• People Analytics
    ADP收购Marcus Buckingham 的TMBC公司以扩大人才投资组合 这个收购大家关注的不多,但是其实可以关注下~ 人力资源数据管理和咨询服务,更加凸显! ADP做了一个不错的买卖!详细可以看英文版本。 TMBC的创始人,Marcus Buckingham 可是大名鼎鼎的人物啊!著名商业思想家、畅销书作家!发现你的优势!打破一切常规! ADP has acquired The Marcus Buckingham Company (TMBC), an innovator in human capital management (HCM), to bring to ADP clients a more scientific approach to employee engagement and performance. TMBC, and its founder Marcus Buckingham, are pioneers in using data and research to drive talent management practices that help managers build engagement and increase performance in their teams. Their unique approach empowers managers to coach employees based on their strengths and custom-design teams based on those strengths. TMBC's cloud-based performance and talent management solution, StandOut, couples applications with coaching and education to give team leaders the tools, insights and data needed to turn talent into better employee performance. Built on decades of groundbreaking research that has uncovered the factors that differentiate high-performing teams, this solution will now be offered as ADP StandOut. TMBC has a global client roster that spans a broad range of industries from professional services to hospitality and includes many companies in the Fortune 100. "At its core, the strength and differentiation of any company lies in its talent," said Carlos Rodriguez, president and CEO, ADP. "That is why we are continuing to invest in data-driven talent management solutions with the acquisition of TMBC. The company's technology and renowned research will add to our existing talent portfolio and puts ADP in a position to better serve the growing number of innovative organizations who are thinking differently about how they manage and engage their talent. We are thrilled to welcome both TMBC's associates and Marcus into the ADP family." According to the Deloitte Global Human Capital Management Trends 2016 report1, 77 percent of executives say that people analytics are a priority, but only 29 percent think that they are successfully using outside data to predict workforce trends and target the right talent to meet those trends. With that in mind, the acquisition of TMBC helps ADP further deliver on its talent management strategy of helping companies build better workforces through the strategic use of data and research. Marcus Buckingham is a noted author and speaker and has been featured as a thought leader on talent management and leadership trends in a range of outlets that span The Harvard Business Review and Forbes to Oprah and Larry King. "In the world of people-at-work, everyone trusts ADP data, so I leapt at the chance to bring to the ADP ecosystem StandOut's data-based insights and tools on people's talents, engagement and performance," said Marcus Buckingham, co-head, the ADP Research Institute. "At a time when so many companies are clamoring for real-time and reliable people data, when they crave tools that leaders and team members actually want to use, the combination of ADP's scale, security and data-integrity with StandOut's focus on real-world teams, is unique and powerful. I'm so excited to see how many companies and people we can serve." ADP has helped organizations of all types and sizes for more than 60 years unlock the potential of their workforces. ADP's cloud-based talent portfolio -- which includes recruiting, recruiter training, outsourcing, screening and selection, onboarding, learning, goals, performance, data analytics, succession and compensation -- is expanded in breadth and depth with TMBC's technology, consulting and research. For more information, visit adp.com/standout.  
    People Analytics
    2017年02月04日
  • People Analytics
    GOOGLE 的HRVP Laszlo Bock 离职创业了!《重新定义团队:谷歌如何工作》的作者 《重新定义团队:谷歌如何工作》一书作者、前谷歌人力运营部高级副总裁、谷歌高级顾问Laszlo Bock先生在2017年元旦正式宣布离职创业。 在BOCK先生任职谷歌(2006-2016年) 担任人力运营部负责人的shi年期间,谷歌的员工数从6000增长到近5万,在全球四十多个国家设立了七十多个分支机构。他帮助谷歌创建了人力运营部,并创建了谷歌(也许是世界上)第一个人才数据分析团队。他撰写的《重新定义团队:谷歌如何工作》一书是用人才数据说话的典范,并揭示了改变未来的工作法则。该书曾获《纽约时报》畅销榜第一名,并在国内HR界引起“重新定义”热潮。他本人曾于2014年被《Human Resource Executive Magazine》评为 十 年内对HR行业影响最深远的 十 人之一。 以下是Bock先生刚刚在LinkedIn上发布的信息: Today I wrap up over a decade at Google. It's been an honor to be a part of the company's story, and a joy to learn so much from so many Googlers. Even more, it was a privilege to build People Operations along with so many exceptional friends and -- together -- to create the first People Analytics team. As for what's next .... (see Google HR chief Laszlo Bock leaving to launch startup By Ethan Baron / December 13, 2016 at SiliconBeat) And with that, here's to an amazing 2017, for all of you and your loved ones! Google HR chief Laszlo Bock leaving to launch startup   If an HR manager can be called legendary, it would be Laszlo Bock, the man behind the company culture at Google for the past decade, and a driving force behind the firm’s data-driven hiring and famed free-food cafeterias. Now, Bock is leaving his post as senior vice president of “people operations” to launch a startup. The enterprise will be in “stealth mode” for a while, Bock said in an email to friends and associates. Bock gave a brief description of the startup. “It hinges on a few ideas: that every job can have meaning, that if you give people freedom they will amaze you, that applied science (which I dubbed ‘people analytics’ a decade back) can illuminate the truth about what really makes people happy and productive, and that it doesn’t take a ton of effort or investment to make things better … but that you can make work better, everywhere,” Bock said in the email. Replacing Bock as head of human resources at Google will be Eileen Naughton, who had been the vice president of sales and operations for Google in the U.K. and Ireland, Fortune reported. Sources told Fortune that Naughton was one of the highest-rated Google managers among employees, and that she was a founding member of internal group Women@Google. According to Fortune, Bock will retain an advisory role at Google. However, in his email he signed himself off as a “soon-to-be-former” senior adviser at Google and its parent company Alphabet, which would appear to mean he won’t be advising or he’ll be doing so in a more informal capacity. Bock transformed hiring at Google from a “clunky, arduous process that relied on gimmicks like math puzzles on billboards” to a “smooth engine,” according to online magazine Quartz. “He helped usher in employee-friendly policies like free meals and shuttle buses, and introduced take-your-parents-to-work days,” the article said. Bock oversaw data-driven hiring practices at Google that “led it to disregard college prestige in job applications, to dramatically change its pay policies, and even change the way it presents food in its cafeterias,” according to a profile in Quartz last year. Before arriving at Google in 2006, Bock, who has an MBA from Yale was a vice president of HR at General Electric, and before that, a management consultant at McKinsey & Co. He is the author of “WORK RULES! Insights from Inside Google to Transform How You Live and Lead.”    
