Blog

I presented at the ISPIM Conference in 2020 (ISPIM: International Society of Professional Innovation Management) on how co-production can bridge the gap between academic research and industry use-cases creating shared, collective intelligence. Co-production in this context is people with different skills and areas of expertise working on a common goal. By sitting at the intersection of these divergent perspectives, co-product marries aspects of each viewpoint, bringing ideas born in research to life in industry.

Cleaning data is often the primary job of the data scientist, and not necessarily the one they signed up for.  While getting the "words" right with computers is important, it is not fraught with the nuance that getting words right with humans is. Across disciplines the same word can have entirely different meanings and this miscommunication is only one aspect of team building that can go awry.

Innovation requires collaboration, but due to the current state of our filter bubbles, collaboration is stuck in a rut. Data science utilizing knowledge graphs and team and portfolio optimization software can help us climb out. It can increase the scale, the intentionality, and the nuance of how we collaborate. With the right data and algorithms, we can use software to optimize our teams and facilitate innovation.

How do you pick who works together, who reports to whom, and who exchanges information with whom? Usually it gets done within a department, within a project team, or based on some other common ground. It turns out we should be focusing on our differences a bit more.

Human Resources... HR... Human Capital Management, no matter what you call it, people are the beating heart of any organization. Taking a data driven approach to mapping the skill-sets of the people in your organization not only allows Leadership to optimize team formation, but provides an opportunity for hidden talents in your people to emerge.

You can find all sorts of “how to” treatises on assembling a team built for innovation, and they all include some subset of the following concepts - bring in experts, make sure they are diverse, have wide-ranging skillsets and have them be people who aren’t afraid to buck the system or go against the flow. Then don’t distract them with mundane tasks and give them enough time and money to actually accomplish their innovation mandate.

Unlocking team creativity during a pandemic.

For 20 years, I have worked with researchers in academic settings to help them design, develop and obtain resources to support the research they do. Over those 20 years, I have cultivated relationships and developed partnerships to the benefit of these researchers, sometimes brokering relationships and other times developing partnerships on behalf of others. I am not advancing my own research or research interests. Instead, I am developing my own understanding of what others do with their research and communicating that understanding to other audiences. Knowledge transfer of this sort is somewhat common in academia, and in other sectors such as industry or non-profit organizations, this form of communication might also be referred to as marketing, storytelling, and/or knowledge mobilization.

The goal: investigate huge amounts of research data in new ways. The pool for teams: neuroscientists, data scientists, and software developers. The result: answering questions we didn’t even know we had.

Ever work in a place where you looked up to everyone there because they are experts at whatever they do and can pretty much solve any problem that arises? That’s what working at Exaptive is like. It’s a mix of expertise across various computer/data fields that works very well. Ever felt like you didn’t belong in a place like this?