Pic
HR Transformation
Bright, Shiny Objects and the Future of HR

Many of us have had the experience of listening to a talk and suddenly making a connection between the speaker’s big idea and a challenge we face at work. To listen to David Rock, of the NeuroLeadership Institute, for example, is to have one’s eyes opened to recent neuroscience research. One discovery Rock shares is that when people realize they are being compared with others, a “threat response” in their brains sends cortisol levels skyrocketing and makes it hard for them to take in other information. If you oversee your company’s annual performance review process and it centers on the delivery of a single number derived from a stack-ranking exercise, this insight could be a lightbulb going on.

Maybe you’re listening to Rob Cross, of the University of Virginia, revealing that your company runs according to a hidden structure that looks nothing like its official org chart. Informal networks matter much more than hierarchies. Whatever the source, you find yourself doing what so many HR leaders have done before. You grab that bright, shiny object and take it home, in the form of pages of excitedly scrawled notes and an intense resolve to get your team working on it.

Is this a bad thing? We’re inclined to say that the opposite reaction—sitting with arms crossed, impervious to any provocative ideas—would be far worse. But such enthusiasm does present challenges. Applying any big new idea will change how some aspect of HR is managed, and that aspect is connected to all the others in a larger system.

In this article we will describe how Juniper Networks, a Silicon Valley–based innovator of high-performance networking technology, tackles those challenges. Over the past six years the company’s HR team has adopted an approach whereby it can tap into the latest research and thinking and apply it in unexpected contexts. It’s a loose, four-part process, which we’ll outline below. But first we want to share a valuable lesson we’ve learned about cultivating such constant evolution and innovation. Before Juniper could figure out which solutions were right—much less how to apply them—it had to adopt a certain mindset.

HR needed to figure out how its initiatives and activities could yield a talent pool that was better prepared and empowered to innovate.

Fall in Love with the Problem, Not the Solution

As leaders, we have ready access to potentially powerful, game-changing ideas. It’s easy—and tempting—to chase after a new practice, a new expert, or new research that seems to provide some relief or a solution to a problem. What’s harder, but far more valuable, is to fall in love with the problem. Then you aren’t quite so eager to embrace the first possible solution and move on. You spend some time letting the challenge soak in, studying it from various angles, and understanding it more deeply. Rather than hastening to narrow the scope of your decision and the options under consideration, you remain receptive to additional, possibly better ones. For example, Juniper’s David Rock moment didn’t end with a workshop or a separate initiative. Brain science fueled the company’s understanding of an important problem—one tied to values and talent.

In 2009 Juniper’s top managers had called for a renewed focus on values and culture as a differentiator. They sought advice from Ann Rhoades, who had done much work along these lines at Southwest Airlines and then at JetBlue. (She later wrote about her work with Juniper in Built on Values: Creating an Enviable Culture That Outperforms the Competition.) One outgrowth of that effort was a program of “trio tours”—a total of 75 sessions with three senior executives at various company locations to connect with local talent in thoughtful discussions of Juniper’s culture.

During one session in Bangalore, a young engineer decided to speak his mind. The topic he put on the table was the company’s use of forced performance rankings. He felt that it was demoralizing and effectively pitted colleagues against one another in a zero-sum game. How did that jibe with Juniper’s espoused values of authenticity and trust? How did it support a culture of innovation? Unquestionably, his bluntness was challenging. But he raised a problem worth digging into: How should a company do performance management if it really believes in its talent and wants to raise everyone’s game? And why would a company that seeks to differentiate itself through talent use the same performance evaluation tool everyone else does?

This was the problem Juniper was considering when neuroscience started to edge its way into the business world. Rock’s research clarified why forced rankings were undermining the desired culture of trust, collaboration, and risk taking. It provided another angle for exploring the complexities of culture, values, and talent systems.

In 2011 Juniper became one of the first global companies to abolish forced rankings. Rather than spreading people across a bell curve, it now seeks simply to have what it calls Best Talent. It has replaced annual reviews with frequent “conversation days” for the purpose of discussing areas for improvement, goals, and career aspirations. Today more than 97% of its employees are considered Best Talent, and Juniper’s talent management efforts focus on putting the right capabilities in the right places to achieve its business goals. The Bangalore engineer was absolutely right, but his insight required more than a rush to a solution. The problem had to be viewed differently.

Juniper’s leaders avoid knee-jerk reactions and instead hold out for bigger ideas and underlying principles. They home in on what’s pivotal—where change will have the greatest impact. They hired Chris Ernst, David Gonzalez, and Courtney Harrison, of the Center for Creative Leadership, and built an HR team committed to radically rethinking HR. The team’s members collectively immerse themselves in challenges—even those that don’t appear to be HR problems or that seem too big to solve.

Falling in love with the problem rather than the solution makes it possible to avoid shiny-object syndrome, unconnected programs, and random HR innovation. Within that overall mindset, we believe, the right approach consists of a four-part process.

1. Get the Big Picture

How do HR leaders decide which few, pivotal solutions to adopt and then successfully integrate them into the organization? First, they need a reliable way to discern the big picture—the conditions and business imperatives that create the context for their choices.

Six years ago that big picture was coming into focus at Juniper after something of an identity crisis. As a start-up, the company had revolutionized the computer network industry with the M40 router, which played an essential role in scaling the internet to where it is today. Juniper grew rapidly, expanded its offerings, and was flush with success. Even so, it struggled with being “stuck in the middle”—tiny in comparison with its closest competitor, but bigger and more diversified than the single-solution niche players. It couldn’t compete with a rival that was able to throw 10 times as much money, time, and personnel at any problem. And unlike the smaller companies, Juniper had already banked on offering end-to-end solutions. To reinvent its business strategy and grow, it would have to innovate. So HR needed to figure out how its initiatives and activities could yield a talent pool that was better prepared and empowered to do so. That meant innovation within the function as well.

A great example is the initiative Juniper undertook a few years later to refresh its understanding of the big picture. It sounded like madness to some at the time, but the HR team resolved to have one-on-one conversations with every senior leader of the company (a total of 150 executives), including the chairman, and with 100 other managers around the world. The conversations would include questions such as What are the key external environmental challenges currently facing Juniper? How are they affecting you and your team specifically? What excites you most about Juniper’s business strategy and its execution? What concerns you most? Of course, the risk was that HR would hear about a lot of people issues for which it had no ready solutions.

Uncomfortable truths did surface. Juniper had too many silos and too many priorities. It was top-heavy and conflict avoidant. It made the work overly complex where it should have been operating more interdependently to provide solutions for customers.

