I read with interest a small article in New Scientist* about how the COVID-19 pandemic has led to a flood of misleading science, writes Steve Bone, a seasoned R&D practitioner.
This quote stood out “… There is widespread confusion and scarce resources being squandered. There are many causes of this, but the main one is that masses of people suddenly have access to raw scientific information – without necessarily knowing how to judge it – plus the tools to spread their opinions of it far and wide. This isn’t an elitist gripe, merely a simple statement of fact: becoming, say, an epidemiologist takes many years of education, not a week scanning scientific preprint studies and a working knowledge of spreadsheet graphing tools or Twitter”.
I thought that this had parallels with R&D management. Quickly assessing a mass of diverse and sometimes misleading information is exactly what many R&D managers have to do every day, with much of the decision making based on experience and gut feel.
The R&D Manager has to be an instant expert in an area that takes a specialist years to get on top of. The problem is more acute in large R&D departments, where the science and technology is broad.
In this situation, it’s human nature that the manager will use data that is easier to understand and agrees with or fits their world view. The result of this conformational bias is that they could easily miss the important areas that are crucial to a good decision. (see also Intuitive vs Analytical thinking in Technology Management, a white paper by Steve Bone, R&D Today, Oct 2017).
Effective decision making
In order to make decisions, managers must understand the nuances of the scientific discipline and also to see the top-level commercial view of a non-specialist. That’s very hard to achieve particular as the R&D landscape is highly competitive and evolving fast. To have a good chance of success a good manager will recognise they can’t be experts in everything and they need to understand their cognitive biases before making decisions.
To be successful, R&D Managers need to work efficiently and be confident in their R&D areas.
This requires ready access to high-quality, up-to-date, and very accurate information. Yet with the volume, complexity and interconnectedness of data increasing daily, it can be challenging to keep up with the latest developments and even more so understand them.
Several well-tried approaches are discussed in literature and used every day in R&D departments to try to avoid expensive poor R&D decisions. These approaches can be useful when trying to align a good flow of science and technology information with sound R&D decisions.
Useful decision support tools (not in any order)
1. Data analytics: A process for scanning, capturing, and assessing the quality of this information is the first important step. Data has the potential to inform, enable, and transform or disrupt R&D management across four dimensions: strategy, people, technology, and process integration; this is useful when information is categorized and in similar formats. However, whilst there are vast improvements in AI, the nature and extent of that impact is somewhat uneven among different industry sectors.
2. Tools & Techniques: decision-making tools from simple ranking against weighted criteria, through portfolio techniques to Option Pricing and other financial platforms can provide objective metrics. These tools work well as objective team-based decision-making methods but rely heavily on having access to good fact-based information without misleading science.
3. External objective experts: The use of external expert advisory boards offers a good screening process for management. This relies on finding the right team-spirited experts and good facilitation. Scouting for, connecting with, and integrating the right experts is time consuming and difficult requiring a specific skill and good global information gathering tools. It also requires internal management to remove blinkers and cultures to listen to these experts.
In practice it takes all three of these approaches to make good decisions about the management and future of R&D.
Many companies are doing this, but many are not. I remember a 25 year old paper** that describes the “best” decision-making companies as those that: “Establish an explicit process for aligning R&D with corporate strategy; employ metrics that measure this alignment; and maintain a fertile organisational setting that supports quality decisions and the implementation of change efforts.” Aligning R&D with corporate strategy relied then on good scientific information.
Therefore in my opinion it is even more important now to acquire good information because there is a higher chance of low quality or even misinformation getting through.
Returning to Covid-19
The global pandemic is brutally exposing how reliant R&D management is on high quality research, collaboration, and data sharing.
A recent article in The Conversation, an independent source of news and views sourced from the academic and research community and delivered direct to the public, said:
“Speed is, of course, important when a crisis such as COVID-19 is at hand. But speed – in research, the interpretation, and the implementation of science – is a risky endeavour. Coronavirus research done too fast is publishing against the safeguards, and bad science is getting through.”
This was also mentioned in a BMJ blog on 26th May:
“The immediate and long-term consequences of covid-19 will occupy the scientific community for many years to come and the scientific publication platforms will certainly seek solutions to adapt to these challenges.”
In my opinion this will also affect all forms of science and technology information and the R&D management processes will have to adapt to these new pressures.
To overcome this, we still need to adhere to long established R&D management principles practiced over many years by industrial R&D managers but they will need modified based on the new realities facing all science.