{"id":16207,"date":"2016-11-17T18:09:56","date_gmt":"2016-11-17T23:09:56","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/wearables-at-work-is-the-rise-of-people-analytics-software-innovative-or-intrusive\/"},"modified":"2016-11-17T18:09:56","modified_gmt":"2016-11-17T23:09:56","slug":"wearables-at-work-is-the-rise-of-people-analytics-software-innovative-or-intrusive","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/wearables-at-work-is-the-rise-of-people-analytics-software-innovative-or-intrusive\/","title":{"rendered":"Wearables at Work: Is the rise of \u201cpeople analytics\u201d software innovative or intrusive?"},"content":{"rendered":"

The proliferation of fitness trackers and sleep monitors has given rise to the quantified-self movement. Recently, this trend has started to gain traction in corporate environments as well. The number of employers using wearable technology as part of their HR strategies is up 30% since 2014 and 55% of companies are using these devices to boost worker productivity.[1]<\/a> One of these companies, Humanyze, uses a smart employee badge to evaluate how employees interact with one another. The badge contains a microphone, accelerometer, Bluetooth connection and other tools to measure how people move throughout an office, track who they interact with and even analyze their tone of voice.[2]<\/a> Humanyze\u2019s back-end software breaks down the important information in real-time into a dashboard showcasing key trends for employers. One of the key challenges in human capital management is that performance evaluations often contain flawed inputs. Managers rely on self-reports or a small sample size of incidents when evaluating how team members interact with one another. [3]<\/a> Humanyze\u2019s platform allows managers to apply analytic rigor to their observations of team behavior and provides routine un-biased feedback for employees.<\/p>\n

\"humanyze-badge\"<\/a>
Humanyze Smart Badge Source: Humanyze<\/figcaption><\/figure>\n

Recently, Bank of America used Humanyze to improve customer service in their call centers which employ 10,000 people around the world. Initially, Bank of America and Humanyze set out to study how employees communicated with customers. However, they realized that what actually improved outcomes was how frequently employees spoke with one another to share information and techniques. Humanyze found that employees interacted most during their fifteen-minute overlap in lunch breaks. Bank of America ran a test allowing one group to eat lunch at the same time and kept the other group\u2019s lunch break staggered. Among the experimental group, network cohesiveness went up 18% and stress levels declined 19%. Furthermore, turnover rates went down 28% and call completion time improved by 23%.[4]<\/a> These important data points around employee interaction helped Bank of America discover a low-cost solution to improving employee engagement.<\/p>\n

\"Example<\/a>
Example of how Humanyze analyzes office networks<\/figcaption><\/figure>\n

Privacy Concerns<\/strong><\/p>\n

The rise of employee monitoring devices has caused concern amongst critics who worry that these technologies are too invasive. Humanyze insists that their content, like audio, is not recorded, but instead analyzed in real-time. They also do not give companies access to individual data, they only provide analysis on aggregate data. Only employees can see their own individual metrics. MIT Professor Alex \u201cSandy\u201d Petland argues that in this new world of big data, allowing people to own their own data should be a prerequisite for all companies offering tracking and monitoring services. In his \u201cNew Deal on Data,\u201d he argues that individuals will consent to less privacy if they feel the data will benefit them and will not be shared without their approval.[5]<\/a><\/p>\n

But when does employee monitoring stop being helpful and start being harmful? Ethan Bernstein argues in \u201cThe Transparency Trap\u201d that too much transparency can be counter-productive in organizations because employees may waste time trying to manage impressions. Drawing on the findings from this article, Humanyze can mitigate some of these concerns by helping their partner organizations create \u201czones of privacy.\u201d They can do this by limiting their observations to well-defined teams (which can increase the level of psychological safety), ensuring their experiments are time-bound and removing judgment from feedback loops. [6]<\/a><\/p>\n

Future Applications<\/strong><\/p>\n

Going forward, I think one of the most promising applications of Humanyze will be in studying the collective intelligence of teams. Studies have found that a team\u2019s collective intelligence depends not on the intelligence of individual members, but on their social sensitivity, conversational turn-taking and number of women on the team.[7]<\/a> Humanyze provides an un-biased method for companies to analyze the collective intelligence of different teams within their organizations and track their improvements over time. It could also be a helpful tool for studying implicit bias within organizations around gender, sexuality and race. Using Humanyze\u2019s data, managers can study inter-office networks to see how these biases manifest themselves and develop new solutions. As Humanyze begins to reach scale and amass a more comprehensive dataset, it will be interesting to compare their findings across companies to advance the study of interpersonal networks in corporate environments.<\/p>\n

[799 words]<\/p>\n

[1]<\/a> Brin, Dinah W. “Wearable Worries.”\u00a0HRMagazine<\/em>, vol. 61, no. 5, 2016., pp. 138-140. http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1794444119?accountid=11311.<\/p>\n

[2]<\/a> Miller, Ron. \u201cNew Firm Combines Wearables and Data to Improve Decision Making.\u201d TechCrunch<\/em>, 24 February, 2015, https:\/\/techcrunch.com\/2015\/02\/24\/new-firm-combines-wearables-and-data-to-improve-decision-making\/<\/a><\/p>\n

[3]<\/a> Onnela, Jukka-Pekka et al. \u201cUsing Sociometers to Quantify Social Interaction Patterns.\u201d\u00a0Sci. Rep. Scientific Reports<\/em>, vol. 4, 2014, doi:10.1038\/srep05604.<\/p>\n

[4]<\/a> Miller, \u201cNew Firm Combines Wearables and Data to Improve Decision Making.\u201d<\/p>\n

[5]<\/a> \u201cWith Big Data Comes Big Responsibility.\u201d\u00a0性视界 Business Review<\/em>, 5 Nov. 2014, hbr.org\/2014\/11\/with-big-data-comes-big-responsibility.<\/p>\n

[6]<\/a> Bernstein, Ethan. \u201cThe Transparency Trap.\u201d\u00a0性视界 Business Review<\/em>, 28 Oct. 2014, hbr.org\/2014\/10\/the-transparency-trap.<\/p>\n

[7]<\/a> Williams Woolley, Anita et al. \u201cEvidence for a Collective Intelligence Factor in the Performance of Human Groups.\u201d\u00a0Science<\/em>, vol. 330, no. 6004, 29 Oct. 2010, pp. 686\u2013688. doi:10.1126\/science.1193147.<\/p>\n","protected":false},"excerpt":{"rendered":"

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