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. …
According to our research at CEB, now Gartner, even though 85 percent of CEOs believe it enhances business performance, only one third of employees are satisfied with diversity and inclusion at their organization, while nearly 60 percent of heads of HR believe their D&I strategy is ineffective. Many organizations are focused on making their cultures more inclusive and ensuring compliance with evolving legislation, but aren’t always seeing the results they had hoped for.
At our recent summit for HR executives in Johannesburg, more than 100 HR executives from 45 organizations had the opportunity to share ideas and hear from a panel of their peers how progressive organizations in South Africa are addressing the challenge of enhancing and evolving their D&I strategies.
1) Bring the Outside In
When defining what successful D&I looks like, our participants highlighted ideas and innovations, deliberate dialogue and co-creation, and thinking about diversity in all aspects: clients, products, and employees alike. The more integrated these are, the greater the impact. Many companies find that hiring employees from more diverse backgrounds gives them a way to engage new markets through new products, ideas or services. By bringing new perspectives into the organization, companies were better able to address the needs of both employees and customers.
2) Tackle Systems and Processes
Organizations that have made progress on D&I stressed the value of accelerated development programs that have yielded results in nurturing internal talent, including C-suite executives developed from within the organization; as well as the need to make hard decisions such as suspending the promotion process because the pool of candidates was not diverse enough.
Even though 91 percent of S&P global companies offer D&I training with 46 percent of organizations conducting their D&I training to mitigate unconscious bias, but as one participant shared, “It’s hard to catch bias in the moment.” One way to mitigate bias is by creating accountability for decision makers. For example, rather than expecting a hiring manager to make unbiased decisions independently, organizations are using a diverse panel when interviewing candidates. (To learn more, CEB Recruiting Leadership Council members can read our research on Driving Diversity Through Talent Acquisition.)
In this half-hour talk posted last week at re:Work, Google’s VP of People Operations Prasad Setty discusses his experience leading the development of the search giant’s talent analytics program, and about the key difference he discovered between having data make decisions for people, and using data to improve the way people make decisions:
When Prasad Setty joined Google ten years ago to build its People Analytics team, he envisioned a workplace where all people-related decisions would be made by data and analytics. If algorithms were spitting out search terms, why couldn’t we use them to make decisions for and about our people?
Setty soon discovered that this was the wrong approach. Despite the ability of analytics to objectively predict outcomes with high accuracy, people were reluctant to rely solely on formulas when it came to making important decisions — especially decisions that involved people, such as a promotion. And so, Setty shifted his vision for the People Analytics team. Rather than using data and analytics to make all decisions at Google, the team’s mission would be to educate Googlers on how they were making decisions and to help them make better decisions over time.
What really stands out about Google’s approach here is that they chose not to use a quantitative focus, even though they had the analytic sophistication necessary to do so. At one point, Setty mentions how HR was able to create a logistic predictive model that was able to accurately predict promotion decisions with an error rate of only 10 percent based on a few easily measurable attributes. Despite this, the engineers involved in the hiring process made it very clear that they did not want to outsource such an important task away to an algorithm.
This is an important lesson in how organizations can effectively use data in managing talent issues, particularly culture change.
At the Harvard Business Review, Tadhg Nagle, Thomas C. Redman, and David Sammon present the findings of a study they conducted to assess the quality of data available to managers at 75 companies in Ireland. Using Redman’s Friday Afternoon Measurement method, they asked managers to collect critical data on the last 100 units of work conducted by their departments and mark them up, highlighting obvious errors and counting the number of error-free records to produce a data quality score. “Our analyses confirm,” they write, “that data is in far worse shape than most managers realize”:
- On average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error. A full quarter of the scores in our sample are below 30% and half are below 57%. In today’s business world, work and data are inextricably tied to one another. No manager can claim that his area is functioning properly in the face of data quality issues. It is hard to see how businesses can survive, never mind thrive, under such conditions.
- Only 3% of the DQ scores in our study can be rated “acceptable” using the loosest-possible standard. We often ask managers (both in these classes and in consulting engagements) how good their data needs to be. While a fine-grained answer depends on their uses of the data, how much an error costs them, and other company- and department-specific considerations, none has ever thought a score less than the “high nineties” acceptable. Less than 3% in our sample meet this standard. For the vast majority, the problem is severe.
