Gartner Reimagine HR 2018, Orlando.
The digital transformation of learning and development offers HR leaders new opportunities to embed learning within their talent strategies and make the business case for L&D investments crystal clear. Part of the promise of digital learning comes with the application of data and analytics, enabling organizations to measure and communicate the impact of these programs more precisely than ever before. Unfortunately, as with all new technologies, the rapid emergence of new options can be overwhelming, not every solution is right for every business, and adopting a technology without a clear understanding of how it will generate value can be a very expensive mistake.
To survey this new landscape of learning analytics, Justin Taylor, Director, Talent Solutions at Gartner, moderated a panel discussion at our ReimagineHR conference in Orlando on Monday, bringing together Patti Phillips, Ph.D, President and CEO of the ROI Institute; Dave Vance, Ph.D, Executive Director of the Center for Talent Reporting; and Kimo Kippen, a former Chief Learning Officer at Hilton. The conversation covered the range of new technologies emerging in this space, the opportunities they provide, and the challenge of figuring out how to take advantage of those opportunities.
When considering an investment in learning analytics, the L&D function should keep a few strategic considerations in mind. Based on Monday’s discussion, here are a few of the key questions leaders should ask themselves:
What is your objective?
There are a number of technologies currently on the market that apply analytics to L&D in different ways and to different ends. There’s adaptive testing, in which training modules and skill assessments automatically adapt to each individual’s level of ability. Learning record stores and xAPI record and track learning experience data, allowing organizations to track the progress of learning employee more closely and draw more insights from that data. Learning experience platforms offer new ways of delivering learning to employees on an individualized, self-directed basis. Natural language processing, machine learning, and augmented and virtual reality are also finding applications in learning.
With all these options out there, the panelists agreed, it’s important for an organization to identify just what they hope to get out of learning analytics before buying a new piece of enterprise technology. Don’t chase a shiny toy, Kippen advised, but ask what the business objective is and whether the investment is worth it. You might find that the extra dollar is better spent on fundamentals, Vance added, as new technology won’t fix more fundamental problems in your L&D program. “Without algebra,” he analogized, “you’re not ready for the calculus.”
Google has developed a new feature for its G Suite of enterprise software that will enable managers to track whether and how employees are using various G Suite apps such as Gmail and Google Docs, the tech giant revealed this week. The tool, called “Work Insights,” is now in beta after being previewed with a small set of business customers, and will allow administrators to “gain visibility into which teams are working together and how they’re collaborating” and “review trends around file-sharing, document co-editing, and meetings to help foster connections, strengthen collaboration and reduce silos.”
To protect employee privacy, Google added, Work Insights only produces aggregated data analytics for teams of ten people or more, so admins will not be able to monitor individual employees’ use of G Suite apps, but will be able to see, for example, how many employees in a given business unit are using Google Hangouts.
The move looks like part of Google’s efforts to make G Suite more competitive against Microsoft’s enterprise technology collection, Office 365, CNBC’s Jillian D’Onfro noted in reporting the news. G Suite had 4 million paying customers as of this past February, whereas Microsoft counts 135 million active monthly commercial users of Office 365, which made its own Workplace Analytics feature generally available in 2017. Workplace Analytics also only uses aggregated and de-identified data to provide insights on a team, not individual, level.
In a panel discussion at Gartner’s ReimagineHR event in London last week, Birgit Neu, Global Head of Diversity & Inclusion at HSBC, and Eric Way, Director of Diversity & Inclusion at Volvo Group, sat down with attendees to share their experiences evolving their organizations’ D&I strategies over time. Although Birgit and Eric come from different organizations with different D&I journeys, common themes emerged from their stories that offer some insight into how to run a successful D&I program. A key point both panelists raised was that D&I must be “red-threaded”—that is, consistently part of the entire employee experience, both on an individual level and in interactions with colleagues.
Birgit was HSBC’s first global Head of Diversity & Inclusion, which meant that her strategic direction was defined by the organization’s need to understand what work was already being done in the space of D&I at the organization. Her first tasks were to build that understanding and use it to create a central theme for how the organization would approach their D&I mission in a unified way going forward. Being closely aligned with the talent analytics function, she said, helped her and her team to assess the experience of the bank’s employees and identify opportunities for improvement.
One example she gave was about parents and caregivers: Many organizations assess the number of parents in the organization by how many individuals have identified dependents in the HR information system. At HSBC, however, Birgit and the talent analytics team were able to determine that when asked directly, many more individuals identified themselves as parents than the system indicated. This gave the company an opportunity to reconsider the experiences of the parents in its workforce and think about wellness communications in a different way. HSBC went back to employees to see if there was a difference between parents and caregivers, as they had previously lumped these groups together. They found that asking people these questions separately gave them a clearer picture of their employees’ needs and challenges, and have been able to work with the benefits team to ensure that communications are relevant and timely to each group’s needs.
What will your job look like in 2025? How confident would you be in your answer? These are the questions Gartner has been asking in our ongoing series of briefings with hundreds of HR business partners, HR generalists, and other strategic HR professionals.
This particular group’s answer to this question is a matter of particular concern for their organizations. HRBPs and HR generalists make up the largest portion of today’s HR functions: about 25 percent of HR headcount and 19 percent of HR budget expenditure, according to Gartner’s HR Budget and Staffing Benchmarking Survey. Accordingly, the work these professionals do has a large impact on the global HR community.
At one of our recent briefings in Chicago, HRBPs discussed the new responsibilities they expect to take on in their jobs in the coming decade, as well as the tasks they are looking forward to setting aside or delegating.