    People Analytics
    2017年01月04日
  • People Analytics
    2017的4大趋势让HR更有战略价值 编译:德鲁菌 数据来源:Bersin by Deloitte 来源:公众号“人才学”  (微信号 people-insight) 就在不久前,德勤(Deloitte)出了一份报告,预测了人力资源行业在2017年的11个趋势。我们总结了这份40多页的报告,提炼出了其中四个最为关键的类目。这四个预测都围绕一个更大的主题——数字化(digital)——而展开:数字化是这个时代的大趋势,2017更是如此;企业在人力资源上的策略,也应当持续地为这个趋势铺平道路。最初,企业关注的是如何用最有效的组织结构实现规模化。企业越能够有效地进行大规模生产,在市场中便越占优势。例如,产品经由研发-->设计-->生产-->销售这个过程时,由于规模较小,所以每个环节都三四个人负责。当公司越做越大,每个部门所需要的人力便增加,于是公司就按照部门的结构来划分,由此产生了研发部、产品部、市场部和销售部。 这是一种典型的由上而下的层级结构(hierarchical structure)。 后来,科技发展越来越快,对于员工的知识更新速度也加快。企业在市场中的竞争力也不再由单纯的规模大小和生产效率决定。能持续找到新的解决方法满足消费者需求才是维持企业竞争力的关键因素。这就使得企业组织架构设计的关注点不再是「可规模化的效率(scalable efficiency)」,而是「可规模化的学习(scalable learning)」——员工数量的增加的同时学习效率能够保持在一定的水平,甚至提升。   新进来一名员工,如果几周之内就能够把业务熟悉透,这要比同时招进三个员工却要花费半年甚至更久来让他们熟悉业务,效率高得多。 为了实现这样的效果,前几年一直很流行组织结构扁平化。这种结构的确减少了管理者与其他员工之间的距离,这样leader们能够更直接地和员工进行沟通,学习的成本也就相应地降低。   但这还不够。这种组织架构依然延续着之前的瀑布流式(waterfall method)的产出方式。比如在一家较大的企业中,只有当研发和设计部门完成了一整套的方案设计并敲定后,才能够进行生产;生产完成后再由市场及销售部门进行推广。 在这种结构下,跨部门沟通的障碍依然没有得到很好的解决。这使得企业在环境快速变化的当今市场缺乏了快速试错的能力。 为了能够更加敏捷地对市场做出学习和反应,更加敏捷的小团队化组织架构由此诞生。  在这种架构下,公司由各个具备跨部门能力的小组构成。每一个小组负责一个产品或项目,并且具备了完成这个产品/项目的必须function(研发、设计、市场、销售等)。   例如当初苹果在进行产品设计和研发的时候,就按照产品项目分成了Lisa小组和Apple II小组。两个小组相互独立运行,各自专注于自己负责的项目。当然正如大家知道的,最后大获全胜了的是另一个叫做Macintosh的小组。 如今,这种小团队化的组织架构的趋势正由互联网企业逐渐蔓延至其他行业。而这也是2017年的一个趋势。人人都在谈论企业文化。自2008年至今,在谷歌上搜索企业文化的数量呈明显上升趋势。而参与度则是员工对于企业文化的反应——企业文化越符合员工期望,员工便越能投入地进行工作。衡量企业文化和它相应带来的员工参与度,可以参考五个大类组成的20个因素。 为了得到关于这些方面的更多更精准的员工数据,越来越多的企业开始将之前的季度/年度review换成了短频且快速的Pulse Survey(脉搏调查)。所谓脉搏调查,正如它的名字暗示的那样,就像医院里的脉搏显示仪那样能对病人的每次心跳给出反馈。 这些每周的pulse survey通常都很简单,一次1-2个问题,涉及到的话题可以是任务反馈、团队建设、客户服务、工作环境等。员工每次只用花两三分钟的时间就可以完成这个调查。目前市面上流行的生产力工具(productivity tool)如Slack、Outlook、Gmail都内置了脉搏调查的插件,其功能也越来越成熟。   而最近兴起的People Analytics概念(直译为员工解析技术),能从数据的角度将员工行为公司业务表现相联系。   例如德勤自己开发CulturePath就能够将团队小组级别的员工信息直接与员工积极性、离职率等内容进行关联,便于企业在这些方面进行更好的决策。 2017年,企业要做的不仅仅是建立一个具有数据分析能力的团队,并去运用这些工具;更重要的是,要在企业内部建立起一个由这些工具和团队构建的反馈机制。通过总结长期的研究,HR部门的任务可以归为以下三点: 确保人事相关的流程有效率地进行,包括招聘、培训、薪酬、绩效等 建立与战略相关的人才系统,不光是“招到人”,而是“招到符合需求的人” 了解、管理和提升员工体验,确保员工能在一个能让他们充分发挥潜力的环境下工作   可以看到,HR的工作是围绕着人展开的。但长时间以来,这个部门的许多思维却是「项目思维」。回顾一下自己的人力资源部,是否设计并实施了这样那样的项目(program)?   需要让员工了解更多业务知识,于是开设了很多培训项目。为了提倡员工的自我学习,甚至有的公司要求员工下班后参加读书学习会。这些项目很多时候轻则治标不治本,重则有点……用产品设计的话说就是“反人类”。 奇怪的是,企业在面对消费者的时候却能够充分考虑用户的需求,并依据他们的实际需求进行产品设计。尤其在互联网行业,产品经理就承担着这份重要的职责。他充分地洞悉用户的痛点,在进行了详实的市场分析后,为这个痛点量身定做解决方案。  脑海中回荡其万塘路18号的《产品经理是条狗》这首业内知名曲目人们一直强调的「产品思维」就是这个意思。你是在为用户设计产品,产品必须解决他们的痛点。HR部门在提升员工体验的时候,也应当具备这种思维方式,把员工当做用户,发现他们的痛点,并相应地解决。   再回到上面的例子,员工平时缺乏自我学习,或许原因根本就不是少了几场读书学习会。 也许真正的原因是团队在计划工作内容的时候,没有划分清楚任务的优先级,导致员工经常陷入没完没了的应对繁琐任务的消极节奏中,从而丧失了学习的动机。 了解到了这一点,那么为了提升员工学习的机会,HR部门应当协助团队确认任务的优先级,提升员工的work-life balance,这样员工便有更多的时间和精力去自我提升。 第一个E:Education(教育)这是培养领导力的最基础形式。开一场讲座,或者开设一系列的培训,让部门负责人在台上以讲课的方式培训员工。  这种形式的培养是单向的,缺乏环境和反馈,因此根据多项研究表明,这也是最没有效果的一种培养领导力的方法。   第二个E:Experience(经验)指的是除了接受正式的培训,还能够通过观察前辈的日常工作、向上司进行请教的方法,获得他们的经验,从而提升相关的领导能力。  第三个E:Exposure(感受) 通过与同事、专家、意见领袖、客户等角色进行接触而学习到的领导能力。在这种环境下,学习者可能会负责一个项目,但这个项目大多是模拟的,而非与实际公司业务结果相联系的。   第四个E:Environment(环境) 这是最高形式的培养领导力的方法。意思是将学习者放到实践中,去直接领导一个项目,并由指定的上级给出反馈和指导。更重要的是,这些项目不是模拟的,而是真正与业务有关的。这样一来,学习者就能够持续地看到自己的工作对公司的实际影响,从而产生更多的动力。 根据德勤的数据显示,在被访的企业中,只有15%的80、90后觉得企业提供有效的领导力培训。而根据另一项高效领导力的相关调研,教育式的领导力培养效果是最差的。   所以在2017年,企业对年轻人的领导力培养,应当以四个E的结合形式进行。
    People Analytics
    2016年12月28日
  • People Analytics
    Slack 应战微软协作工具Teams Dear Microsoft, Wow. Big news! Congratulations on today’s announcements. We’re genuinely excited to have some competition. We realized a few years ago that the value of switching to Slack was so obvious and the advantages so overwhelming that every business would be using Slack, or “something just like it,” within the decade. It’s validating to see you’ve come around to the same way of thinking. And even though — being honest here — it’s a little scary, we know it will bring a better future forward faster. However, all this is harder than it looks. So, as you set out to build “something just like it,” we want to give you some friendly advice. First, and most importantly, it’s not the features that matter. You’re not going to create something people really love by making a big list of Slack’s features and simply checking those boxes. The revolution that has led to millions of people flocking to Slack has been, and continues to be, driven by something much deeper. Building a product that allows for significant improvements in how people communicate requires a degree of thoughtfulness and craftsmanship that is not common in the development of enterprise software. How far you go in helping companies truly transform to take advantage of this shift in working is even more important than the individual software features you are duplicating. Communication is hard, yet it is the most fundamental thing we do as human beings. We’ve spent tens of thousands of hours talking to customers and adapting Slack to find the grooves that match all those human quirks. The internal transparency and sense of shared purpose that Slack-using teams discover is not an accident. Tiny details make big differences. Second, an open platform is essential. Communication is just one part of what humans do on the job. The modern knowledge worker relies on dozens of different products for their daily work, and that number is constantly expanding. These critical business processes and workflows demand the best tools, regardless of vendor. That’s why we work so hard to find elegant and creative ways to weave third-party software workflows right into Slack. And that’s why there are 750 apps in the Slack App Directory for everything from marketing automation, customer