Postgraduate students are not always aware of the different career paths that are available to them, which is something King’s is trying to tackle. I believe there are some great opportunities that people should think about, especially now.
Social scientists clearly have some important business-relevant skills. These include working with data, producing insights, presenting findings and arguing your case.
In the podcast, I suggest that these kinds of skills are going to become even more important in the future. Technology is already having a big impact on work. Routine elements of jobs are being automated. There are new and emerging cognitive technologies. As a result, what companies need more of is people who can exercise judgement and reasoning. In fact, in an age of AI and machine algorithms, human judgement, critical thinking and problem solving become even more important.
On top of this, what companies have more and more of is data – and not just numbers, but increasingly text, images and video. People who can develop insights from all these data are already in high demand. That’s especially true if you can also communicate those insights through storytelling.
According to the World Economic Forum, the top business skills are soon going to be things like analytics, critical thinking and complex problem-solving. On top of that, workers will need to be a life-long learners. They will also need to be able to teach others new skills in turn.
There are potential derailers. I have seen social science PhDs who are too rigid in their thinking and not practical enough. You have to be prepared to try things out. “Good enough” is sometimes an important principle.
But there is a growing movement of people who are interested in evidence-based practice in business, and this plays to many social science researchers’ strengths.
According to Rob Briner, evidence-based management means making “a conscientious, explicit and judicious use of the best available evidence.” This means using multiple sources and adopting a structured approach of inquiry and appraisal. In other words, it’s about applying social science rigour to business data and decision making.
I hope to see this movement grow over time. In the realm of people analytics, which I am particular interested in, for example, there are immediate benefits from evidence-based practice. When it comes to people and performance, it’s far better to explore the evidence than to rely on intuition and gut feeling.
Let me know what you think of these points and feel free to connect with me here on LinkedIn or on twitter @nickl4. I’m always happy to link up and to offer advice if I can.
Please note: The image above is Copyright of The Center for Evidence-Based Management. CEBMa is the leading authority on evidence-based practice in the field of management and leadership.
Tags: #PeopleAnalytics #SocialScience
This article was first published on LinkedIn on December 9, 2019.
One of my favourite books is Alan Pennington’s “The Customer Experience Book”. The reason is in the subtitle: “How to design, measure and improve customer experience in your business”. It’s a very practical guide to putting customer experience into action. I use ideas from it all the time.
One idea that I’ve found especially useful is assessing the maturity of an organisation in terms of its approach to customer experience. Alan talks about moving from being customer centric to being customer intelligent. In a customer-intelligent organisation “all staff know the experience they are required to deliver”. Moreover, there’s an understanding of “the precise points in the customer journey where value is either created or destroyed”. Above all, “a customer-intelligent company is making small adjustments every day to improve the experience”.
There is an obvious parallel with employee experience (EX) where many companies are looking to make a similar leap in maturity. I typically think about this in two dimensions: insights and activation.
Organisations who are just starting to build EX capability probably collect insights through an annual engagement survey. However, engagement survey results are likely to be looked at in isolation from other human capital data, even the results of other surveys. Key results from an engagement survey may be included in the company’s annual report and some engagement insights may be included in recruitment materials. These can help with building a consistent approach to how the organisation markets itself to potential recruits on LinkedIn and elsewhere.
More mature organisations supplement their engagement survey with agile pulse surveys. This means they can track sentiment on an ongoing basis. Connections are made between the findings of the engagement and pulse surveys, as well as automated joiner and exit surveys. This allows them to identify expectation gaps and misalignment. Insights are used to develop a broader employment brand, which is linked to organisational values and leadership behaviours.
A key tool is integrated people analytics that uses a broad range of connected data. These can include unstructured qualitative data, survey results, network data, human capital data, operational and business measures, and customer feedback such as NPS. Insights are used to personalise communications. The employment brand is translated into a differentiated employee value proposition (EVP) which is customised for key talent groups.
In mature, employee-intelligent organisations, data are translated from moment-in-time insights into employee journey maps and personas. They focus on a deep understanding of cohorts and critical talent and the employee life cycle. HR takes a design-thinking approach to employee experience. This means maximising the value of key episodes and moments, such as on-boarding, anniversaries, performance reviews, development discussions, and so on. They do this through prototyping and testing, from learning what’s working well and what’s not, and through rapid iteration. All people managers understand their role in delivering experiences that build trust in the future.
One problem with maturity curves like this is that they are seen as a sequential progression when it’s my experience that in practice things are typically messy and uneven. But by assessing where you fall in terms of your current EX capability you can identify where you need to focus and how to prioritise your efforts. To come back to Alan’s book again, he argues that it’s best to focus on lots of small changes rather than major programmes: “Your mantra for change is 100s and then 1000s of tiny changes”.
Alan Pennington, The Customer Experience Book (Pearson, 2016)
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)
