（2016年12月5日，/HRoot.com/）据招聘专家瀚纳仕中国的研究，人力资源团队拥有大量的数据可以为他们提供见解，以更好地了解自己的员工。

“2000年代见证了数字革命，公司开始更多地了解其客户，”瀚纳仕大中华区董事总经理Simon Lance说。“预计这对人力资源也将是一大新闻，但大多数人力资源部门的旅程才刚刚开始。数据提供的可能性和机会是相当令人兴奋的。例如，当一个有价值的员工在考虑离职时，人力资源部门要能够加以识别，大数据就是一个非常有价值的工具。

瀚纳仕指出，人力资源部门充分利用数据，也是技术不断变化的结果。技术快速变化意味着，如果人力资源系统还只是功能性的，那么它可能已经过时了。此外，工具的数量和大量的数据似乎令人畏惧，让许多企业开始思考自己该从哪里开始。

“重要的是，在决定前进方向和学习领域的时候，要从小处开始，” Lance说。“技术进步如此迅速，实施一个大的系统并连接所有部门，可能是不值得的。相反，要通过使用你已经拥有的数据来首先找出基本的连接，并以此开始探索。”

在过去的一年，瀚纳仕也开始探索用先进的机器学习来管理数据。瀚纳仕数据科学团队负责人David Pardoe说：“有关我们业务许多方面的数据已经在收集了，用瀚纳仕已开发的算法和模型来处理这些数据，帮助企业确定关键的发展机遇。我们希望这对瀚纳仕和我们的客户都是有价值的。”

Hays：HR professionals can use data to draw insights on their workforce

(Dec.5, 2016, /staffingindustry.com/)According to international recruitment firm Hays China, HR teams have a huge amount of data available to them and can draw insights from this data to better understand their own workforce.

Hays states that data can determine what drives employee performance, when an employee is about to resign and how HR contributes to an organisation’s objectives.

 “The 2000’s witnessed a digital revolution and companies began learning more about their customers,” Simon Lance, Managing Director of Hays in China, said. “It was predicted at the time that this would also be big news for HR, but the journey has only just begun for most HR departments. The possibilities and opportunities that data can provide is quite exciting. For instance, being able to recognise when a valued member of staff is thinking about leaving is an extremely valuable tool for businesses.”

Hays states that the reason HR departments are yet to fully use the data available to them could be the result of ever changing technology. The speed at which it changes means by the time a system is functional, it could already be out of date. Also, the number of tools and vast amount of data can seem daunting, leaving many businesses asking themselves where to begin.

“It’s important to start small before deciding which direction to head in and which areas you wish to learn about first,” Lance said. “Technology does advance so quickly that implementing a large system and connecting all departments may not be worthwhile. Instead find out the basics first by using the data you already have available and begin to explore from there.”

Over the past year Hays too has begun exploring advanced machine learning to manage its data. David Pardoe, Group Head of Data Science, said: “Data has been collected about many aspects of our business and Hays has developed algorithms and models to process this data, helping identify key development opportunities. The expectation is that this will be of value to both Hays and our clients.”

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