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.
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.
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.
SHRM’s Roy Maurer recently highlighted a survey from KPMG showing that corporate leaders around the world remain distrustful toward their organizations’ data and analytics when it comes to using these tools to make business decisions:
In the survey of 2,190 senior executives from Australia, Brazil, China, France, Germany, India, South Africa, the U.K. and the U.S., just 35 percent said they have a high level of trust in their organization’s use of data and analytics. Another 40 percent said they had reservations about relying on the data and analytics they produce, and 25 percent admitted they have either limited trust or active distrust in their data and analytics. Nearly all respondents (92 percent) worry about the impact flawed data could have on their company’s business and reputation.
“Executives and managers are being asked to make major decisions based on the output of an algorithm that they didn’t create and don’t always fully understand,” said Thomas Erwin, global head of KPMG International’s Lighthouse, the firm’s center of excellence for data, analytics and intelligent automation. “As a decision-maker, you really need to have confidence that the insights you are getting are reliable and accurate, but many of these executives can’t even be sure if their models are of sufficient quality to be trusted. It’s an uncomfortable situation for any decision-maker to be in.”
One barrier to the credibility of analytics for business leaders is the prevalence of incomplete data; another is that the metrics against which organizations are measuring are often ill-defined. HR metrics like source of hire and quality of hire are particularly hard to measure accurately, Kevin Wheeler, founder and president of the Future of Talent Institute, tells Maurer, and there is significant disagreement on how best to define them.
Data scientists are among the most in-demand professionals in the US right now, as more and more industries look to harness the power of data to drive productivity and innovation to new heights. Demand is high and supply is short, so these experts command remarkably high salaries. However, recent research has suggested that many companies’ data isn’t sufficiently high-quality to produce the kinds of insights managers are expecting. This is particularly true in the emerging field of talent analytics, where companies are making major investments but most aren’t seeing them pay off.
In addition to the data quality challenge, Data Quality Solutions President Thomas C. Redman suggested in a recent Harvard Business Review article that senior managers at many organizations are mismanaging their data scientists: placing them in the wrong part of the organization, not focusing the data science program on business outcomes, and not facilitating a transition to a more data-driven culture. He offers some suggestions for how companies can get more out of their data scientists:
First, think through how you want data scientists to contribute, and put them in spots where they can do so. The worst mistake a company can make is to hire a cadre of smart data scientists, provide them with access to the data, and turn them loose, expecting them to come up with something brilliant. Lacking focus and support, most fail. Instead, clearly define the opportunities you want to address using data science, and put your data scientists in places in the organization where they can best pursue those opportunities. …
Diversity, new interviewing tools, data, and artificial intelligence are the four trends set to have the biggest impact on recruiting in the coming year, according to LinkedIn’s latest Global Recruiting Trends report. Based on a survey of over 9,000 talent leaders and hiring managers worldwide, along with a series of expert interviews, the report underscores the growing role of technology in shaping how companies meet their hiring goals, of which diversity is increasingly paramount. Nonetheless, while many HR leaders see these trends as important, the number of organizations fully acting on them lags far behind.
Diversity was the top trend by far, with 78 percent of respondents saying it was very or extremely important, though only 53 percent said their organizations had mostly or completely adopted diversity-oriented recruiting. In recent years, diversity has evolved from a compliance issue to a major driver of culture and performance, as more and more organizations recognize its bottom-line value. This shift was reflected in the LinkedIn report, with 62 percent of the companies surveyed saying they believed boosting diversity would have a positive impact on financial performance and 78 percent saying they were pursuing it to improve their culture. Additionally, 49 percent are looking to ensure that their workforce better reflects the diversity of their customer base.
Diversity was the only top trend identified in LinkedIn’s survey that wasn’t directly related to technology, but technology is definitely influencing how organizations are pursuing it. In the past year, we have seen the emergence of new software and tools to support diversity and inclusion. The aim of these tools is to remove the human error of unconscious bias from the recruiting process, but it’s important to be aware that automated processes can also develop built-in biases and end up replicating the very problem they are meant to solve. This is an issue we’ve been following in our research at CEB, now Gartner; CEB Diversity and Inclusion Leadership Council members can read more of our insights on algorithmic bias here.
The development of new interview tools and techniques was identified as the second most important trend, with 56 percent saying it was important. The LinkedIn survey found that the most common areas where traditional interviews fail are assessing candidates’ soft skills (63 percent), understanding candidates’ weaknesses (57 percent), the biases of interviewers (42 percent), and the process taking too long (36 percent). The report highlights five new interviewing techniques, all enabled by technology, that aim to address these problems:
Employee monitoring technologies represent the cutting edge of workplace gadgets, and these technologies are already becoming increasingly common, from sociometric badges to tracking devices at desks to sentiment analysis and even experiments with microchipping employees. Olivia Solon at the Guardian recently explored the next generation of this tech:
How can an employer make sure its remote workers aren’t slacking off? In the case of talent management company Crossover, the answer is to take photos of them every 10 minutes through their webcam. The pictures are taken by Crossover’s productivity tool, WorkSmart, and combine with screenshots of their workstations along with other data —including app use and keystrokes—to come up with a “focus score” and an “intensity score” that can be used to assess the value of freelancers.
Today’s workplace surveillance software is a digital panopticon that began with email and phone monitoring but now includes keeping track of web-browsing patterns, text messages, screenshots, keystrokes, social media posts, private messaging apps like WhatsApp and even face-to-face interactions with co-workers. …