That initiative paid off in ways that go well beyond leadership development and performance management. The findings inspired Juniper to rip out business units, break down P&Ls, and integrate in ways it had never done before. Now it has the simplest operating model in its history. Across the company, six people can get together and make any decision. Product lines have been streamlined too. Previously, for instance, numerous resources were tied to multiple routers, switches, and security products. Now a common resource strategy spans the product road map, saving the company millions of dollars and untold headaches.

2. Spot the Valuable Insight

Deep understanding of the business allows HR leaders to pursue the second part of the process: Seek out—and take in—the latest and greatest management ideas and connect them to what is pivotal in the organization. Tying together values, performance assessment, and neuroscience is one example in Juniper’s experience. Another is improving customer service by analyzing an organizational network—and this effort put HR directly into the operations of the business.

A series of communication problems with a key customer had resulted in missteps and quality concerns. The obvious, traditional solution might have been to focus on the salespeople who met the customer. Instead the HR team reflected on an intriguing line of research: the idea that organizations are networks, not just hierarchies and business units. That exposed the problem as a lack of sufficient collaboration across units and functions. It wouldn’t be effective to encourage everyone in the organization to be slightly more collaborative; collaboration had to be seriously enhanced in the few spots where it would make a crucial difference.

Rob Cross was recruited to conduct an organizational network analysis. He and the HR team began by looking for employees who were involved in any way with the customer account. They spoke with a few dozen key people and identified 344 such employees. “Impossible,” said the EVP sponsoring the project. “There cannot be 344 people working on a single account.”

So the team did a formal network analysis, after which it told the executive he was partly right: The account didn’t have 344 people working on it—it had 920. In other words, almost 10% of Juniper’s employees had some involvement in getting the job done well for that big customer. It’s no wonder that lines were getting crossed. The network analysis brought hard (albeit uncomfortable) evidence to support an observation often made by Juniper’s founder, Pradeep Sindhu: “Silos rob networks of their inherent value.”

Since then Juniper has learned to think about natural organizational networks as crucial components of how it works. But it’s what the company does with a network analysis that makes a difference. Later, for example, with a different account team, it went beyond simply describing the informal network to learning how to optimize it. HR served as an embedded adviser to the account team, conducting weekly sessions to systematically apply the network analysis findings and concepts in team planning, development, and information sharing. The team began to operate across the internal network, bringing in expertise faster, clarifying decision rights, and eliminating power or information bottlenecks. The account relationship went from being closely held by a few to involving open communication among functions and levels in both organizations. The customer is now Juniper’s largest in the Europe, Middle East, and Africa region, delivering 135% of expected revenue in 2014 and with stellar customer service results.

This sounds like a story in which a new idea was spotted and introduced to an organization. Its more subtle lesson is that although a new concept may be broadly useful, it will get the most traction if you think beyond its obvious appeal. Understand the research. Look at the evidence. Then dig more. You’ll better see how to translate the idea to your context.

3. Apply with Care

Next comes sensitively integrating the insight with other initiatives already under way. Most important here is that major HR innovations must be purpose built. Juniper has explicitly moved away from a “best practice” approach. Instead it strips a promising practice down to its kernel of insight and then expands that insight into work experiences that are right for the company’s unique climate, brand, and business objectives. This allows and requires the application to have impact in connection with other components, leading to a greater payoff.

Prototyping the application of an idea in some fertile area of the organization is a valuable way of working out the necessary synergies. It also offers proof of concept through experience. You can’t just tell people about a great idea and expect them to pick it up and run with it; they need to see and experience its value. HR plays a big part in creating such experiences. One example of how Juniper put its own stamp on a research idea lies in the area of boundary-spanning leadership.

Rooted in research from the Center for Creative Leadership, the concept of boundary spanning—which reframes common barriers (horizontal, vertical, stakeholder, demographic, geographic) as places for opportunity and innovation—was introduced at Juniper by Chris Ernst. However, the ability to lead and collaborate across boundaries doesn’t come naturally in today’s siloed and internally competitive organizations.

To more deeply explore the implications of boundary spanning, Juniper decided to bring together 85 people from engineering, sales, and operations who had differing roles, levels, locations, and backgrounds. The focal point for the gathering was a Juniper Innovation Challenge: The participants would spend three days collaborating in San Francisco with the explicit goal of hatching new product ideas. But the company had another agenda, which it made no attempt to hide: These people would be immersed in an experience that might reveal how boundary-spanning leadership connects to the problem Juniper loves most—the need for breakthrough innovation.

That purpose-built network and energizing experience changed the thinking of a small cross-section of Juniper employees about their ability to innovate. Out of it came a product that was prototyped within six months and is today being tested in production environments in more than half a dozen large companies. Of course not every HR initiative or project will lead to a clear and tangible business win and a proof-of-concept experience. But that Innovation Challenge showed how valuable it is to put early applications of a new idea in service to already recognized priorities and try them out on a manageable scale that will generate learning quickly.

Ultimately, the broader dissemination of any concept will call for integration. The amalgam of ideas to which you commit must have integrity as a system, with no elements working at cross-purposes and everything guided by the same sensibility and vision. If the ideas you’ve introduced are well integrated, you’ll know—because they will begin to connect and amplify one another in unforeseen ways.

When Rami Rahim was named CEO, late in 2014, he set the expectation in his first 30 days that the Juniper Way (the company’s values and related behaviors) would return to the front seat. The concepts of informal networks and boundary spanning had already taken hold, so rather than relaunching the Juniper Way top-down, the company turned to a subset of “connectors”—informal influencers—who’d been discovered through organizational network analysis. Rahim asked them to work together to redefine the values in simple language and in terms of observable behaviors and then develop that understanding across the company. HR’s collection of applied ideas revealed itself in that moment as a well-integrated system.

4. Aim for Business Impact

The similarity of HR metrics between organizations with very different strategies is amazing—especially when you consider how powerfully the choice of what to measure can drive behaviors. In our view, if you intentionally rethink your HR function, you will also need to rethink how you measure progress and impact.

In all its measurement efforts, HR must ensure that it keeps people’s focus on what is most pivotal for the business. Assessing pivotal impact is a critical step toward further progress. Measurement becomes a forward-looking learning and improvement process rather than a backward-looking declaration of triumph or failure. Metrics and signals along the way tell you what’s working and what isn’t, where to recalibrate or ask more questions. You need the mindset—and the stomach—for experimentation, revision, and occasional missteps.

If your new initiatives are well applied, they may suggest and enable important new metrics. For example, after Juniper’s network and boundary-spanning efforts had identified the nearly 5% of employees who operate as connectors, the company had the basis for an important new metric: Those people are flagged on the dashboard, so any attrition in the group will spark an investigation of the cause.

