This week I attended the People Analytics World conference #PAWorld17. It’s my third year and attendance has gone from 100 to 350, which indicates a real growth in interest. It’s always an event I enjoy. There are great case studies and there’s a lot of enthusiasm among attendees. It’s also personally satisfying, as I have a career-long interest in helping organisations use data and insights to make better decisions about people.
There was a lot of discussion this year on how the field should develop both inside organisations (how do you build an effective people analytics team?) and as a profession (including a call to establish a professional body). And I think this is a good discussion to be having as it feels like people analytics is not having as much impact as it could.
In fact, there’s evidence to show its impact is really quite limited. From an HR perspective, a 2016 report by the New Talent Management Network (NTMN) concluded that “Only basic people analytics are being performed by most organizations, undercutting the popular narrative that companies are rapidly advancing in this space.” The study also found that in those organisations which are doing people analytics, the most common focus areas by far are still turnover and acquisition. From an academic perspective, Janet Marler and John Boudreau conducted an evidence-based review of HR Analytics in 2016 and concluded that “despite evidence linking the adoption of HR Analytics to organizational performance, the adoption of HR Analytics is very low.” Even in the latest Deloitte Human Capital Trends report, people analytics is well down the priority list (a lowly 8th) and the authors note that “Readiness to capitalize on people analytics remains a challenge… Only 8 percent of organizations report they have usable data, while only 9 percent believe they have a good understanding of the talent factors that drive performance”. At PAW itself Alec Levenson, who kicked things off, talked about “amazing potential” but highlighted how “much analysis is disjointed and uncoordinated”.
This lack of impact is a problem, and it’s worth digging into in more detail.
Broadly, I think there are three domains within people analytics.
- The first is human capital measurement, workforce planning and reporting. In large organisations this is being automated by technologies whose analytical capabilities are increasingly impressive. They are able to provide leaders across an organisation with near-enough real-time updates on key measures. For a central team the manual workload is reduced and attention can shift to thinking about what information to highlight because of its impact on business strategy and performance. Smart algorithms, machine learning and artificial intelligence which are being embedded within these tools will simplify even this task and help drive further efficiency. The other role of the central team is to help leaders use this information to make better decisions regarding org. design and capabilities, which is often a storytelling and consulting role. In smaller organisations that cannot afford these smart systems, this whole area can remain a problem. In fact, the NTMN report revealed that many smaller companies still rely on Excel and have problems with basic data quality.
- The second domain is what you might call “classic” people analytics. Take a business and talent problem, such as “Why do so many of our engineers leave after three years?” Calculate the business cost (in terms of lost experience, cost to replace, etc.) and dig into the data to understand the drivers of turnover for that particular group and to identify possible fixes. These are often small projects dealing with specific “fires”. Sometimes the question might be broader, such as “Are our assessment tools actually predicting performance?” or “Does our performance management process reward collaboration and innovation?” I’ve called this domain “classic” because it’s been around for a long time. For example, in 1999 we produced a service-profit-chain analysis for a major retail bank. It was based on structural equation modelling and several years of data and it showed the business impact in terms of sales (penetration) of improving the onboarding of new staff. It led to a revised induction and training programme, which helped to improve performance. I felt I could have presented that client story at this year’s PAW and not felt too out of place. There have been advances in using more creative statistics (e.g. machine learning, cluster, segmentation, survival analyses), there have been improvements in data visualisation, and there has been a notable advance in qualitative data analysis, but these feel like refinements rather than major shifts.
- The third domain is “big data science” and it is the least defined and the most hyped. Data science outside of people analytics has grown quickly due to easy access to massive, open data sets which can be explored and tested. When you have a huge amount of data you can take a different approach to analytics, namely using lots of computing power to explore, test and refine different kinds of models to see what’s out there that might be useful to know. The challenge here for people analytics is really a data challenge. For obvious reasons, companies are reluctant to provide access to integrated data at an individual level. This is why approaches are often conducted at a very aggregate level on external data (e.g. tools like Joberate) or very specifically on topics like workspace and collaboration (e.g. tools like Humanyze). At PAW Randy Knaflic described how Jawbone, who you would expect to be ahead in this, has used biometric data to help some of its teams improve. With these kinds of data the focus for the people analytics profession is to build trust in and to demonstrate value through these kinds of approaches. I think the real future for big data science in people analytics is when large organisations fully embrace social platforms like Workplace (i.e. Facebook at work) and whatever MS/Yammer/LinkedIn evolves into, and when employees are producing data that social media-style analytics can explore (more on this below).
