I’ve recently bumped into a couple of thought-provoking R&D research studies that inspired me to dive in and elaborate further on their findings.
One recent study by Deloitte suggests that although supply chain disruption is the top challenge that organizations across different domains are grappling with today, they’re also facing growing pressures due to continuously accelerating innovation development cycles and faster time to market.
Another study by Gartner finds that “attracting and retaining technical talent” is the top challenge facing R&D leaders in 2022. In particular, 96% of R&D leaders surveyed by Gartner say it’s critical to solve this challenge for the overall performance of their department, and 53% of leaders are confident in their company’s ability to solve it in the next 12 to 18 months. One more significant challenge identified by Gartner is that 81% of R&D Heads find it challenging to optimize their R&D resourcing.
These findings imply that the lack of qualified R&D talents can significantly slow down corporate innovation in general and next-generation product development. This can drastically affect the evolution of the cutting-edge technologies required to remove current technical constraints and find non-trivial, outside-the-box solutions for newly discovered problems that had never existed before.
To better understand the difference between R&D engineers and mainstream, regular developers and how to bridge the R&D skills gap effectively, I’ve talked to Victor Dornescu, Chief Innovation Officer and Co-Founder of a Romanian software company rinf.tech that has its own R&D Center for Embedded Development and IoT in Bucharest.
Victor, what defines a good R&D strategy today?
Essentially, it’s a Team. A cohesive multidisciplinary team can drive any R&D project to success and bridge the gap between science and engineering.
For R&D engineers to bring value to any research, they must work closely with people skilled in many other areas and leverage the so-called “group intelligence.”
Working in a multidisciplinary team can help engineers abstract from generic findings and self-evident conclusions and focus on discoveries that can create from scratch or disrupt and enrich the existing body of knowledge on a particular subject matter.
That being said, a good R&D strategy is to:
- properly define the problem that needs to be solved through research,
- assemble a team that will provide space for people with different skill sets and backgrounds to complement each other’s knowledge, and
- expand and narrow the scope while filtering and venting the backlog by killing zombie ideas/features that will take the team astray.
Given the current shortage of R&D engineering talent, as indicated by many recent research studies, where can businesses (especially the non-tech ones) looking to innovate and indulge in engineering research source such talent? And what should they focus on when building their R&D teams?
Co-creation is emerging as today’s innovation model of choice, with corporations exploring collaboration with academia, startups, custom software development providers, and even competitors.
Pairing engineers with scientists and researchers to work on R&D pilots and proof-of-concept (PoC) projects isn’t easy, as these two groups of specialists are used to different methodologies and approaches. Scientists discover new things while engineers make things work.
So, answering your question, there’re a couple of factors to consider.
First, make sure you have the ability to search for diverse, multidisciplinary talents both inside and outside your organization.
Second, when creating perfect candidate profiles, focus on attracting people from different domains with strong system- and innovative thinking skills.
Third, make sure they have done the same or similar thing before and have relevant composable (reusable) assets to speed up the work.
Can you provide a real-world example?
Some time ago, Honda Research Institute built a multidisciplinary R&D team to explore in-depth Multithreading in JavaScript using a JSEN data structure format. As they were looking to integrate real-world software engineers with their research team, they turned to rinf.tech and involved three of our team members as research contributors. Our employees participated in the early discussions on the research concept, reviewed the study, and helped develop the virtual language and debugging tool to validate the findings.
That being said, Honda Research Institute assembled a multidisciplinary team and created conditions supporting cross-skill collaboration, brainstorming, agility, innovative thinking, and others. Thanks to this strategy, they came to several important conclusions like this one: with JSEN, it is possible to execute functions concurrently, avoiding blocking the JavaScript main thread.
Today, there’s an acute need to integrate scientists and researchers with coders and software developers, architects, hardware engineers, business analysts, marketing and HR people. Instead of applying T-matrix when evaluating potential R&D team members, consider applying the so-called π (pai)-matrix.
Can you explain more about the π-matrix?
It is a concept first coined by Airbus. In the past, people were expected to specialize in just one technology area (a vertical line in T-matrix) and have some knowledge of related technical topics (a hotizontal line in T-matrix).
In software engineering, this created a situation when developers lacked business understanding and didn’t share their clients’ mission, vision, and values when building their custom products. This resulted in a strong technical focus but a disconnect from the development strategy, engagement model, overheads at post-production stages, cumbersome, buggy, and partially effective solutions, and other negative business consequences.
Over 70% of the code written is never used.
A new breed of engineers should have multidisciplinary technical specialization plus related technical disciplines as in the full T-matrix), as well as a good understanding of business functions (the horizontal line in the π-matrix) paired with mastery of the innovation contradictions (second vertical line in the π-matrix).
π-shaped engineers typically work at the intersection of technology, business and society and understand the key performance indicators and functions such as sales, marketing, and HR to be able to deliver actual value and drive modern research.
To better manage expectations and R&D outputs, they need to be flexible in changing things on the fly, as projects can change a lot after an MVP stage and target user feedback.
They need to be elastic in giving up their planning and direction for a pivot towards a new set of objectives, then renounce their attachment to the comfort of carefully built client relationships and select just a few out of many in order to essentialize new features and scale with those only.
They are required to mind-bend in changing research concepts and shifting from an initial focus.
It may seem like a straight line, but it’s cyclical like innovation itself, so the engineers must accommodate contradictory directions.
Summing up, the ‘T shaped’ engineers are those who appreciate the existence and importance of other engineering roles and technical disciplines and can cross technical boundaries conceptually and in terms of vocabulary and ways of working. Extending this ‘T’ to ‘π’ means engineers are additionally able to work effectively with all business functions (2nd horizontal line) - marketing and sales, services, finance and procurement, etc. - which must be supported by the innovation competency (2nd vertical line).
And what constraints exist in transitioning from a T-shaped into a π-shaped engineer?
I’d say many constraints are coming from the innovation life cycle. Many engineers still live in the paradigms of fixed-scope projects or agile models, which affects how they treat projects and deliver value.
Modern R&D engineers should be able to pivot and change directions. Therefore, they need to keep an open scope and be able to brainstorm, accommodate, reorient and find the way forward within cyclical roadmaps. It’s a new shift in the engineering mindset they should pursue to become π-shaped.
So what does it take to turn T-shaped people into π-shaped ones?
I think this happens naturally. Today, engineers are influenced significantly by entrepreneurs and salespeople, scientists and researchers, HR and marketing people, creative hipsters, growth hackers, hustlers, etc. They form a new social club, and they add value to any type of project, not just R&D.
But still, how is this possible?
Well, the engineers are users as well!
As new technologies emerge, evolve and catch on, more engineers tend to master them, so there’s an interesting trend of “traditional” engineers transforming themselves into data engineers or AI engineers. Considering the boost in Quantum Computing research (with 127 Qubits of data having been processed by Intel to date and 300 Qubits expected to be processed by the end of the year), many new opportunities pop up for R&D engineers to become π-shaped.
So, to wrap it up, modern R&D leaders should be driving the evolution and maturity of a new generation of π-shaped engineers by creating favorable conditions for multidisciplinary collaboration among diverse skills and functions within their teams. On the other hand, academia should promote such new skills among STEM students and adjust their curricula and programs accordingly. This should be a joint effort between the corporate (business) and education (science) worlds.
And keep in mind – it is well underway already.
Images courtesy of rinf.tech