Understanding progress may also mean looking at available data through a different lens. Pay attention to business measures such as time-to-revenue of new products, cycle times, product introductions, and quality metrics. Factor them into your talent processes, compensation systems, or learning objectives—and interpret them through your big themes or pivot points. At Juniper, the level of participation in the 401(k) plan can be seen as a proxy for how people feel about the company—an indicator of culture and values. For HR, the fact that 87% of employees now participate in the plan—one of the highest participation rates among high-tech companies—is a signal that people are committed to future growth and believe that they and their colleagues can make it happen.

The precise ROI of an important new idea in HR is impossible to measure. That doesn’t mean that HR can’t make its case, or that you can’t observe the idea’s impact. You can map the logical connections between an effective HR initiative and desired business outcomes. Focus on what can be measured along that path, and extrapolate where you can’t measure precisely. That’s exactly what every other management discipline does.

Juniper has undergone significant changes and challenges—including three CEOs in three years. In 2014 it sent out an employee survey designed around three key questions: Do you know our strategy? Do you understand your role in executing that strategy? Do you feel inspired and accountable to help the company achieve it? None of those questions got even 50% affirmative responses, so the company created a road show for executives to share the granular details of its strategy with teams around the world. Then the connectors were empowered to engage their peers in small-group strategy conversations across the company. Four months after the initial survey, the results of a second survey put all three indicators above 80%.

Different by Design

Developing a reputation as an innovative HR organization requires walking a fine line. You are not an R&D facility or a university; you do not employ a large cadre of social science researchers and data scientists. Your ideas for innovation will often arise from popular talks and articles. Embrace too many of those, however, or apply them too superficially, and you’ll develop a reputation for fad surfing. Dig beneath the surface to the fundamental scientific research and insights, and you can set the stage for true impact.

Failing to innovate is not an option, so it’s important to have a specific approach for responsibly bringing new ideas to the organization. Juniper’s method—getting the big picture, seizing on insights, applying them wisely, and ensuring their impact—may be useful to you. It has enabled this company to move from one-off programs and unconnected experiments to a system that is always evolving in exciting and consistently business-aligned ways.

There’s nothing wrong with being attracted to the bright, shiny objects of a thriving thought leadership industry. They offer new solutions and, at the least, inspire you to revisit your assumptions. The key is to maintain your long-term love affair with the problems you need to solve and the business you are here to serve. You’ll know you’ve struck the right balance when your HR programs start to look less and less like your competitors’ and contribute more and more to your competitive distinction—when every year makes you more different by design.


Pic
HR/People/Workforce Analytics
HR Business Partners should become HR Analytics 'Translators' - interview with Luk Smeyers

Question: Luk, you have recently attended HR Tech World Congress in Paris. What are some of the things that stood out?

LS: I had two extremely busy and informative days at the event. One of the things that I can highlight are all the fantastic technological evolutions represented by an array of vendors. While I came across many exciting and interesting solutions and spoke to many representatives, I hardly heard anything from them about one of the biggest issues in my area of analytics: the role of the coordinator in an analytics project.

And this is important. The HR function often fills this position with an HR business partner, at least in my personal experience (see our article 'HR Analytics Learnings from 2014'), however such coordination does not come easy.

Question: Could you tell us more about the role of such a coordinator?

LS: Absolutely. I came across a study by McKinsey recently, which discusses the indispensable part of a analytical coordinator or a ‘translator’ as the report called it. This is someone who is able to bridge the gap between data, analysis and decision-making.

According to this study, the translator turns quants’ analytical outcomes into actionable insights which the management is able to work with. An über-difficult task, I can tell you. Of course companies can choose to outsource the analysis as such but never the translator’s role.

Question: So what do you think makes a “good HR analytics translator”? 

LS: Well, in ‘new-school’ organisations like Google, translators are often professionals with a thorough background in consultancy. The classic HR business partners I tend to encounter in projects on the other hand, are, with all due respect, not often ‘the Google type’.

These HR business partners are expected to have 4 important skills:

  1. Analytical Acumen: First, they should possess a strong analytical acumen, which is not the same as being able to analyse!
  2. Understanding of the business: Second, this person must have a thorough understanding of the business since the whole point of analytics is to add value to the business.
  3. Consulting skills: Next, the translator has to have strong consulting skills, meaning he or she is able to make a business case for analytics projects.
  4. Cross-functional project management skills: And finally, this person should be able to steer and coordinate analytics projects and collaborate cross-functionally.

Question: What is the biggest challenge these translators face? 

LS: First, they have to conquer HR’s inherent fear of analytics. Most HR people didn’t come to HR for the figures after all. In addition, the translator has to take on another, stubborn, phenomenon, which is the almost always politicised access to data in the organisation. Another thing we didn’t hear much about at the event.

Question: You mentioned HR’s fear of analytics. Could you elaborate?

LS: Of course. Harvard recently dedicated a whole article to the subject of the fear of data, with a call to action by its author Thomas Redman. He writes that more and more managers and their employees have the feeling that sooner or later, data will infiltrate every nook and cranny of their organisation or department, which will transform the work they do and ultimately change working relationships and power structures.

What that means is that they increasingly adopt a wait-and-see attitude and are extra careful not to give others the ownership over the data in their projects. This is a double-ditch for HR to cross in terms of both their almost natural aversion to analysis and many managers’ wait-and-see attitude when it comes to data. 

Question: So this is where the translator comes in?

LS: Exactly! Against the background of this dual issue (natural aversion to analysis & managers' wait-and-see attitude), we expect from the HR business partner that - as real translators - they display strong analytical leadership:

  • Embracing data-driven decision-making and
  • Inspiring everyone to do the same.

Fortunately I’ve encountered some real translators in Paris. Wonderfully inspiring!

Thank you for sharing, Luk!


Pic
HR Transformation
How AI Is Changing Human Resources

Making a good impression with a prospective employer often requires little more than a great résumé and congenial personality. But how do you impress an algorithm? That’s the question facing applicants of Facebook, IBM, and a spate of other companies that are starting to incorporate artificial intelligence into their hiring practices. They’re using machines to scan work samples, parse social media posts, and analyze facial expressions on behalf of HR managers. Such practices raise questions about accuracy and privacy, but proponents argue that harnessing AI for hiring could lead to more diverse, empathetic, and dynamic workplaces.

Though traditional personality-assessment techniques, such as the Myers–Briggs test, are designed for objectivity, somewhere along the way “managers still inject personal bias,” says Mark Newman, founder and CEO of HireVue, a recruiting-technology company. That’s where machines can act as a check. HireVue records and analyzes interviews, noting things such as facial expressions and word choice to provide its clients (including Hilton Worldwide and GE) with feedback on a candidate’s levels of engagement, motivation, and empathy. Koru, another human resources software developer, also gauges personal attributes, using a written test to evaluate “impact skills,” such as grit, curiosity, and polish. Koru compares candidates’ results to those of a client’s top staff performers to identify those most likely to excel at the company.

