling Pfizer to see trending issues that may cause turnover. Seeing this information ahead of time allows HR to partner with BU leads to take proactive actionable steps.
Some examples of high risk dimension include: employees where are a rehire (they have already left once), short-time with a manager (have not developed a strong bonding with the direct manager), and long tenure in a role (it’s time to refresh with a new focus). I was thinking these are all indicators of high risk turnover by itself. So, why do you need to do a study? The genius is that employee turnover is multi-dimension. Not one thing by itself are drivers of turn-over but, by combining all the various turnover drivers and employee profile, you begin to see a multifaceted profile of their employees – HR and BU can then take multiple tracks to drive retention.
Qualcomm* – Use a Data Analytics
Qualcomm has a dedicated data analytics team. That team started in 2008 and was a small group who was responsible for generating large HR data but, on spreadsheet format. Over the years, Analytics Team went on to focus on benchmarking to creating data visualisation and now focusing on predictive modelling for the company.
Qualcomm human resources has the ability to pull up dashboard data an a click of a button. This is information is globally accurate and with the ability to do drill downs by organisation, business function, geography and employee types etc. This enables Qualcomm HR, at all levels – HRVP to HR specialist, to have the same data points, at any time.
The analytics team also conducts research projects analysing the success of a merger and acquisitions project. The team created a social network analysis / model indicating the strength on network and social ties. In a M&A, one would typically want to see the newly acquired company integrate into the overall company. The faster employees integrate the greater the success outcome. Creating such a model allows Qualcomm to analyse and visualise social interactions to gain insight on who were the “bridge builders,” those who were the best at helping with integrating after a merger.
Doosan – Don’t Over Do It With Data
Doosan is a Korean-based conglomerate. The HRVP reminded us that sometimes over use of data can be detrimental to business decision. Instead of using judgement, managers often ask project analysts or HR for more data to help with their decision making. With the data provided, business will ask for more next level data, to back up the high-level data. Analyze the data, analyse more data, then the data paralyses you. By the time the data is complete that the information is out of date and decision window is closed. How often have we faced this before? The presenter was so right on with this point.
In HR, we also have metrics and data to measure our performance. The roundtable participants all have HR KPI scores they manage to. One hotelier HR said that after a HR systems implementation that their HR satisfaction scores dropped. I thought that after any large project implementation that one would expect a drop (remember that people hate changes). Instead of managing to the dropped score HR should be managing to improving the score and maybe, that scoring criteria will be different from the prior standard but, the processes and systems have changed.
Doosan further explains that in a business downturn, for example, HR is expected to manage employee reductions. So, what happens if HR is successful with meeting the employee reduction targets and morale KPIs are on track yet, the business continues to decline. Doosan HR further suggests that we use experience and judgement to help the business. Data is only one part of the story.
This wraps the summary of key points by each presenter. The Chapman Consulting Group always does a good job with brining together a group of HR leaders from various industries for sharing and networking.
作者声明：
*I am employed by Qualcomm. However, the information contained within is the opinion of the author and not that of my employer. All company and/or product names may be trade names, trademarks and/or registered trademarks of the respective owners with which they are associated. Furthermore, this blog post does not represent an endorsement of their products and services and I have woven my own experience in this post. This is for informational purposes only. There is no representations as to the accuracy or completeness of any information.
HR data analytics
HR data analytics
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