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Most of us are excruciatingly aware of how many hours of any given work day are ruthlessly eaten up by a seemingly endless stream of meetings, phone calls, and emails. And if you’re of the belief that there must be a better way, good news: There is.
In a recent article for MIT Sloan Management Review, McIntire Information Technology Professor Peter Gray and his Babson College colleagues Rob Cross and Thomas H. Davenport discuss how analytics is helping organizations to change not only how their people can work together more efficiently, but also how diagnosing and streamlining collaboration can support employee performance, satisfaction, and retention. Their discussion is the lead article in the publication’s special report on collaboration.
After speaking with more than 100 managers and executives actively engaged in projects taking advantage of collaboration analytics, the professors identified how using analytics directed evidence-based decisions that impacted overall collaboration performance. The results discovered five key ways that companies are deriving value from analytics with this specific focus:
- Maximizing the benefits of scale around specific leadership roles, across strategically important functional roles, and within communities of core technical experts
- Improving collaborative design and execution—including finding mid-tier “hidden integrators,” employees whose connectivity among employees enabled others’ success
- Driving planned and emergent innovation
- Streamlining collaboration
- Engaging talent
We recently spoke to Gray about his team’s findings and the how their research sheds light on how analytics can enable us to make the most of our time and best utilize our strengths and those of our coworkers.
Your article, “Collaborate Smarter, Not Harder,” speaks about streamlining shared work to make the process more efficient, and in one example, by incorporating a “passive data” analytics engine that required little of employees in order to help uncover insights. With these strategies for working better together coming from analytics, what can organizations do to ensure their interconnected employees share more but without creating new problems arising from information overload?
I think the title points to the answer: Companies are trying to figure out smarter ways to collaborate, not just urging their people to collaborate more. In fact, many firms are struggling with collaborative overload—when people spend so much of their time in collaboration with others that they can’t get to their regular work. Some of the initiatives that we describe in the paper show that companies are often trying to step away from the “more is better” approach, and instead using analytics to very precisely target opportunities for improved collaboration.
For instance, General Electric’s automated expertise routing system directs questions to people who are most likely to have the right answer, and in the process avoids overloading employees with requests that they can’t answer. We are seeing forward-thinking managers whose goals often include stripping out unnecessary collaboration while at the same time implementing very targeted efforts to improve collaboration at key points.
As companies seek to reduce attrition through analytics, some have been implementing models to identify successful collaboration patterns and networks. What about networking—as it applies to employee retention—have you found to be the most surprising aspect and for what reasons?
Newcomers to an organization are often advised to engage in “brand building” when they join in order to build big networks and let everyone know what they can offer. But that’s often a poor approach, and one that we actually found to be associated with higher turnover in subsequent years. Instead, leading companies are teaching their people how to pull others into their networks by setting up a lot of exploratory meetings, asking plenty of questions to fully understand others’ priorities and challenges, morphing their expertise to the problem at hand, and then offering very targeted expertise and assistance, if appropriate. Newcomers who generated pull in this way built more effective networks and as a result, were much less likely to turn over.
Historically, turnover has been found to be higher among isolated newcomers, so you can see why conventional wisdom was to encourage employees to build big networks. It was surprising to see that a big network wasn’t necessarily a good network, and what really mattered was how you engage with others to pull you into their worlds.
Your article also discusses how collaboration analytics helped General Motors make the most of concept development, finding that densely clustered groups with many ties among its members, and less impacted by external distractions, were more successful in making rapid, focused progress. How can organizations encourage this type of connectivity among collaborators while shielding them from the external pressures that often impede the freedom to fail fast and often?
What GM found was that team members who were being pulled in multiple different directions found it harder to develop concepts into novel prototypes. For those teams, it wasn’t a question of success versus failure; rather, it was a question of speed and efficacy. Concept development teams’ performance was undermined when they were exposed to a wide variety of ideas and when they simultaneously tried to pursue multiple different options.
Your network can pull you in many different directions, or it can help you power ahead in one direction by providing the right kind of expertise and assistance required for the task at hand. At GM, concept development teams benefited from that kind of focus, but, interestingly, that wasn’t the case for innovation teams operating at the fuzzy front end of exploring radical new ideas. Instead, innovation teams perform better by having broad networks that span many disconnected organizations, locations, and expertise types, which exposes them to emerging ideas and positions them to combine these ideas in novel ways.