support, and analytics, to project management, CRM, and developer tools. Together with the thousands of applications developed by customers, more than six million apps have been installed on Slack teams so far. We are deeply committed to making our customers’ experience of their existing tools even better, no matter who makes them. We know that playing nice with others isn’t exactly your MO, but if you can’t offer people an open platform that brings everything together into one place and makes their lives dramatically simpler, it’s just not going to work. Third, you’ve got to do this with love. You’ll need to take a radically different approach to supporting and partnering with customers to help them adjust to new and better ways of working. When we push a same-day fix in response to a customer’s tweet, agonize over the best way to slip some humor into release notes, run design sprints with other software vendors to ensure our products work together seamlessly, or achieve a 100-minute average turnaround time for a thoughtful, human response to each support inquiry, that’s not “going above and beyond.” It’s not “us being clever.” That’s how we do. That’s who we are. We love our work, and when we say our mission is to make people’s working lives simpler, more pleasant, and more productive, we’re not simply mouthing the words. If you want customers to switch to your product, you’re going to have to match our commitment to their success and take the same amount of delight in their happiness. One final point: Slack is here to stay. We are where work happens for millions of people around the world. You can see Slack at work in nearly every newsroom and every technology company across the country. Slack powers the businesses of architects and filmmakers and construction material manufacturers and lawyers and creative agencies and research labs. It’s the only tool preferred by both late night comedy writers and risk & compliance officers. It is in some of the world’slargest enterprises as well as tens of thousands of businesses on the main streets of towns and cities all over the planet. And we’re just getting started. So welcome, Microsoft, to the revolution. We’re glad you’re going to be helping us define this new product category. We admire many of your achievements and know you’ll be a worthy competitor. We’re sure you’re going to come up with a couple of new ideas on your own too. And we’ll be right there, ready. — Your friends at Slack
    People Analytics
    2016年11月03日
  • People Analytics
    招聘文本分析创企Textio,获800万美元A轮融资 来源:猎云网(编译:竹子) Textio,一家分析特定情景中的词汇和语言的创企,今天宣布完成了由Emergence Capital领投的800万美元A轮融资。Cowboy Ventures、Bloomberg Beta和Upside Partnership也参与了此次融资。 Textio的第一款工具瞄准的是人才并购领域如招聘。创始人Snyder发现,某些特定的词汇和设计对应聘者更有吸引力,于是这些预测分析就被吸纳进了Textio的服务里。Textio可以分析一个公司的职务说明、绩效考核与其它备案,并判断这些文案是否可以为公司取得最佳效果。 该软件利用人工智能技术扫描招聘文本信息,然后向公司建议进行调整,以提高该公司吸引能力强的应聘者的机会。比如,有重点句的职位招聘总是比没有的更吸引求职者。Textio的软件还会建议各公司引进更多样化的应聘者。例如女性职场人士通常不会参与办公室内斗,或通常不会从事代码类的工作。 Textio成立于去年秋天,它的两位创始人 Kieran Snyder 和 Jensen Harris 分别是微软和亚马逊的前员工,Kieran Snyder之前致力于科技公司中性别歧视的研究,同时曾在微软、亚马逊任职语言学家,Jensen Harris曾在微软工作过16年。Textio创办后不到5个月,就在今年2月份拿到了150万美元的融资。 Textio主要以三个方面来衡量相关的科技短语:一是申请包含该短语岗位的应聘者人数;二是满足该词语要求技能的应聘者在全部应聘者中的百分比;第三是工作招聘发出后多久能够招到相关的人才。 虽然目前它的用处在于职位招聘,但很明显,这些技术可以被用到其他很多方面,比如邮件、简历以及其他各类信息。如果技术运行良好,理论上它可以为各类文档搭建分数库,这或许也是吸引投资人的地方。 Textio还有其他有价值的过人之处吗?大概就是它的客户了吧。目前使用Textio服务的企业有Twitter、Atlassian、Starbucks、Square和Microsoft等等。自然语言处理技术有广阔的应用领域,这又是对投资人的另一大强烈吸引。 Snyder表示,Textio目前可以识别出超过6万句短语词组,而这一数据还在持续增长。它会研究词汇是以何种方式组合在一起,比如词组中动词的密度和其他语法相关的特性。基于以上种种,最后给出评定分数。 当然,Textio也有不少潜在的竞争对手,诸如IBM Watson理论上也能分析文本并给出类似的结果。不过Snyder表示他们的优势在于专注的内容领域更具体。 Textio, A Startup That Analyzes Text Performance, Raises $8M Textio CEO Kieran Snyder took a quantitative approach to how language worked in her linguistics studies. And when she and her co-founder Jensen Harris were leaving Microsoft to start a new company, it was only natural that it would be centered around language in some way.   That’s how Textio, a startup that analyzes text for how well words and phrases perform in certain scenarios, was born. The company today said it raised $8 million in a financing round led by Emergence Capital. Cowboy Ventures, Bloomberg Beta, and Upside Partnership also participated in the financing round.   “We had this premise that word processing in text hadn’t been disrupted in a while, from command line to GUI,” CEO Kieran Snyder said. “We had the internet come along, it was about social and sharing, and we think that AI and the set of related technologies is the next big disruptor of text. If you know the performance of a document before it’s ever published then you can fix it before it’s published.”   Textio’s first tool looks at talent acquisition documents — like job postings — to determine how well they will perform among candidates. Certain words and layouts attract more candidates than others, Snyder found, and those predictive analytics are baked into the service. For example, Textio shows that job postings with bullet points tend to perform better than job postings without them.   Right now it’s used for talent acquisition documents, but it’s pretty easy to see that the technology can be applied to documents that include common phrases — such as email, resumes, or other kinds of messages. If the technology works, it can theoretically begin building up scores for those kinds of documents, which is likely what attracted investors to the product and the team. Another reason it might be so valuable to investors? Its customers. Already Textio is being used by companies like Twitter, Atlassian, Starbucks, Square and Microsoft. Natural Language Processing technology has very broad applications if done right, which makes it an attractive bet for many investors.   