Today’s business climate demands a dynamic and collaborative workforce. In order to address the complex hurdles that companies face, they must find ways to break down problems and work in a multidisciplinary environment. Although many companies espouse their support of the value of collaboration, far fewer spend the needed time to actualize its potential. If you are looking to increase efficiency, reduce churn and increase ROI for various product lines, it is essential to embrace analytic collaboration. I define analytic collaboration as the meeting of people, preferably from multiple departments, to problem solve solutions through quantifiable and testable trial and error evaluation.
To illustrate what I mean I want to use a case of analytic collaboration.
- Problem: While developing an air compressor, a certain part, a one way valve, is found to be not holding pressure
- Causes:The team meets with personnel from multiple departments to discuss potential causes. People submit several hypotheses;
- Could be a problem of design tolerances in Solidworks
- Could be the valve is not properly secured to the tube or mount
- Could be an inefficient seal from the epoxy
- The valve could be not designed for our application (either not designed to be fitted to the tubing that we are using or not designed for our pressure range etc.)
- Could be human manipulation during installation
The list could go on. Now the team should break apart and evaluate each of these hypotheses.
- Solutions: Here it is critical to challenge assumptions and share information. Try to isolate variables. Go back to square one, could the problem be something basic? Record data and gather feedback.
This example of analytic collaboration revolves around testing and critically evaluating different theories. To be successful and avoid wasted time, it also requires information sharing. In fact, analytic collaboration requires three central tenants: Challenge Assumptions, Share Information, Gather and Use Feedback.
A huge problem with collaboration today is: 1) It lacks data to back claims/ideas 2) It fails to question existing assumptions that may be hindering progress 3) It is often not across multiple departments and thus many people are coming at problems from the same direction 4) It is not multi-phased. Testing is not being done to validate hypothesis 5) People aren’t being rigorous enough in gathering, compiling and using feedback (either from tests or from clients, depending on your problem).
Remember, that when you bring a group together to discuss a problem, effective solutions often take time. If you rely on what people have to say off the top of their heads in a 30-minute window, you will often get superficial answers. Addressing problems from an iterative vantage point allows collaborators to come to the table equipped with data and feedback to fuel solutions. Companies that master the art of analytic collaboration will enjoy continual innovation and progress.