But recruitment isn’t just about discovering the best people—it’s about eliminating the worst. Fama, founded in 2015, uses natural-language processing to conduct automated web searches on a candidate, scanning news coverage, blogs, and even a person’s public social media history for signs of bigotry, violence, sexual content, and illegal drug use. It can also look for indicators of positive attributes, such as volunteering.

Artificial intelligence can even be used to check for skills specific to certain jobs. The year-old company Interviewed, which has worked with clients such as Instacart and IBM, administers “blind auditions” in which applicants for customer-service jobs field chats or calls from bots that represent consumers. It’s now beginning to automate the assessment of what cofounder Chris Bakke describes as “softer skills,” by using computerized analysis to identify speech patterns among, for example, empathetic individuals. An algorithm’s ability to understand something like empathy, Bakke says, points to a new hiring technique—one in which machines assess, but humans make the final call.



Pic
Future of Work and Technology
The Complete Guide To The 5 Types Of Organizational Structures For The Future Of Work

Over the past few weeks I've been writing about various types of organizational structures that either already exist in today’s business landscape or are starting to emerge as viable options for the future of work. I explore each of these structures and concepts in my book The Future of Work: Attract New Talent, Build Better Leaders, and Create a Competitive Organization. However, I've been going through each one of these in detail and you can see the 5 part series with links below:

·       The traditional hierarchy (click to read)

·       Flatter organizations (click to read)

·       Flat organizations (click to read)

·       Flatarchies (click to read)

·       Holacratic organizations (click to read)

Here's a brief overview of the five types of structures as well as a handy visual the shows you the actual structures of each.

The 5 Types of Organizational Structure - image01(07-06-15)Traditional hierarchy

There are many challenges with this model but to name a few. Communication typically flows from the top to the bottom which means innovation stagnates, engagement suffers, and collaboration is virtually non-existent. This type of environment is riddled with bureaucracy and is extremely sluggish. This is why the hierarchy is perhaps the biggest vulnerability for any organization still employing it. It opens up the doors for competitors and new incumbents to quickly take over. There is also no focus on the employee experience in this type of a structure and as organizations around the world are exploring alternative organizational models, those still stuck with the hierarchy are going to have one heck of a time trying to attract and retain top talent. This is the model I firmly believe is on its way out of the world of work and will be replaced by one of the models below.


Flatter organizations

Unlike the traditional hierarchy which typically sees one way communication and everyone at the top with all the information and power; a “flatter” structure seeks to open up the lines of communication and collaboration while removing layers within the organization. As you can see there are fewer layers and that arrows point both ways. Obviously a very simplified way to look at this type of a company but hopefully it gets the point across. For larger organizations this is the most practical, scalable, and logical approach to deploy across an entire company. This is the model that most large (and many mid-size) organizations around the world are moving towards. It’s true, some form of hierarchy still does exist within this model but that isn’t necessarily a bad thing in this case. In flatter companies there is still a strong focus on communication and collaboration, improving the employee experience, challenging the status quo around traditional management models, and the like. But instead of completely reinventing the entire company and introducing a radical new structure and approach to work, it achieves similar results in far shorter term and with much less effort and resource allocation. 


Flat organizations

Unlike any other corporate structure that exists, flat companies are exactly that…flat. Meaning there are usually no job titles, seniority, managers, or executives. Everyone is seen as equal. Flat organizations are also oftentimes called or referred to as self-managed organizations (there can be some differences but for our case we will put them together). The most famous example of this comes from Valve, the gaming company responsible for classics such as Half-Life, Counter-Strike, Portal, and many others. At Valve there are no job titles and nobody tells you what to work on. Instead all the employees at Valve can see what projects are being worked on and can join whichever project they want. If an employee wants to start their own project then they are responsible for securing funding and building their team. For some this sounds like a dream for others, their worst nightmare.  I don’t see this as something that is practical or scalable for larger organizations when we think about the future of work. Smaller and some medium size companies might be able to operate in this type of an environment but when you get to organizations with thousands of employees then it becomes challenging.


Flatarchies

Somewhere in between hierarchies and flat organizations lie flatarchies. These types of companies are a little bit of both structures. They can be more hierarchical and then have ad-hoc teams for flat structures or they can have flat structures and form ad-hoc teams that are more structured in nature. Organizations with this type of structure are very dynamic in nature and can be thought of a bit more like an amoeba without a constant structure. This type of a structure can work within any type of company large or small. However a flatarchy is to be thought of as a more temporary structure which creates isolated pockets of new structures when needed, such as in the case of developing a new product or service. This is starting to become more common as organizations around the world invest more time and money into creating innovation programs that look beyond a set R&D department. It’s not hard to imagine having a permanent structure as a “flatter organization” which then gives employees the opportunity to create special teams when needed. This model is quite powerful yet also more disruptive than the other structures explored. The main benefit here is the focus on innovation which is quite a strong competitive advantage in the future of work.


Holacratic organizations

One of the things I've always said about holacracy is that I believe there are ways to achieve some of the desired effects without having to go through such a radical change. It's sort of like trying to improve the way your car runs by taking out the entire engine and rebuilding it instead of working on some of the core areas that might really drive performance. Sometimes ripping out the engine and starting from scratch isn't always as an option, especially as the car is moving, like most organizations always are. My opinion is that holacracy can be more viable for smaller or medium size organizations or perhaps larger organizations that have started off with holacracy as their base operating model. However, it's very hard for me to imagine a large organization with tens or hundreds of thousands of employees around the world implementing something like this. Holacracy is still very much an emerging structure with a lot of inserting concepts but we still need more case studies and examples over a longer period of time. Zappos will perhaps give us the best look at what a transformation to holacracy can look like, but I suspect we will need to wait another 2 years to really get a sense of the impact

 


Pic
Employee Engagement/Experience
The Future of Work: How Artificial Intelligence Will Transform The Employee Experience

Artificial Intelligence is on the verge of penetrating every major industry from healthcare to advertising, transportation, finance, legal, education, and now inside the workplace. Many of us may have already interacted with a chatbot (defined as an automated, yet personalized, conversation between software and human users) whether it’s on Facebook Messenger to book a hotel room or ordering flowers through 1-800 flowers. According to Facebook Vice President, David Marcus, there are now more than 100,000 chatbots on the Facebook Messenger platform, up from 33,000 in 2016.