Textio recognizes more than 60,000 phrases with its predictive technology, Snyder said, and that data set is changing constantly as it continues to operate. It looks at how words are put together — such as how verb dense a phrase is — and at other syntax-related properties the document may have. All that put together results in a score for the document, based on how likely it is to succeed in whatever the writer set out to do.   Given who’s likely using Textio, it’s important that it feels easy to use — hence the highlighting and dropdown boxes rather than readouts. Snyder said, at its core, Textio can’t feel like a statistics tool, and that’s probably because the kinds of people using it might not always be NLP experts.   Of course, there are potential competitors in the space when it comes to natural language processing. There are tools like IBM Watson that can analyze text and, in theory, pull off a similar result. But Snyder says Textio’s results will be better because they are content-specific — like in the case of talent-acquisition documents.   Source:TC
    People Analytics
    2015年12月24日
  • People Analytics
    观点:企业级市场召唤一个“真正移动”的时代 编者按 : 彼得·瓦格纳(Peter Wagner)和马丁·吉尔斯(Martin Giles)都是风险投资公司Wing Venture Capital 的合伙人。   移动设备、云计算和大数据显现出来的优势地位,对人们的生活和工作带来了深刻的影响。但令人遗憾的是,很多在智能手机和平板电脑上运行的企业级应用不能充分发挥这一革命性转型的作用。甚至标榜自己是“移动为先”的某些服务,往往也不能推出新奇的功能来提高生产力和利润。   现在,企业级应用市场的规模达到 1000 亿美元,要想主导这个市场,就必须是做到“真正的移动”(authentically mobile),即如果我们不充分利用移动生态系统的特有属性的话,各项服务将难以完成,甚至不可能完成。   名副其实的真正移动企业级应用数量还很少。但是,一些从事客户关系管理业务的公司(如 Clari 和 Base CRM)和提供人力资源服务的创业公司(如 Yoi),他们的服务体现出了移动真实性的一些特点,这预示着移动行业的未来发展趋势。   相对于传统因循守旧的应用,“移动为先”的应用(优先使用移动应用而不是事后再用)必定是一大进步,因为传统方式只是在移动平台上简单模拟台式机的功能。“移动为先”的服务则提供更加优雅和更加直观的用户界面,可以更加充分地利用智能手机和平板电脑一些的独有功能。例如,我们可以通过传感器和轻便的用户输入设备来储存数据,可以对内容和操作方式进行超个性化的设置,同时这些服务的使用时间随意,对用户的吸引力更大。   但是,“移动为先”的服务主要还是优化现在的工作流程,以适应已有的新平台,而没有开创全新的功能。这是要达到真正移动应用(它们才能带来真正的改变)前所要经历的一个中间阶段。下面的表格概述了一些重要的影响因素。 所谓真正的移动应用,就是说,没有移动设备,工作流程将很难进行。与“移动为先”的服务不同,它们更加注重搜集和分析促进创新体验的数据。若要设计出真正的移动应用,光是专注于移动的创业公司孵化器的纸上谈兵还远远不够,因为企业流程和相应的数据超出了移动获得主导权之前的那个时代的范围。   很多工作人员的手机上都安装了消费类应用,这是真正的移动。例如,Uber 就利用了移动的“及时性”特点,并辅以 GPS 技术来追踪人们的位置。如果在台式机上来运行 Uber 之类的应用,简直是太荒谬了——这种模式可以用来判断一种企业级应用是不是真正具有移动的特点。   一些打入企业市场的消费类应用声称自己实现了真正的移动,至少是部分实现了。例如,Dropbox 吸引用户的地方在于移动用户必备的同步属性。但是,它在台式机上使用的效果也很好,因此这就不是真正的移动了。然而,当 Dropbox 用在其他设备上时,它的重要性就大大显现出来了——Dropbox 的数百万用户清楚地看到了这一点。   还有一些真正的移动消费类应用会以全新的方式来改善劳动力。但是,对于创业公司来说,这也是一个绝好的机会,他们可以为“真正的移动”主导的市场提供个性化产品。估值极有可能达到数十亿美元的公司将是面向知识性人才的。相对于那些面向商店、工厂和医院的工作人员的“低等”应用来说,这些“高等”应用的收费更高,市场空间更广。   “真正的移动”趋势目前还处在发展的初级阶段,但 Slack(以团队沟通协作为基础的移动办公平台)却自我标榜为“原生的移动”,此举说明这一领域还是有很大潜力可挖的。就像 Dropbox 一样,Slack 既能在移动设备上运用,也能在台式机上运用,因此,从严格意义上讲,它并不符合“真正的移动”的标准。但是,它的一些核心功能,如“一体化”的高度集成、多样的数据来源和记录系统,只有在移动环境下才能发挥出特殊的作用。Slack 最多不过就是一个多平台交叉应用,越来越接近“真正的移动”。   一般来说,商业通讯就是在多个应用之间交叉进行。Blue Jeans Network(一家获得 Wing Venture Capital 投资的创业公司)已经推出了一种服务,使员工们可以通过移动设备参加多点式、跨平台的视频会议,并可以彼此之间分享视频画面。展望未来,开发商们可能会开发出真正的移动应用,那时,工作人员就可以轻松地把手机上的视频会议画面传输到会议室或台式机上了。   客户关系管理(CRM)也为“真正的移动”大发展做好了准备。Clari 可以通过手机向销售部门主管发送提醒,确保他们在会面前具有客户或潜在客户的最新信息。它还能提醒他们在外奔走时随时更新交易状态,以便可以快速捕捉和分享数据。Base CRM 则包括一项地理位置功能,可以绘制出用户的联系人地图,提供到达那里的一键式导航。   除了这些至少部分体现了移动真实性的应用开发早期例证,整个客户体验管理(Customer Experience Management)——覆盖售前和售后互动两个方面——将成为那些在企业员工和客户之间打造实时、动态联系的产品的沃土。   另一批真正的移动解决方案将出现在 HR(人力资源)领域,它们正在从相互孤立的《呆伯特》(Dilber)式卡通片,转变为一个数据驱动的整体,与重要业务流程紧密结合。新兴的“人力分析”领域(追踪员工互动等事情)看起来特别具有发展前景。   Yoi 已开始着手改善员工管理的成功率,向新入职的员工提供他们在外出活动及在办公室时所需要的信息。更为重要的是,Yoi 还可以实时捕捉和量化员工的数据,并反馈给部门主管,令其迅速介入,解决员工遭遇的烦恼,或是加速员工学习过程。   Yoi 就是一个令人感兴趣的例证,证明移动应用可以与数据和云端的力量相结合,用于深入揭示一个原本相当晦涩的过程,提高员工留存率。   其他对真正的移动企业级应用做好准备的领域还包括,支付、旅游管理和数字安全。正如我们在之前指出的,有些真正的移动消费类应用将会进入企业级市场,解决一些公司的实际需要。但创业者们也面临着一个激动人心的机遇,为企业及其客户和员工从零开始开发新产品。在新兴的真正移动应用商店内,大量闲置货架正等待新产品的到来。   Mobile First, But What’s Next? Editor’s note: Peter Wagner and Martin Giles are partners at Wing Venture Capital.   The ascendance of mobile devices, cloud computing and big data is having a profound impact on the lives of workers. Yet many of the business applications that run on their smartphones and tablets still fail miserably to take full advantage of this revolutionary shift. Even services that tout themselves as “mobile first” often fall short of delivering novel functionality that boosts productivity and profit.   The offerings that will come to dominate the emerging $100 billion market for enterprise applications will be those that are “authentically mobile,” a term we use to describe services that would be extremely difficult or impossible to achieve without taking full advantage of the unique properties of the mobile ecosystem.   Truly authentic mobile business applications are still pretty rare. But services from firms such as Clari and Base CRM in customer-relationship management, and from startups such as Yoi in human resources, have elements of mobile authenticity that make them harbingers of what is to come.   Mobile-first applications, which treat mobile use cases as the priority rather than the afterthought, are certainly an improvement over traditional, “pave-the-goat-paths” ones, which simply mimic desktop functionality on mobile platforms. Mobile-first services have more elegant and intuitive user interfaces, and they take greater advantage of some of the unique properties of smartphones and tablets. These include the ability to gather data via sensors and lightweight user inputs, frequent, in-the-moment, “bite size” usage, and hyper-personalization of content and operation.   