Much of the new work HR professionals are anticipating mirrors the environment in which they will work (and in many cases, are already working):
- Doing more with data. HRBPs already feel growing expectations around their data skills and all expect that trend to continue. The ability to use data effectively, participants predicted, will also increasingly depend on fluency with HR technology and information systems, making the already difficult task of analyzing and telling stories with data more complex. For example, one HRBP from the retail industry shared that employee sentiment analysis and mood tracking was one particular area where she was already being asked to do more. Instead of relying on the formal employee survey, HRBPs will be asked to spot trends in employee email histories, health data, technology use tracking, and other data sets to identify workforce issues and opportunities.
- Being predictive, not just proactive. The HRBP role originally emerged as part of the HR function’s transformation from being reactive to being proactive. The next evolution of HR is to become predictive. Being proactive meant trying to anticipate events and align their work accordingly; being predictive, participants said, means not only anticipating potential outcomes, but also being able to judge which outcomes are most and least likely to occur. In other words, being predictive blends anticipation and prioritization in a way that proactivity alone does not. Many of our attendees indicated that they were enthusiastic about this change, especially in combination with their growing strategic role.
Talent analytics has rapidly grown from an experimental trend into something every organization uses. While many HR functions are investing in analytics, however, few are getting the kind of results they’d like to see. If the promise of talent analytics remains unfulfilled today, it’s not because the technology isn’t ready. Over the past two years, we have heard from HR leaders that their biggest challenge in implementing analytics has been in connecting the data to critical business questions and drawing actionable intelligence from it. Gartner research has also found that collecting high-quality, credible data is a significant hurdle for many organizations.
Perhaps as a result of these growing pains, a global survey earlier this year found that most C-suite leaders don’t have a high level of trust in their analytics programs. HR is still under pressure to get senior leadership on board with talent analytics and prove its value to the bottom line.
At Gartner’s ReimagineHR event in London last Wednesday, Principal Executive Advisor Clare Moncrieff moderated a discussion with a panel of leaders at major companies on the practical lessons they have learned in applying talent analytics on the ground. The panelists were Christian Cormack, Global Head of Workforce Analytics at AstraZeneca; Nanne Brouwer, Head of People Strategy and Analytics at Royal Philips; and Jacob Jeppesen, Specialist in HR Analytics at Novo Nordisk A/S.
The limiting factor for talent analytics professionals is rarely their knowledge of analytics, the panelists observed. Rather, it’s their knowledge of the rest of the business. Understanding how other business functions like supply chain or strategy work allows them to combine different sources of data that have never been looked at together before. This combination of data is ultimately more valuable than extremely advanced analytics that focus only on people data.
According to Gartner research, the adoption of AI is poised to grow rapidly in the coming years. This and other emerging technologies like robotics are bound to fundamentally change the way we work, largely or completely automating many of today’s jobs. While this technological upheaval is generally expected to create more jobs than it destroys, the transition will be disruptive and challenging for many professionals accustomed to working in a pre-AI world. The most dire projections anticipate widespread displacement or the radical transformation of current jobs due to AI and robotics, potentially affecting tens of millions of workers in developed countries.
The effects of automation will be challenging for the clients of many HR business partners, and HRBPs will be called to provide increasing support for those impacted, such as ensuring they have access to retraining opportunities. In addition, HRBPs see themselves as part of the population affected by automation: Ten years from now, HRBPs expect nearly half of their current day-to-day responsibilities to be automated. HRBPs are optimistic, however, about the impacts that technology and automation will have on their role. Our research at Gartner finds 68 percent of HRBPs agree that automation is an opportunity to prioritize strategic responsibilities. To capitalize on this opportunity, however, HRBPs need to anticipate what work will be automated and what work will be augmented.
At a recent meeting with 70 HRBPs in New York City, we discussed predictions for the future of their role and asked them how technology has changed or will change it. Several attendees mentioned employee data collection: Previously, this was an onerous monthly or quarterly process of manually pulling together data from various sources to populate dashboards for stakeholders. Technology has made this process easier and quicker, with the use of pulse surveys and other tools. It also creates opportunities to collect data in larger quantities or more precisely, and to use it in new ways, though HR still has a lot of work to do in convincing the C-suite of the value of talent analytics.
When it comes to making judgments based on large data sets, machines are often superior to humans, but many business leaders remain skeptical of the guidance produced by their organizations’ data analytics programs, particularly when it comes to talent analytics. That skepticism derives largely from doubts about the quality of the data the organization is collecting, but there is also a natural tendency among people who make strategic decisions for a living to reject the notion that an algorithm could do parts of their job as well as or better than they can.
While this may be true of executives and high-level professionals, some recent research suggests that most people are actually comfortable with the decisions algorithms make and even more trusting of them than of judgments made by humans. A new study from the Harvard Business School, led by post-doctoral fellow Jennifer M. Logg, finds that “lay people adhere more to advice when they think it comes from an algorithm than from a person”:
People showed this sort of algorithm appreciation when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic matches (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when people chose between an algorithm’s estimate and their own (versus an external advisor’s—Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy.
Our colleagues here at Gartner have also investigated consumers’ attitudes toward AI and found that these attitudes are more welcoming than conventional wisdom might lead you to believe. The 2018 Gartner Consumer AI Perceptions Study found that overall, consumers are not skeptical of the potential usefulness of AI, though they do have some concerns about its impact on their skills, social relationships, and privacy. The study was conducted online during January and February 2018 among 4,019 respondents in the US and UK. Respondents ranged in age from 18 through 74 years old, with quotas and weighting applied for age, gender, region, and income.