As we increase the usage of chatbots in our personal lives, we will expect to use them in the workplace to assist us with things like finding new jobs, answering frequently asked HR related questions or even receiving coaching and mentoring.  Chatbots digitize HR processes and enable employees to access HR solutions from anywhere. Using artificial intelligence in HR will create a more seamless employee experience, one that is nimbler and more user driven.


Artificial Intelligence Will Transform The Employee Experience

As I detailed in my column, The Intersection of Artificial Intelligence and Human Resources, HR leaders are beginning to pilot AI to deliver greater value to the organization by using chatbots for recruiting, employee service, employee development and coaching. A recent survey of 350 HR leaders conducted by ServiceNowfinds 92% of HR leaders agree that the future of providing an enhanced level of employee service will include chatbots. In fact, you can think of a chatbot as your newest HR team member, one that allows employees to easily retrieve answers to frequently asked questions. According to the ServiceNow survey, more than two thirds of HR leaders believe employees are comfortable accessing chatbots to get the information they need, at the time they need it. The type of questions HR leaders believe employees are comfortable using a chatbot for range from the mundane and factual ones; such as how much paid time off do I have left, to the more personal ones; such as how do I report a sexual misconduct experience. The comfort level with using AI to answer employee inquiries is shown in Figure 1:

/var/folders/8r/bmgv5qd15h70k2mt6w0cjlx00000gn/T/com.microsoft.Word/WebArchiveCopyPasteTempFiles/https%3A%2F%2Fblogs-images.forbes.com%2Fjeannemeister%2Ffiles%2F2017%2F11%2FFigure-1b-11-9-17.jpgFuture Workplace

According to Deepak Bharadwaj, General Manager, HR Product Line, ServiceNow "By 2020, based on the adoption of chatbots in our personal lives, I can see how penetration in the workplace could reach adoption rates of as high as 75% with employees accessing a chatbot to resolve frequently asked HR questions and access HR solutions anywhere and anytime." Bharadwaj points out how fast we are changing our behavior as consumers, given the dramatic rise of conversational AI technology and its ease of use. For example, Amazon's Alexa now has more than 15,000 “skills” (Amazon’s term for voice-based apps), nearly all of which were created in the last two years since Amazon opened Alexa to outside developers. In fact, 10,000 Alexa skills were created since fourth quarter, 2016.


As we become comfortable with chatbots in our everyday lives, we will expect to use them in the workplace.There are already a growing number of technology firms targeting HR with artificial intelligence solutions for sourcing (Textio), interviewing (MontageTalent), on-boarding (Talla), coaching (mobile Coach), social recognition (growBot), and employee service centers (ServiceNow).


Capital Group HR: On The Journey To Digital Transformation 

Artificial intelligence and chatbots are revolutionizing both the candidate and employee experience. As Diana Wong, Senior Vice President of HR at Capital Groupsays,"Technology is an enabler to delivering world-class Advisor and Investor experiences to our customers. So, we believe HR must mirror these best in class experiences by leveraging artificial intelligence for all phases of the employee life cycle from recruiting to on-boarding and developing employees."


Capital Group is piloting a number of artificial intelligence technologies in HR, from using Textio to write more effective and bias free job descriptions to using predictive analytic web based video interviewing through the MontageTalent platform. Wong believes the piloting and usage of artificial intelligence not only improves the efficiency and effectiveness of the candidate and employee experience, but also enables Capital Group to be seen as a modern employer with Millennial workers.


However, there are barriers along the journey as HR experiments with artificial intelligence. I recently spoke about the impact of artificial intelligence to a group of senior HR leaders in Milan last week. This group identified a number of barriers to using artificial intelligence in HR, namely the fear of job loss among HR team members, lack of skills to truly embrace these new technologies and the change management needed to adopt to new ways of sourcing, recruiting, and engaging employees. Wong emphasizes this when she says, "One of the critical success factors to adopting artificial intelligence for HR is the cultural orientation around change and on-going employee communications on how and why the organization is digitally transforming HR."


As HR leaders begin developing a strategy and roadmap for artificial intelligence, I believe there are five work streams to begin this digital transformation.


1. Embrace artificial intelligence by experimenting with a range of piloting a range of chatbots

Chatbots are already ubiquitous in our lives as consumers, and now they are starting to appear in the workplace. Rather than just read about themconsider embracing a productivity chatbot as your newest HR team member. There are a number of new digital virtual assistants led by AmyZoom, and Shae. Each represents new way of working using natural language processing to schedule meetings, automatically generate documents, and provide you with personalized health data. So why not have your HR team pilot these as a way to understand the power of artificial intelligence on behavior change?


2. Develop a shared vision with cross functional stakeholders of HR, IT, Real Estate, Communitarians, and Digital Transformation 

Delivering a compelling employee experience is a competitive advantage in attracting and retaining talent. Companies are realizing that transforming employee experience is not an HR initiative, rather it is a business initiative. This means senior C-level executives from HR, IT, Digital Transformation, Real Estate, and Corporate Communications need to develop one common shared vision on what a memorable and compelling employee experience is and define the elements of the employee experience over the short, medium, and long term.


3. Understand the implications of implementation on the technology roadmap

All new technology based initiatives such as using artificial intelligence in the workplace require the design of a technology roadmap outlining the short-term and long-term goals and how the organization will meet these goals. This means the cross functional team of HR, IT, and Digital Transformation will need to agree on a shared vision for employee experience and define the technology roadmap to bring this vision to reality.


4. Identify new job roles needed to fully leverage AI in HR

Adding a number of new job roles is part of the journey to transform employee experience in an organization. This starts with creation of the Head of Employee Experience role. I interviewed Mark Levy, the first Head of Employee Experience for Airbnb in a Forbes column, “The Workplace As An Experience: Three New HR Roles Emerge.” This role is responsible for bringing to life the Airbnb mission of Belong Anywhere to life by creating memorable workplace experiences which span all aspects of work space, recruitment, development, volunteer experiences, and even the menus in Aribnb facilities.  Other roles I have seen created during the implementation of an employee experience transformation include Head of Conversational Design at Capital One, which aims to create conversational interfaces for customers to access account information and complete financial tasks. GE Digital has also created the unique role of Recruiting Scrum Master. This role applies many of the scrum techniques used in software development to recruiting by breaking down the massive hiring needs into incremental and iterative steps, where the highest value hiring challenges are addressed first for the hiring manager.


5. Upskill HR team to understand the power of artificial intelligence in HR 

Build an expertise on how AI will impact HR within your team. Designate one team member to partner with your IT and Digital Transformation group to provide HR with the latest information on new AI products, services, and how other areas in the organization such as marketing or IT are embracing AI to create more compelling customer experiences.

