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| Zev Eigen |
Relational data are measures of how people interact to get work done. By contrast, attribute data are measures that describe what people are and include measures like educational attainment, tenure on the job, skill sets, etc. Relational data include measures like whether people are connected to each other, how much they rely on each other for information or support, how much they inspire or collaborate with each other.
When work consisted of linear hierarchies and reporting, and people spent their whole careers at one job, often in one or two geographic locations in close proximity to the rest of the company, attribute data were more useful. Work doesn’t look like that anymore. Work is decentralized, global, and there is less reliance on standard hierarchical structures. People work on devices from anywhere and at any time of day. A big part of the data scientific revolution is the realization that relational data are much better suited than attribute data at solving the biggest and most costly HR problems.
Another important component of the HR tech revolution is the renewed focus on collaboration. There is so much evidence of the direct link between the right kinds of collaboration and objective measures and bottom line profit across a diverse array of organizational contexts. For instance, in their study: "Collaboration and Creativity: The Small World Problem," professors Brian Uzzi and Jarrett Spiro demonstrated that finding the right kind and amount of collaboration increased the likelihood that a Broadway musical would be profitable. They looked at 45 years’ worth of musicals. Collaboration in the hospitality industry produces happier guests.
Similarly there is a lot of attention being paid to the ways in which collaboration at law firms produces greater profit. For instance, Heidi Gardner, a professor at Harvard Law School and former consultant, has written extensively about how law firms should be gathering and analyzing relational data in order to drive profitability. She evaluated billing records from several large firms over time to show the consistent connection between collaboration and success.
And even at hospitals, the doctor-nurse teams that collaborate better are more likely to generate better patient outcomes. Across all of these diverse organizational settings (creative arts, hospitality, legal services and medical services), humans have to work with other humans to produce some output. So it should not be surprising that understanding the interconnectedness among people via relational data analytics is the A+ method for driving bottom line profitability in so many diverse organizational settings.
Even when law firms spend millions of dollars renovating physical spaces to induce “better collaboration,” often very little is done to deliberately measure which groups are collaborating better than others. How do you know that the spaces are cultivated collaboration? How do you know that you’ve assigned work on teams with optimal collaborative potential? How do you know which teams of lawyers require coaching to improve collaboration? Without measuring collaboration and using the data to create the right constellation of groups, employers are missing out on the tremendous cost savings and profit generation potential.
There is ample empirical social scientific evidence that would make the value proposition of investing in tools to accurately measure collaboration and to enable the deliberate optimization of teams a “no-brainer.”
Relational data analytics platform Syndio has developed the most effective and efficient method for gathering, analyzing and reporting relational data at organizations. Syndio deploys a survey instrument that enables employees to report critical information about how work gets done. Where appropriate or necessary, the platform takes in e-mail and calendar meta-data as a proxy for quantity of digital communication. “Under the hood,” Syndio calculates a barrage of sophisticated measures used to gauge how well individuals are collaborating and where they are failing to sufficiently collaborate.
Those measures are then visualized on easy to understand dashboards. The dashboards are designed to ensure that the data are transformed into actionable business intelligence. For instance, the dashboard output empowers HR leaders to answer the following critical questions:
• Which groups are the most cohesive? Which groups are the least cohesive?
• What is the relationship between collaboration and measurable bottom line profit?
• Which individuals are suffering from “collaborative overload,” which is a form of suboptimal overutilization often associated with highly influential employees feeling burned out?
Once Syndio’s instrument is deployed, and the data received analyzed, the dashboard empowers business leaders to answer these questions with ease. Some customers use these data to identify high collaboration and low collaboration groups, and then identify how to make the lower performing groups to be coached to collaborate more like the higher performing collaborative teams. That could mean simply enabling some lesser collaboration individuals to work with the high-collaboration individuals as a way of training them to improve.
It is truly the case that as part of the data science revolution in HR, better tools are now readily available to employers wise enough to take advantage of them.
Zev Eigen is the global director of data analytics for Littler Mendelson and the chief science officer of Edge Analytics. He can be reached at zeigen@littler.com. Follow him on Twitter: @zevdatascience. He will be a speaker at Lawyering in the 21st century: How to succeed through innovation, in Toronto on Monday, May 15. For more information about this event visit the conference website.
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