But mobile-first services still focus on optimizing existing workflows for the new platform in town rather than creating new-to-the-world functionality. They are an intermediate evolutionary stage on the road toward authentically mobile applications, which are true game-changers. The following table summarizes what makes these so powerful.   Authentically mobile applications address workflows that have been frustratingly elusive in the absence of mobile devices. Unlike mobile-first ones, they also place much greater emphasis on the collection and analysis of data, which fuel the innovative experiences they deliver. Conceiving them requires far more than just a “mobile for X” startup incubator whiteboard exercise because the business process and data being tapped were simply out of bounds in the pre-mobile world.   Many workers already have consumer applications on their phones that are authentically mobile. Uber, for instance, takes advantage of the “in-the-moment” property of mobile and leverages GPS to track people’s location. It would be ridiculous to think of developing an app such as Uber for the desktop — an acid test that we can usefully pose when assessing whether a business application is authentically mobile.   Some consumer applications with at least a partial claim to mobile authenticity have already crossed over into business markets. An example is Dropbox, whose appeal is grounded in the mobile must-have property of synchronization. It’s pretty useful on the desktop, so it fails the strict acid test for mobile authenticity. Nevertheless, its value soars when it touches other devices — something that is obvious to its millions of users. Other authentically mobile consumer apps will empower workforces in novel ways. But there is an exciting opportunity for startups to create products specifically for the corporate market with mobile authenticity at their core. The ones most likely to become multi-billion-dollar businesses will be those designed for knowledge workers. Such “carpeted floor” applications can charge higher prices and target larger markets than “linoleum floor” ones aimed at folk in warehouses, factories and hospitals.   It is still early days here, but the popularity of Slack, a workforce-collaboration tool that bills itself as “mobile native,” hints at the opportunity waiting to be tapped. Like Dropbox, Slack is a powerful tool on desktops as well as mobile, so it doesn’t pass our strict acid test. But some of its core properties, such as its “all-in-one place” hyper-integration with varied data sources and systems of record, are particularly valuable in a mobile environment. It can best be characterized as a multi-platform crossover application with an increasingly strong claim to mobile authenticity.   Business communications in general is a place where more crossover applications will emerge. Blue Jeans Network (a Wing portfolio company) already offers a service that makes it possible for employees to conduct multi-point, cross-platform videoconferences and collaborative screen shares from their mobile devices. Looking ahead, vendors may develop authentically mobile applications that make it simple for workers to, say, transfer a video session easily from a phone screen to a conference room or a desktop.   CRM is also ripe for greater mobile authenticity. Clari can send alerts to sales executives via mobile phones to ensure they have the most recent information related to a customer or a prospect before meetings. It can also prompt them to update the status of their deals on the go so that the data is captured and shared promptly. Base CRM includes geolocation functionality that maps users’ leads and contacts, and offers one-touch driving directions to get to them.   Beyond these very early examples that have at least a partial claim to mobile authenticity, the entire universe of Customer Experience Management, spanning both pre- and post-sales interactions, will be fertile ground for products that create vibrant, real-time tethers between a company’s employees and its clients.   Another cluster of authentically mobile solutions will appear in HR, which is being transformed from the disconnected, anecdotal adjunct of various Dilbert cartoons into a data-driven discipline that is tightly integrated with critical business processes. The fledgling field of “people analytics,” which measures things such as employee engagement, looks especially promising.   Yoi sets out to improve success rates in employee onboarding by providing new hires with information they need while on the move, as well as when they are at their desks. Importantly, the application also captures and quantifies data from employees in real time, feeding it back to managers who can step in swiftly to address any pain points or to speed up a learning process.   Yoi is an interesting example of how a mobile application combined with the power of data and the cloud can be used to shed more light on a rather opaque process and to improve employee-retention rates.   Other domains ripe for authentically mobile business applications range from payments to travel management and cybersecurity. As we noted earlier, some authentically mobile consumer apps will migrate into the business arena to address companies’ needs. But there is an exciting opportunity here for entrepreneurs to create new offerings designed from the ground up for companies, their customers and their workers. Plenty of shelf space is waiting to be filled in the emerging, authentically mobile application store.   来源:techcrunch.com   扫一扫,关注“HRTechChina",聆听人力资源科技的声音!