Enterprise AI adoption is still in the early stages, but the opportunity to develop a concrete understanding of AI, its ecosystem, and the implications of augmenting new HR job roles is massive. We are only at the beginning of  this journey where artificial intelligence and chatbots transform all aspects of HR processes. What is your company doing to transform the employee experience using artificial intelligence?

Jeanne C Meister is Partner, Future Workplace and co-author of The Future Workplace Experience.



Pic
HR/People/Workforce Analytics
How HR analytics avoids being a management fad - Thomas Rasmussen and Dave Ulrich

Introduction

Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.

John W. Tukey, mathematician, 1962

Half a century later, Tukey's point is as relevant as ever. It helps explain why HR (human resources) analytics risks becoming a management fad, instead of providing powerful insights for general managers and HR leaders making key decisions about talent, incentive structures, organization design, allocation of training budget, etc. to support value creation and the business strategy. Management fads exist. Some fads become institutionalized within companies (e.g., MBO, matrix management, core competence); other fads fade (e.g., time management, zero-defects, T-groups). They are shiny new ideas that get attention but do not endure (e.g., learning organization, Japanese management, one minute manager, re-engineering). That HR analytics is one of the latest emerging fads is a paradox in itself. The promise of analytics is great: replace fads with evidence-based initiatives, data-based decision making, bridge management academia and practice, prioritize impact of HR investments, bring rigor to HR and supplement HR intuition with objectivity. Large parts of HR analytics, however, are not new and people have talked about HR metrics, utility analysis, HR scorecards, HR ROI (return on investment), personnel economics, and evidence-based management for years without a large noticeable step-change in the business impact of HR. So far the published evidence supporting the alleged value of HR analytics is actually quite slim – it is currently based more on belief than evidence, and most often published by consultants with a commercial interest in the HR analytics market, while organizations rarely share the same success stories of business impact, but typically share cases with turnover prediction (even if turnover is not an issue) or projects with a similar narrow HR focus. Rigorous analyses of loads of data on the wrong questions often have little practical value. Yet HR analytics tops most conferences this year (greatly helped by the many HR technology and consulting firms who see a major future business opportunity in selling data and statistics capabilities to a function that is short on both), and is also the dream of many management academics of how what they do finally becomes the center of the HR profession. We predict HR analytics in its current form will continue to fail to add real value to companies. We agree with those who argue that HR analytics is being taken over by other functions that are more mature in their analytics journey (in particular finance, IT, and marketing) and that this will happen sooner rather than later, but also that this is actually a good thing: HR analytics needs to evolve and transcend HR (as other functions’ analytics will need to transcend their own functional boundaries), and will only become relevant when it takes an “outside in” approach, and is taken out of HR and integrated in existing end-to-end business analytics. In this paper we highlight what is contributing to HR analytics in its existing form becoming a management fad, what can help HR analytics deliver value by being part of end-to-end analytics, and illustrate this with two cases.

What contributes to making HR analytics a management fad?

HR analytics in its current form has the risk of being a fad that fades. Here is a list of analytic pitfalls that will contribute to make it a fad:

(a)

Lack of analytics about analytics. One colleague made a vehement case that HR work required more analytics and that rigorous analytics was the wave of the future for HR. We asked him a simple question, “what is your data that suggests that analytics is critical for the future?” Some who are enamored with analytics are not using analytics to justify analytics. They are analytical hypocrites who call for analytics, but do not use analytics to justify the use of analytics.

(b)

Mean/end inversion or data fetish. Some are enamored with analytics, thinking that more data (or “Big Data”) is always better. It is not about data, but about data for informed decision-making. For example, what separates distinguished academics like Daniel Kahnemann, widely known for his work on cognitive biases and how same can distort decision-making (see his bestseller Thinking, Fast and Slow), from less distinguished colleagues in academia is not having more or “bigger” data, but having the right data (including qualitative data or other data that is not readily available), asking the right questions, and interpreting the results and implications the right way. Analytics for the sake of analytics is not helpful. Analytics too often starts with data, when it should start with business challenges (hence all the analytics cases linking survey data to turnover because the data is readily available – while it does not yield new, insightful or value adding results). HR succeeds by adding value to business decisions – by informing how to make business decisions that intervene and create business success, not just by validating existing knowledge in practice. Think of the efficiency/effectiveness discussion in HR as an analogy: HR analytics is often preoccupied with “doing things right” with an “inside-out” HR perspective (e.g. do we use the right recruitment assessments? What is the ROI of our training programs? How efficient is our onboarding?), while it may create disproportionately more value when HR analytics applies an “outside-in” perspective and “does the right things” (How do we help transform the organization's culture so we can better deal with market consolidation and expected acquisitions the next 3–5 years? How can we grow critical technical talent faster, cheaper, better than the market to realize our growth strategy in a booming market and differentiate ourselves from the competition?).

(c)

Academic mindset in a business setting. Some companies, e.g. Google, Shell, Aramco, PepsiCo, HSBC, are currently using/implementing human capital analytics as a way to bring more theory and rigor to the practice of management. One leading company in fast moving consumer goods hired some well-trained theorists and researchers who set about to predict turnover, consistent with published studies in the academic literature. After enormous effort, they were able to explain more than 70 percent of the variance in retention of human capital. But, when they shared their results, a thoughtful observer said, “so how serious is the problem of regrettable losses in the company?” The researchers responded that the company had less than 2 percent regrettable losses for the key positions and top levels. The academics who went into industry led with theory about what they had studied, not with questions about business challenges facing this company. This company was facing challenges of global market penetration, product innovation in declining markets, an activist investor who wanted to force management changes, and a culture of working within silos rather than collaboration. But, the theory based academics started their human capital work with a theory they were testing (turnover of firm specific assets), not with a deep understanding of business challenges. So even though academia and the accumulated science is an enormous resource for management practice (and an underutilized resource too), not understanding the differences between academia and practice – or academia and actionable analytics – may actually undermine the value of HR analytics. Academics like to create assumptions that allow them to test null hypothesis and offer incremental insights on theory. Business leaders face complicated problems that require integrated solutions. Academics like precision; business leaders require practical “good-enough” solutions. Academics start with theory; business leaders start with real challenges. Academics like to reflect; business leaders have to act.

(d)

HR analytics run from an HR Center-of-Expertise (CoE). Recent evidence suggests that chief human resource officers with a clear business focus are still few and far-between (and hence receive a premium on pay). Practical experience tells us that HR CoE's with an “outside-in” approach and deep business understanding are even rarer. HR analytics CoE's will often use big data to discover insights that they will “push” out to the businesses. This is a bit like shooting a gun in the air and hoping a bird flies over. Dust bowl empiricism was popular with the advent of multivariate statistics when statisticians were seeking statistical relationships without a clear theory guiding their analyses, but when analytics are push, not pull, they risk the liabilities of dust bowl empiricism and rarely yield business value. Just as Kahnemann's distinguished work was more about his focus than amount of data, impactful HR analytics is more about strategic business focus than random patterns in big data.