- The variation in DQ scores is enormous. Individual tallies range from 0% to 99%. Our deeper analyses (to see if, for instance, specific industries are better or worse) have yielded no meaningful insights. Thus, no sector, government agency, or department is immune to the ravages of extremely poor data quality.
The data quality challenge should sound familiar to HR leaders attempting to implement talent analytics strategies.
At our ReimagineHR summit in London on Thursday, CEB (now Gartner) Principal Executive Advisor Clare Moncrieff led a session on creating a common vision of digitalization for the business and HR. After examining hundreds of trends, our research councils serving chief HR officers and chief information officers have identified six deep shifts in the business environment that will result from digitalization. These shifts should act as the framework for heads of HR to:
- Ensure talent conversations with the line are grounded in business context
- Identify the current talent implications of these shifts, project future implications, and partner with the line and C-suite peers to prioritize and respond to each
- Improve their teams’ business acumen (to underscore the importance of this, 58 percent of HR business partners indicated in one of our surveys that building business acumen was their top development goal in 2017)
(The case studies we link to below are available exclusively to CEB Corporate Leadership Council members)
1) Demand Grows More Personal
As customers seek personalized products that align with their preferences and values as individuals (rather than as segments), companies will rely on digital channels and digital innovations in logistics and customer service to achieve personalization at scale. Customers will continue to expect lower-effort, nonintrusive service.
This could, for example, affect how HR functions look for new talent. Attraction of critical talent now requires differentiated, customized branding and career coaching. Candidates will demand a more effortless, personalized application experience. AT&T approached this shift by creating a more personalized “Experience Weekend” to show the innovation of its brand to campus candidates and make top talent more likely to accept job offers.
In a breakout session at the ReimagineHR conference hosted by CEB (now Gartner) in London today, a group of several dozen HR leaders came together for a peer benchmarking session to compare notes and discuss common challenges in the field of talent analytics. The attendees at Wednesday’s session had a variety of roles, including some CHROs, some heads of employee experience, HR business partners or other leadership positions within the HR function: Just as in our peer benchmarking session last year, very few identified themselves by title as heads of talent analytics. The diversity of titles and roles in the room illustrates both the breadth of the impact talent analytics is having on the HR function and the fact that many organizations do not have a dedicated talent analytics team.
The discussion centered on several key themes in the sphere of talent analytics and the challenges attendees were facing at their organizations in bringing data analysis to bear on their talent strategies. Enabling the use of talent analytics, making the function more strategic, building analytic capability, and improving data quality were all areas of concern. These are some of the key challenges that came up in Wednesday’s discussion:
Aligning Talent Analytics to Critical Business Questions
Asked where they were primarily focusing their efforts to drive action in enabling the use of talent analytics, a plurality of attendees identified this as their main focus. Some attendees noted that they are gathering robust data but were still struggling to translate that data into actionable insights to solve business problems. Attendees at last year’s session shared the same frustration. To some extent, the degree to which data can be leveraged is a matter of the analytics function’s maturity. One component of solving this problem is ensuring that the data is “clean,” accurate, and helpful in making decisions: As one HR leader remarked, she is often presented with the data that is easiest to gather rather than the data that is most useful.
Benchmarking surveys can be a useful tool to understand how your organization compares to its peers across a variety of metrics, including talent metrics. However, Scott Mondore writes at Talent Economy, some organizations become over-reliant on benchmarks in defining their talent strategies, which “takes away from the value of the metric as a strategic tool.” Rather than chasing potentially arbitrary benchmarks, Mondore, co-founder and managing partner of the human capital analytics advisory Strategic Management Decisions, argues that talent leaders should use their data and analytics capabilities to figure out what talent metrics really matter to the organization’s performance:
Consider that a benchmark is just an average. Thus, the pursuit of outperforming a benchmark is simply a chase to be better than average against a number that may not reflect a true reality — it just reflects your particular vendor’s database. Benchmarks are also subjective. They’re a number that can change when, for instance, a vendor surveys more clients or you switch vendors. If the target is arbitrary and highly fluctuating, why spend time and money aiming for it? Shouldn’t leaders spend time and money focusing on improving metrics that have proven connections to building their business, and not just trying to outscore the average organization?