    People Analytics
    2015年05月20日
  • People Analytics
    GoSquared 推新分析工具 People Analytics,帮助企业更好地了解用户需求 就在 GoSquared 庆祝成立 9 周年之际,这家位于伦敦的“创业公司”推出了其迄今最具雄心的一款产品。GoSquared 由詹姆斯·吉尔(James Gill)、詹姆斯·泰勒(James Taylor)、基奥夫·瓦格斯塔夫(Geoff Wagstaff)在他们 14 岁时创建。   GoSquared 以 实时网页分析产品 著称,竞争对手包括 Chartbeat、Google Analytics 以及实时分析领域的其他公司。GoSquared 新产品“People Analytics”旨在解决一个更大的问题,即对传统软件即服务(SaaS)公司存储于多个独立软件上的所有用户数据进行整合,帮助他们更好地了解用户需要。   这些独立软件一般包括内部开发工具、第三方计费系统、桌面支持软件、网页分析工具以及“庞大的”客户关系管理(CRM)系统等,也就是说,若想解决某个客户问题,必须对它们每一个进行单独询问。这种做法不仅费时耗力,还要求员工必须经过一定的培训,才能使用每一个独立软件。   相比之下,PeopleAnalytics 旨在让企业客户可以从单一、直观的界面访问和搜索所有这种“用户级别”数据,最终目标则是让他们可以从“个人”层面与用户建立联系。   GoSquared 联合创始人兼首席执行官詹姆斯·吉尔说:“People Analytics 就是为了将企业所拥有的一切用户数据和信息——当前存在于众多不同工具中的信息——整合到一个地方,供团队里的每一个人轻松搜索、排序和访问。”   “其他工具一直在慢慢瓦解这种创意,也许会向你显示用户的账单历史,或是他们的在线活动,或是支持客户的历史等等。People Analytics 将这块拼图的所有部分都整合在一起,这样,你就有了一个寻找用户信息的明确地方。”   至于这一工具如何在实践中使用,吉尔举了在软件即服务领域支持客户的例子,具体而言,就是向遇到升级问题的客户伸出援手,如果没有 People Analytics,客户必定需要在不同工具上查询信息,如计费系统、内部管理员系统或第三方分析工具。   吉尔解释说:“只要搜索一下客户姓名、电子邮箱地址或用户 ID,你就能获得有关该客户的完整信息,包括他们使用过的设备,之前出现过的错误,以及他们浏览过的页面等等。”   吉尔说,PeopleAnalytics 的另一个潜在用途是产品管理。“通过过滤所有用户的信息,你可以利用 PeopleAnalytics 来解答各类问题,比如‘在所有参与测试的用户当中,有多少激活了 X 功能?’”   这样,产品经理就能知道哪些功能经常被使用,哪些功能很少被使用,因为 People Analytics 会整合单个用户的数据,所以你除了报告数据外,还能对这一数据采取行动,比如将定制产品的电子邮件发送给特定用户。   与此同时,与 GoSquared 庆祝成立 5 周年时的情况不同,23 岁的吉尔称该公司不打算举行派对,会将这种庆祝活动留到公司成立 10 周年庆典上。   GoSquared Launches People Analytics To Help Businesses Get A Better Understanding Of Customers As GoSquared celebrates its 9th birthday — James Gill, James Taylor and Geoff Wagstaff started the company when they were just 14 — the London-based ‘startup’ is launching its most ambitious product to date.   Best known for its real-time web analytics offering, which competes with Chartbeat, Google Analytics and other companies in the real-time analytics space, GoSquared’s newly released ‘People Analytics’ aims to tackle a potentially bigger problem: namely, consolidating all of the user data a typical SaaS company has stored in multiple and siloed software to help them get a much better understanding of customers.   Those silos typically span internally built tools, third-party billing systems, support desk software, web analytics, and “bulky” CRM systems, meaning that to solve a particular customer’s problem they each need to be interrogated separately. Not only is this time consuming but requires staff to have been trained to use each disparate piece of software.   Instead, People Analytics aims make all of this ‘user level’ data accessible and searchable from a single and intuitive dashboard, with the ultimate goal to enable businesses to connect with users on a “personal” level.   “People Analytics is about bringing together all the data and information you have on your users – information that currently sits in a handful of different tools – into one place, where it can be searched, queried, and easily accessed by everyone on the team,” explains GoSquared co-founder and CEO James Gill.   “Other tools have been chipping away at this idea, and perhaps show you information about a user’s billing history, or their on-site activity, or their customer support history. People Analytics pieces all of these parts of the jigsaw together so you have one definitive place to look up information on your users.”   As for how this might be utilised in practice, Gill gives the example of customer support in the Software-as-a-Service space, specifically dealing with a customer who is having trouble upgrading, which, without People Analytics, might invariably require looking up information in multiple different tools, such as a billing system, an internal admin system, or third-party analytics tool.   “Just one search for the name, email address or User ID will give you back a full profile with all the info you need on a specific user, including the device they were on, any errors they triggered, and any pages they viewed before they sent their support ticket,” he explains.   Or another potential use-case, says Gill, is product management. “By filtering your entire user base, you can use People Analytics to answer questions such as ‘out of all our trial users, how many people activated feature X?'”.   This enables product managers to know what features are heavily used (or barely used at all) and, because People Analytics includes data on individual users, you can go beyond reporting and actually take action on that data, such as sending a bespoke email to a specific list of users.   Meanwhile, unlike when GoSquared celebrated its 5th birthday, Gill says the startup isn’t planning a party and instead will wait till it reaches 10. “Boy does time fly,” adds the 23-year-old.   来源:techcrunch.com
    People Analytics
    2015年02月04日
  • People Analytics
    Google想出了一个决定人员晋升的算法,然后就没有然后了...... Prasad Setty 是 Google People Analytics 团队的副总裁。7 年前 Google 成立的这支团队的职责是收集和利用数据来支撑公司的管理实践。其使命很简单,即基于数据和分析做出所有的人事决定。在今年 10 月举行的Google re:Work大会上,Setty 介绍了这支团队用科学来进行人力资源管理的一些做法。其结论是,算法虽好,可不能滥用,人事决定终归要有人来决定。     Google 是一个由工程师成立的公司,目前也仍然由工程师统治。这家成千上万的大公司每年都要做出许多的人事决定:应该招谁?提拔谁?最好的人应该给多少薪水?通常 Google 会找 4、5 个资深工程师组成委员会,由每个委员会审查一堆提名,经过很多次的对话后做出决定。Google 的这个人员晋升评审流程相当复杂,要审查的材料和召开的会议太多,以至于连 Google 的会议室都不够用,所以要跑到附近的万豪酒店去开会。     因此,为了帮助减轻审查委员会的工作负担,早期时 People Analytics 团队开发出了一个算法来简化人员晋升的决策流程。这个算法是一个计算晋升可能性的公式,如下图所示,里面考虑了平均绩效、经理推荐以及个人推荐(Google 允许员工自我推荐)三方面的因素(各赋予不同的权重,平均绩效权重最,其次是经理推荐,最后是个人推荐)。 通过与最后的晋升结果比较发现,该算法相当可靠,后台的测试结果很好,经历过多周期后仍表现稳定,其中 30% 的提拔案例决策准确率达到了 90%。团队成员都很兴奋,以为自己因此能够节省委员会 1/3 的工作,让他们腾出时间专注于最困难的决定。     但是结果是那帮人根本不买账,不想用这个模型。因为他们不希望躲在黑箱背后,而是希望自己做出决定。因此这个算法从来都没有用来做过提拔决策。     Setty 得出的教训是人事决策必须由人来决定。不过 People Analytics 仍然可以发挥作用,即用更好的信息辅佐决策者(用模型来检验自己的决策过程),但是不能用算法来替他们做出决策。     而且,这一洞察还帮助推动了 Google 人力资源和管理的办法改进。People Analytics 在很多方面根本性的重塑了 Google 的招聘机制。比方说,现在 Google 已经不再强调 GPA(盖氏人格评估)与毕业学校,而是更看重一些软性的特质,如“谦逊”、“学习能力”等。     People Analytics 还通过数据分析总结出了伟大经理的 8 项特质: 1) 是一位好教练 2) 给团队授权,不做微观管理 3) 对团队成员的成功和个人幸福表达兴趣/担忧 4) 富有效率/结果导向 5) 好的沟通者—懂得倾听和分享 6) 帮助团队成员的职业生涯发展 7) 对团队有清晰的愿景/策略 8) 有重要的技术技能,可帮助团队提供建议     此外,Google 还在内部寻找志愿者开展长期研究,设立了许多数据点来跟踪其数十年的职业生涯中工作表现、态度、信仰、问题解决策略、面临的挑战与抗压性等。尽管尚未确定能有什么发现,但是收集数据研究肯定是利用科学方法来研究人力资源问题的第一步。     [消息来源:qz.com  文章:36氪]
    People Analytics
    2014年11月25日
  • People Analytics
    马海刚:I时代HR大数据思路与腾讯实践 作者:马海刚 本文由马海刚先生授权HRTechChina发布,转载请注明文章作者及出处 引语: HR管理经过几十年的发展,理论基础仍是工业时代的科学管理经验。近年来面对汹涌而来的移动互联网大潮以及层出不穷各种新的管理挑战,HR管理的理论和方法并没有出现相得益彰的创新内容。唯一引起了广泛关注的HR管理遇上大数据的话题,目前公开的研究也多聚焦在概念阶段,能够真正应用到企业HR管理实践的案例却不多见。本文旨在结合腾讯在HR大数据领域的探索历程,来说说大数据将如何助力HR管理升级,迎接这个崭新的时代浪潮!   I时代,传统的HR将被颠覆,你造吗? 2012年12月12日,CCTV中国经济年度人物评选颁奖现场,万达集团董事长王健林同阿里巴巴董事局主席马云给大家留下了1亿元的赌约。     一年以后同样的颁奖现场,格力电器董事长董明珠和小米科技董事长兼首席执行官雷军在另一个赌约上把赌注提升到了10亿。不同的赌约,相同的内涵——移动互联网将挑战甚至颠覆传统行业。     又一年之后的胡润富豪榜,移动互联网挤掉房地产成为前10大富豪中人数最多的行业。     前段时间,又流行起了一个段子,描述当前苦逼潮人的生活,“每天乘地铁,用小米手机,穿凡客T恤,上3W咖啡听创业讲座,在家看耶鲁大学公开课,知乎果壳关注无数,36氪每日必读,马云的创业史了如指掌,张小龙的贪嗔痴如数家珍。肉夹馍只吃西少爷,约朋友得去雕爷牛腩,喜欢Kindle胜过iPad,手机里没游戏全是GTD的APP。”     实际上,在我看来,这些无一不是因为我们正在迎来人类发展史上一个在量级上可以同工业时代相媲美,但在理念上却与工业时代完全相悖的新时代,我把这个时代简称叫I时代。     起名叫I时代的缘由,是因为我认为这个新时代是一个由internet(互联网)、individualism(个体主义)、innovation(颠覆式创新)聚合而成的时代。这个时代的特征完全颠覆传统经济中的大鱼吃小鱼和快鱼吃慢鱼理论,抱着传统管理思想的企业和管理者将不断面临着生死考验。     这已经是个革命者层出不穷的时代,很多企业通过颠覆式思维,利用先进的技术和跨界的创新,使事情变得更简单,从而完成弯道超越老牌大企业,实现令人难以至信的突破式增长。