(e)

A journalistic approach to HR analytics. Politics and power are real phenomena in any organization. The philosopher Foucault noted that “power is knowledge,” referring to the fact that power in part decides what knowledge creation will focus on or that “history is written by the victors.” HR analytics can be misused to maintain the status quo and drive a certain agenda, i.e. when you know what story you want to tell, and you then go look for data to support same (e.g., requests to “validate the effects of our training”). Just like academia suffers from publication bias, findings showing no effect or even value-destroying effects of HR processes or initiatives are sometimes not shared. In many cases, these require substantial energy devoted to stakeholder management (but are often among the most value-adding HR analytics findings). This is similar to the findings generated by various “think-tanks,” in which the particular focus and interpretation are guided by a particular framework with the purpose of advancing particular points of view. HR should aspire to the ideal expressed by the Scottish novelist Andrew Lang in 1937: “I shall try not to use statistics as a drunken man uses lamp-posts, for support rather than for illumination.” HR analytics departments need future funding to exist, and to do that they must balance good news and bad news about the HR organization, and chose their battles. In particular, there are still several HR initiatives around that are more based on belief than evidence (one of the authors recently encountered a company that uses handwriting analysis in selection during recruitment). This is why HR analytics needs to link company specific findings to published research, and always quote what the external and independent research finds on the investigated topic. This also highlights a big difference between HR analytics and independent academic research, and the value the latter brings to the former. One positive thing that HR analytics can take from journalism is the clear storytelling – if you cannot tell your story, including implications and recommendations in one slide (regardless of study complexity and amount of data used) then the odds of getting executive buy-in are slim. Very good HR analytics work often fails because it adopts the academic communication style and loses its business audience (also at times because it wants to show all the work done, which is really not relevant to share – effort really does not give you any points, only results and insights count).

Our suggestions for moving HR analytics from fad to an ongoing part of management decision-making

On the positive side we also see a number of things pushing HR analytics in the right direction, both in terms of focus, setup, change management, and capabilities in HR.

Start with the business problem. HR analytics should not start with data or a preconceived approach to business problems, but with a business challenge. This point is often noted in the analytics discussion, and is actually the application of the “outside-in” thinking to this particular area of HR, as illustrated in Figure 1. This highlights that analytics and data are really only smaller and integrated parts of the overall diagnostic framework – means and not ends. We also recommend that analytics focuses on the three to five big-ticket issues for the business. This means resisting the temptation to continuously pursue many smaller and less value adding issues (e.g., turnover prediction, learning ROI, simple survey linkage analytics etc. when same are not core for a business issue). Ask yourself: “What are the biggest challenges facing our business the next 3–5 years, and how can HR support the business on same?” – that is typically the best starting point for actionable analytics.

Figure 1. Information for decision making: the process starts with these key questions on context, stakeholders, and strategies. The information process proceeds with four questions: What choices do we need to make? What can we discover and test? What data can we collect and analyze? Which actions do we now recommend?

Take HR analytics out of HR. This may sound drastic, but when HR analytics matures, it initially starts cooperating more with other departments’ teams (in finance, operations, etc.), and eventually becomes part of cross functional/end-to-end analytics – looking at human capital elements in the entire value-chain. HR analytics must transcend HR issues and become part of existing cross functional business analytics, just like the analytics from other functions must transcend their functional areas. Analytics typically only yields truly new insights when multiple fields and perspectives are combined (investor perspective, customers, technology, human capital, safety, etc.), so any functional denomination prior to “analytics” is really just a sign that it has not matured enough yet to just be a natural part of “analytics.” Most HR analytics functions are some years away from this, and perhaps need to be matured to some extent within the HR function first (this maturation can be accelerated by importing business analytics talent to run HR analytics – it is often easier to teach business analytics professionals HR than to teach HR professionals statistics and analytics). Technology is also driving the integration of functional analytics; historically, data platforms were limited so each function/line of business typically got its own and correspondingly developed its own reporting team and subsequently its own analytics team. The future belongs to the cloud, real-time data, and cross functional/line of business “enterprise” platforms (which also allows businesses to reduce cost by operating fewer platforms and systems) – and that paves the way for cross-functional, end-to-end analytics. It is time for HR to join the party and “get a seat at the analytics table” and not just sit at its own HR analytics table. This also solves the talent issue in HR analytics (people with statistical analytical capabilities and business understanding typically do not gravitate towards HR), while there may be some practical hurdles to overcome on data-privacy with an end-to-end analytics setup, as HR data is distinct from data used by other analytics teams. Finding a practical way to balance HR data privacy with the business value in insights from analyses of (anonymized) data is a growing issue in any case, but none the less a practical issue that can be overcome (Finance analytics teams face separate challenges, as the right aggregation of data actually can give them inside-trader status).

Remember the “human” in human resources. HR analytics forgot about the H of HR – data and evidence does not change anything, as neither people nor organizations are completely rational. Sometimes it actually just makes it harder to change the status quo. At best, HR analytics provides input for management discussions that can elevate the decision quality, but there is rarely a straight line from data and analyses to action. We can learn a lot from the traditional change management literature and from Festinger's findings on cognitive dissonance. These findings highlight that for most people, given the choice between existing beliefs and new data showing your beliefs are misguided, people will choose their belief system and reject the data. (In Festinger's research, when the members of a UFO cult realized that there would be no Armageddon on earth and Messiah on a spaceship coming to save them on the predicted date, they concluded that actually because they had been so strong in their faith – instead of reaching the logical yet more painful conclusion based on the data, that their belief system just could be wrong). The tendency to reject data that threatens existing beliefs is strong if people have invested time/effort/identity in projects or ideas. That is the case for most HR initiatives, which typically have a proud sponsor or owner, often a senior leader who may not particularly like findings from HR analytics casting doubt on the value of his or her initiative. This is why data and evidence from HR analytics often has little impact – it is not just about science and data – it is about activism and having a point of view, about intervention and change. HR analytics findings are products that have to be sold to have any impact. This is easier if HR analytics also includes qualitative data, intuition, experience and – most of all – if it works on co-creating a coherent story with the key stakeholders. And this story should always start with the business challenge.