All three domains face challenges then. But I think there is also an overall problem for people analytics, in that it is missing a clear “hook” (or perhaps several good hooks). By this, I mean a way of communicating to business leaders and others what it is all about – not so much a unifying theme, but a clear message or set of messages (in marketing terms a hook is a simple way of creating interest). And I would suggest that one very good “hook” is “Employee Experience”.
Employee Experience is a term that is being used more and more. Sometimes it is used in a narrow sense to describe the user experience of the digital tools that employees have access to. But others define it more broadly, which I think is better, such as: “The intersection between employee expectations, needs, and wants and the organizational design of those expectations, needs and wants” (Jacob Morgan) and “The sum of perceptions employees have about their interactions with the organization in which they work” (Tracy Maylett and Matthew Wride). In this sense, it’s a nod to the field of Customer Experience and reflects an overall trend towards approaching employees in the same way as you would consumers and customers. The big driver of this change in perspective is the digital transformation of business and the trends associated with the future of work, such as a more flexible workforce, greater numbers of contingent workers, more diverse teams, dependence on social media, a focus on projects rather than jobs, etc. (I have written more about this here).
Personally, I really like Maylett and Wride’s description of thinking of employee experience as “creating an operating environment that inspires your people to do great things”. To my mind this means identifying the key interactions that employees have with the organisation throughout the life cycle and then applying design thinking to improve performance. It is a joined-up approach to org. design and capabilities, jobs, teams, rewards and the way people work. It includes understanding employee journeys and maximising the value of key episodes. It also means improving the digital tools employees use and reviewing the physical workspace in order to increase collaboration and productivity. A key focus is “inspiring people to do great things” – it’s about removing obstacles, simplifying processes, building trust and allowing people to do their best work and to contribute to the organisation’s mission and purpose.
Each of the domains of people analytics has a role in improving employee experience. “Classic” people analytics can highlight which aspects require optimising and for whom. One key contribution here is to break employee experience down into actionable parts through approaches such as micro-segmentation, personas, journeys, episodes, moments, networks, etc. As more data are integrated at the individual level, it’s possible to tell a far more holistic story about the “operating environment”. In terms of reporting, once you have identified the elements of employee experience that are key to your business strategy, it’s vital to help leaders review the progress that’s being made. One way that we are already doing this for our clients is through Employee Experience Scorecards, which pull in data and insights from a variety of HR, business and external sources. And data science perhaps offers some of the most exciting opportunities for improving employee experience through social media analytics. This can take the form of using design thinking to create shared digital experiences, testing different approaches through analysing real-time feedback, learning about your workforce based on their online behaviours and adjusting your plans as a result. The future of “business-to-employee marketing” is this direction.
To return to the start of this piece then, one of the things that strikes me attending PAW is the passion that people have to use data and analytics to make a positive difference. That’s why the feedback about limited impact is a bit disheartening. As well as debating the need for a professional body, I wonder if the lack of impact is partly because there’s a need for a better “hook” in order to tell a more complete story about the contribution the specific domains of people analytics make. If that’s the case, then Employee Experience is one possible hook and a powerful and positive one as we consider the trends associated with the future of work.
References:
- New Talent Management Network “Still Under Construction: The State of HR Analytics 2016”
- Janet H. Marler & John W. Boudreau (2016) “An evidence-based review of HR Analytics” in The International Journal of Human Resource Management
- Tracy Maylett and Matthew Wride “The Employee Experience: How to Attract Talent, Retain Top Performers, and Drive Results” (Wiley, 2017)
- Jacob Morgan “The Employee Experience Advantage” (Wiley, 2017)
- Willis Towers Watson “Under pressure to remain relevant, employers look to modernize the employee value proposition” (2016)
Tags: #PeopleAnalytics #EmployeeExperience #FutureOfWork
This article was first published on LinkedIn on April 26, 2017
