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Using analytics to improve collaboration

The effects of collaboration are not always positive – but the negatives can be tackled with analytics

Collaboration isn’t always a good thing, write Rob Cross, Thomas H Davenport and Peter Gray, MIT Sloan Management Review.

The benefits of collaboration to a business operating in 2019 are numerous, but the authors warn that the level of collaboration in contemporary businesses enabled by “the plethora of technologies that keep us connected” often “adds a steady stream of time- and energy-consuming interactions to an already relentless workload”.

In the past decade, collaborative time demands have risen by more than 50%, with the majority of knowledge leaders and workers spending more than 85% of their time on email, in meetings or on the phone. Employees have, on average, nine technologies to manage their work interactions.

The result is a more connected, but also more overwhelmed and less productive, workforce.

The answer to this problem, according to Cross, Davenport and Gray, is analytics.


Cross, Davenport and Gray have identified five ways in which companies can derive value from “collaboration analytics”.

1) Scaling collaboration effectively. Your company undoubtedly employs numerous experts in specific fields, but it’s likely these individuals are “far-flung throughout the organisation” and therefore unconnected.

Collaboration analytics can “maximise the benefits of scale” in three key areas: specific leadership roles; strategically important functional or “pivot” roles; and communities or core technical experts.

General Electric (GE) employs more than 300,000 people in nine businesses operating across the globe. In 2015, GE started using collaboration analytics.

GE identified “expertise communities” and developed a quantitative model to calculate when these communities were ready to plug into a new “knowledge-sharing architecture” and interact globally with other experts in “discussion spaces”.

GE also used analytics to identify which expert in a community was best placed to answer a specific question.

GE’s 43,000-employee Renewable Energy business has created 27 communities to connect individuals from across the organisation. In 12 months, 1,172 collaborators solved 514 customer problems, saving $1.1m.

2) Improving collaborative design and execution. It’s common within businesses to split employees into teams, but if employees are assigned to too many teams, team-based structures can become counterproductive and lead to the loss of talent. 

Analytics can enable team leaders to determine the teams, and the individuals in those teams, that are most effective.

For example, executives at a global investment bank conducted a network analysis that uncovered a cohort of mid-tier employees who were enabling other team members to cross-sell services.

These “hidden integrators” were responsible for six times the average revenue earned from cross-selling – but they were also underappreciated, and several had recently left the company.

The executives immediately adjusted the compensation system to acknowledge their contribution and reward their efforts.

3) Driving planned and emergent innovation. Innovation relies on individuals with different kinds of expertise coming together and pushing each other in unexpected new directions.

General Motors (GM) used collaboration analytics to achieve this.

GM invested in “human capital”, acquiring startups and bringing in new talent. It also recognised the importance of “social capital”, “networks of ties that connect employees and amplify their individual capabilities”, focusing on creating “adaptive space”, networks linking the innovative elements of the business with the operational elements of the business.

Creating adaptive space required intervention in four networks: idea discovery, concept development, innovation diffusion and organisational disruption.

For example, the internal analytics team studied two concept development teams responsible for transforming ideas into prototypes, finding that one was more effective than the other.

The superior team excelled at forming subgroups to collaborate on a single task. These subgroups then shared their advancements with the whole team, allowing their small achievements to feed into the success of the larger challenge. The superior team also had fewer “external ties” and were therefore “free from outside distractions”.

4) Streamlining collaborative work. More time spent on email, in meetings and on the telephone is leading to “collaboration overload”. Collaboration analytics can be used to identify where excessive collaboration is having a negative effect and streamline collaboration. 

For example, the leader of a drug-commercialisation unit in a pharmaceutical company, looking to increase efficiency in routine decision making, issued questionnaires to team members, analysing the answers to highlight the causes of delays and creating “guidelines for optimal decision making”.

It’s also possible to use existing data to streamline collaboration. For example, a leader in the secondary mortgage market used a “passive data” analytics engine to reveal the reasons for one particular team’s effectiveness.

Team members preferred approval-related emails to approval-related meetings, spending 29% more time than other units on the former and 56% less time on the latter. They were more autonomous, spending 20% less time in meetings with a supervisor present. Their meetings were more efficient, with 40% fewer meeting conflicts and 18% fewer emails sent in meetings.

5) Engaging talent. Analytics can also be used to address “a variety of thorny talent-related issues”.

For example: identifying the collaboration patterns that predict retention; studying networks of high performers in order to replicate their success; refining performance-management processes to identify and retain top talent; and using data to generate greater impact from diversity and inclusion programmes.

Product-development company W L Gore & Associates asks employees to rate each other’s performance, using the information gathered to produce a ranking for each employee that determines their compensation.

In 2015, W L Gore & Associates had 9,000 associates, and this evaluation process was taking up a significant amount of their time. Collaboration analytics came to the rescue.

An automated survey allowed associates to nominate colleagues who could best rate their contribution. An algorithm used this data to determine which associates were in a position to evaluate pairs of other associates. A second automated survey asked associates to rate these pairs. This process took just 15 to 20 minutes, saving an estimated 10,000 hours per year.


Efficient collaboration is critical to the success of your company.

“With collaboration analytics, we can begin to shed light on who needs to collaborate with whom about what, what types of collaboration yield particular results, and how collaboration affects employee satisfaction, performance, and attrition,” write Cross, Davenport and Gray.

Source Article: Collaborate Smarter, Not Harder
Author(s): Rob Cross, Thomas H Davenport and Peter Gray