Train HR professionals to have an analytical mindset. Let's be honest – most HR professionals are not attracted to HR because of the opportunity to work with data and analytics as part of their role. There is, however, a growing appetite amongst HR professionals to acquire analytical capabilities, in particular when they experience firsthand how it helps them support their business. There are few courses in HR analytics, and those that exist may be superficial. A course in HR analytics would include: deploying a diagnostic framework (see Figure 1), basic training in statistics and science methodology (or perhaps just a recap for some), change management, and storytelling. It is important to be realistic: we typically see a 20–60–20 split between groups of HR professionals who get it, those who can be taught, and those who will never get it. We recommend that you focus on the first two groups, and supplement training with hands-on projects, and closer cooperation with academics. In addition, we would argue that 80 percent of analytics is similar across functions/lines of businesses. The majority of analytics training should ideally be cross-functional, and only a smaller part of the training should be HR specific (or specific for other functions/lines of business).

Two cases showing the value of embedding HR analytics in business analytics

In the following we will illustrate two cases of HR analytics being successfully integrated in business analytics and leading to impactful interventions on offshore drilling performance optimization and technical talent development, respectively:

Case 1

Leadership quality, crew competence, and outcomes on safety, operational performance, and customer satisfaction.

Maersk Drilling, a leading offshore drilling company and a business unit in the A.P. Moller – Maersk Group, experienced considerable variance in performance between similar drilling rigs operating under similar conditions, and at the same time faced the challenge of growing 40 percent within a four year period. Top management, including the CHRO, was interested in identifying: (1) What explains variance in performance between rigs? (2) How can that knowledge effectively be deployed to new rigs brought into operation?, and (3) How can the results be used to help convince prospective clients that the company will deliver on promised performance standards while growing considerably in a hot market? Business analytics using both qualitative and quantitative data, experience from the business, and offshore leaders’ intuition about what drives performance found strong and significant links between leadership quality (measured via a yearly people survey), crew competence (documented according to the industry standards and requirements), safety performance (from the company's safety system), environmental performance (spills documented in the company's health, safety, and environment (HSE) system according to the offshore industry standards), and outcomes on operational performance (via the company's operational business intelligence system) and customer satisfaction (via the company's commercial customer relationship management (CRM) system) across units in the company fleet. The findings were integrated in an end-to-end value chain analysis, and compiled into one coherent story: Customer satisfaction is about operational performance (in this case drilling performance/uptime), but other factors also matter for company success: leaders assessed more positively (on various standard leadership tasks) by their direct reports have lower crew turnover, lower turnover is associated with higher crew competence (fewer new people to train), which in turn is related to better safety performance, fewer spills, and fewer maintenance hours outstanding (i.e. the time it takes to fix stuff) which impacts customer satisfaction. Recommended action is to focus on leadership quality (training and selection), crew competence (training budget and controls) and maintenance hours outstanding across the fleet by placing same on unit scorecards, and to communicate the findings throughout the company to all leaders and employees and to existing and prospective clients.

Even though advanced statistical methods were used (logistical regression models on longitudinal data), the presentation just showed the r-squared values between the different elements, keeping in mind that this was not for an academic audience. It was to support storytelling for a (technical) business audience, and emphasizing the importance of co-creating the story with the many stakeholders. The analytics were part of a change management process Figure 2.

Figure 2. HR analytics in Maersk Drilling. Percentages shown are the squared correlations, i.e., amount of variance explained. Often HR Analytics would only link leadership quality and turnover (box 3), while a broad analytics approach like below looks at the entire value chain

Case 2

ROI and Strategic Impact of Technical Trainee Acceleration Program

The same offshore drilling company, Maersk Drilling, had challenges filling lead specialist positions due to industry talent shortage and growth. It had experimented with a strategic initiative to develop technical talent for the senior specialist target positions. Business analytics was used to identify that the company graduate program for Specialist Trainees showed desirable results on key outcomes compared with their peer-group (see Table 1and Figure 3). In addition to showing simple training ROI, the findings fed into a strategic talent discussion (build/buy/borrow). The company decided to double the graduate program intake to sustain its growth plans. Again, simple statistics were used to support the story (see Table 1 and Figure 3). In this case, it was also the co-creation of the story – backed by data and analytics – and that analytics was treated like a change management process that paved the way for the results to have a positive business impact.

Table 1. Outcome of Specialist trainee program compared to peer-group.

Figure 3. Development time to target position

Conclusion

We experience that as soon as we question the analytics movement, we get labeled troglodytes who live in the past and are out of date with modern HR. We disagree. The HR field is littered with good ideas that have not been institutionalized. We hope that our recommendations offer a way to make HR analytics a realistic and ongoing part of improved HR impact.

Selected bibliography

For a comprehensive illustration of the role of HR analytics and the use of HR data to tie people, strategy, and performance together, see E. Becker, M. Huselid and D. Ulrich, The HR Scorecard: Linking People, Strategy, and Performance (Harvard Business Press, 2001). An in-depth discussion of HR metrics and analytics in evidence-based management can be found in E. Lawler, A. Levenson, and J. Boudreau, “HR Metrics and Analytics: Use and Impact,” Human Resource Planning, 2004, 27(3), 27–35; J. Pfeffer and R.I. Sutton, Hard Facts, Dangerous Half-Truths and Total Nonsense: Profiting From Evidence-Based Management (Cambridge: Harvard Business School Press, 2006); J. Phillips and P. Phillips, ROI at Work: Best-Practice Case Studies from the Real World. (ASTD Press, 2005); and D. Rousseau, “Is There Such a Thing as Evidence-Based Management?” Academy of Management Review, 2006, 31(2), 256–269. For a perspective from economics on the use of data and statistics as input for decision-making in management on personnel related matters, see E. Lazear, “The Future of Personnel Economics,”Economic Journal, 2001, 110(467), F661–F639. For a classical perspective of the limited impact of facts and data on belief systems, see L. Festinger, R. Henry, and S. Stanley, When Prophecy Fails: A Social and Psychological Study of a Modern Group that Predicted the Destruction of the World (University of Minnesota Press, 1956).

Thomas Rasmussen is vice president, HR data & analytics, at Royal Dutch Shell and has previously led HR analytics at A.P. Moller-Maersk. He received his M.Sc. and Ph.D. in psychology from the University of Aarhus, Denmark, and his main interest is bridging management science with practical application. (Royal Dutch Shell, Carel van Bylandtlaan 16, 2596 JM, The Hague, The Netherlands, tel.: +31703774716, e-mail: Thomas.Rasmussen@shell.com).

Dave Ulrich is the Rensis Likert Professor of Business at the Ross School of Business, University of Michigan. He has published over 25 books and 200 articles on leadership, organization, and human resources. His work demonstrates how organizations create value for employees, customers, and investors. (Rensis Likert Professor, Ross School of Business, University of Michigan, 701 Tappan St., Ann Arbor, MI 48109-1234, tel.: +1 (734) 764-1817, e-mail: dou@umich.edu dou@umich.edu).

1

Tel.: +1 (734) 764-1817.