这也是个更加注重情感链接和用户感观的时代,了解人性、捕获人心成了商业的制胜法宝,也成为管理上的核心要素。     今天的我们已经处在这样一个经济、社会与技术的大变革之中,面临这场变革大潮,HR将会遇到什么样的挑战?我个人的总结是:挑战很多,有两点最关键。     挑战一:I时代下HR管理的理论基础——管理科学将被重新定义。 在这点上我非常认同中国工程院工程管理学部副主任郭重庆院士的观点:“传统的管理将被颠覆,……从管理学界来看,是历史难得的大机遇,大数据是最接近映射真实世界的手段,云计算是社会化配置的计算服务工具,以及无所不在的互联网,开启了管理科学研究的新范式,是管理科学发展史上最接近现代科学的一次机遇,……”在传统的管理被颠覆之际,HR管理是不可能独善其身的。     挑战二:从工业时代过渡到I时代,HR管理研究的方向发生了变化。 通过对比可以发现工业时代和移动互联网时代在组织和人的研究方向上不仅是不同,甚至是完全相悖。 在迎接移动互联网的过程中,HR管理的变革在所难免,那么,我们又该如何应对?     HR,老板喊你转型升级了! 有一点可以确定的是:传统的HR管理已经无法满足变革时代的需求。新时代的HR管理需要转型升级,而转型升级的重点,我建议从三个层面着手:     一是HR组织模式的升级:需要改革传统的按照“选育用留”这种功能模块设置的HR组织模式,打造由COE(Centers of Experts)、BP(Business Partner)、SDC(Shared Delivery Center)共同组成的三支柱模式,提升HR对战略的驱动力,对业务的支撑力,以及对员工的影响力,让HR真正成为企业的变革推动者、领导者、业务伙伴和HR业务专家。     二是HR信息化的升级:HR信息化的目标将不再仅仅是信息化办公或者提升工作效率,而是通过移动端、云、BI等新技术的使用,打造成能够有效连接COE、BP、SDC以及HR所服务的管理者和员工的信息高速公路,促成HR管理的颠覆性创新。     三是HR数据能力的升级,这也是本文我的阐述重点。 当我们所处的环境都被数据化以后,管理决策所依赖的将更多的是数据而不是经验。这也要求HR的数据能力不再是传统的数据统计,而是包括了数据的分析、挖掘、建模、训练、验证、管理改进等一系列的完整活动。类似于谷歌的People Analytics团队、腾讯的活力实验室、人平数据哥这类研究HR的大数据应用的团队将会出现在越来越多企业的HR队伍中,并发挥越来越重要的作用。     如果将转型升级后的HR管理体系想象成一个智能机器,那么组织模式就是机体,信息化是连接机体各个部位的神经网络,大数据就是“大脑”,这三者相辅相成,缺一不可。     那么问题来了,HR大数据挖掘技术哪家强? 搜索一下“HR+大数据”,可以轻松得到几百万条记录,可见大数据在HR领域并不是一个陌生的话题,遗憾的是,热度有余而深度不足。北大光华的穆胜博士在其写的《大数据为何走不进人力资源管理?》一文中提出“HR可能误会了大数据”,这一点我也是比较认同的。HR的大数据需要有自己的玩法,其不同于传统的HR数据分析的功能可以概括为三个方面:     一是养成平台的能力:大数据的特征概括为4V,Volume(大量)、Velocity(高速)、Variety(多样性)、veracity(真实性)。这也决定HR的大数据绝不仅仅是把一些数据拿过来分析,而是一个涵盖数据的产生、存储、抓取、清理、分析、挖掘、建模、训练、验证、呈现的全过程的综合平台。     二是要有连接的效能:与传统的数据分析只需要得出一个数据性的管理结论不同,HR的大数据分析包括了提出概念、分析框架、数据准备、数据清理、数据挖掘、模型创建、训练验证以及管理行动,其过程充分卷入了HR三支柱的COE、BP和SDC,乃至于管理者和员工,其目标是推动HR管理的持续改善。     三是能够牵引HR的方向:传统的数据分析多是事后的总结,是一种滞后的管理。而HR的大数据分析则要求能够帮助HR进行预测,实现前置的管理。     例如传统的人力资源通过绩效管理来识别高绩效的员工并帮助员工持续提升绩效,而在大数据模式下的思路则是通过数据的挖掘找到高绩效员工的特征要素,让企业的每一个员工都能够持续产生高绩效。     由于多数企业在HR的数据领域缺乏规划,要实现上述突破对HR部门而言将是一个漫长而艰难的过程。     HR大数据领域腾讯的实践与探索 腾讯在HR领域的大数据实践最早可以追溯到2012年,通过People Soft搭建起了HR的统一结果库,并开展了第一期的数据清理工作。     而完整意义上的HR大数据体系探索则到了2014年初,在SDC内部成立了HR大数据团队。这里我将从平台建设、连接效能和方向牵引这三个方面简单介绍我们在HR大数据领域的探索经验,希望能够给同样在研究HR的大数据的HR同行们带来思想碰撞的火花。     一、腾讯的HR大数据平台由应用层、功能层以及团队三个部分组成。 1、应用层主要解决HR大数据如何支撑HR业务的问题,阐述的是大数据的应用场景,以及需求如何被响应和落地(如下图所示)。 2、功能层主要解决HR大数据在后台如何运作的问题,阐述的是如何去科学的管理和使用数据,保障数据的质量和价值,包括元数据管理、数据质量管理和逻辑建模规划三大核心模块。     3、从应用层和功能层我们可以看到HR的大数据涉及了HR专业以外的IT系统、数据库、数据分析、产品设计等多个专业,这也意味着仅凭专业的HR是无法搭建起HR的大数据平台的。     以腾讯SDC的大数据团队为例,其成员由SSC、E-HR、区域中心的员工共同组成,是一个拥有人力资源、HR信息化、数据库、HR咨询复合工作经验和背景的团队。     二、在连接效能上我以我们正在开展的某项目举例。 该项目由COE最先提出概念,先后卷入SDC和BP,执行迅速成立了项目联合团队。     其中COE团队负责政策、资源的协调以及专业方向的把控,BP团队负责模型验证以及落地研究,SDC团队则负责数据清理、质量建设、特征挖掘以及模型的搭建和训练。     在这个项目中,不仅COE、BP和SDC的人被连接起来,同时连接的还有对应的“事”和“信息”。     三、在牵引HR的方向上我以腾讯社招候选人稳定性分析为例。 传统的HR数据分析会围绕离职率展开分析,而在HR的大数据分析中则是将腾讯历史上所有的员工按照稳定程度分成多个样本,通过数据的挖掘找到与稳定性相关的典型特征,建立起能够识别候选人稳定性的数学模型。     其目标之一是希望通过应聘者的简历自动对其稳定性给出评估建议,也为后续招聘以及保留环节提供参考。     在此,还有几点建议给到准备进行HR大数据探索的同行们: 一、从现在开始,夯实数据基础。 以腾讯的某个HR大数据项目为例,一次调用的数据就超过了600万条,400多个字段,一般的PC机以及excel、spss等工具都无法支撑此种量级的数据挖掘,但是其量级又达不到使用TDW的程度,加上数据敏感性等诸多因素,最终发现需要搭建用于HR大数据分析的服务器。     二、数据质量决定数据的价值。 涂子沛在《大数据》一书中用了整整一个章节来阐述数据质量,足见数据质量的重要性。在此我想用一句话来补充说明:在一堆错误的数据中,你能指望得出正确的分析结果吗?     三、是挖掘数据而不是统计数据。 仅从统计学的方法上看就可以看到差别,传统的HR数据分析用的最多的统计方法就是描述统计、箱型图等。     但是到了HR的大数据分析,相关性分析、方差分析、回归分析、聚类分析、决策树模型等用的会更多。其原因就像维克托.迈尔-舍恩伯格在其《大数据时代》中强调的,大数据研究的“不是因果关系,而是相关关系。”     对于企业的HR而言,当HR遇上大数据,我们更应该抓住这个机会,在大数据平台能力,连接的效能,牵引HR方向这三方面寻求突破,进行创新性的研究和探索,提升HR之于企业的价值和影响力。     最后,借用狄更斯的名言“It was the best of times, it was the worst of times”,I时代带给HR的不仅仅有挑战,同样也有机会。     亦如郭重庆院士所言,“管理学界应该抓住这个机会,实现自己的历史使命和担当。”
    People Analytics
    2014年10月30日
  • People 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.
    People Analytics
    2014年05月17日