The future is digital, but not in the way we think
The question I get asked most often in conferences around capability building is, “What skills should I focus on, to be successful in a digital future”. I’ve always suspected that’s a loaded question. After all, if you ask a Global IT executive that question, you’re not expecting a response of “circus trapeze artist”, but most likely some variation of “computer science”. However, as an aside, are you really sure you want to ask any type of expert a question about the future? You see, when experts are wrong, they can be horribly off the mark.
When experts go horribly wrong
Consider these opinions from very reputable people about the future.
“Television won’t be able to hold on to any market it captures after the first six months. People will soon get tired of staring at a plywood box every night.” — Darryl Zanuck, executive at 20th Century Fox, 1946
“The horse is here to stay but the automobile is only a novelty – a fad.” — President of the Michigan Savings Bank advising Henry Ford’s lawyer, Horace Rackham, not to invest in the Ford Motor Company, 1903
“The Americans have need of the telephone, but we do not. We have plenty of messenger boys.” — Sir William Preece, Chief Engineer, British Post Office, 1876
"I think there is a world market for maybe five computers." Thomas Watson, president of IBM, 1943
"There is no reason anyone would want a computer in their home." Ken Olsen, founder of Digital Equipment Corporation, 1977
You might charitably call these “a swing and a miss”. More bluntly, you might wonder what these guys were smoking when they said this. After all, these were all experts in their field.
So, should we be asking any type of expert about the future?
The future is digital.
Seriously, everyone knows that the future is digital. But, what the best visionaries and change leaders know is that this statement cannot be taken literally. The risk with being literal is that you go down rabbit holes of technology in an attempt to find digital versions of products (digital ice cream?), services (digital dry cleaning?) or work processes (digital financial analysis?). To be fair, some of these are important ingredients for the digital world and they are future-oriented. However, they are all means to a digital end.
What most successful change leaders do, is to understand the context of the world around them, and then deliberately go about creating the future by marrying their own capabilities with future trends.
Where’s the future headed?
So, what key context should we examine regarding the digital future? Here’s a few examples I’d like to share to make a point about a digital future. This is what technology will bring to future life as we know it.
- Driverless cars, which were just a dream a few years ago, have gotten to the stage where children being born today may never need to apply for a driver’s license.
- Between 40-50% of jobs in the manufacturing, transportation and retail sectors could be done by hardware or software robots in the next 15 years.
- Even robots in manufacturing will be disrupted in the next 10 years, as 3D printing takes over. If you can print your PC or smartphone at home, you eliminate robots in the factory.
- Certain news agencies already generate 90% of their short, pro-forma real-time news updates on sports and financial markets using software robots. Artificial Intelligence (AI), with some human journalist help will generate 90% of all news in 15 years.
- Voice recognition is already 3 times faster and more accurate than typing. In the future, Natural Language Processing bots will understand and execute most of the day-to-day tasks at home and at work.
- Deep Learning can already read your lips with more than 90% accuracy, while the average lip reader usually delivers 50% accuracy.
- In 20 to 30 years, the cost of producing energy at home will be a fraction of the cost of buying it off the grid.
- More importantly, it’s the consequences of cheap electricity that are more exciting. Cheap electricity means cheap drinking water, as energy allows you to process all kinds of water including sea water.
- In the next 5 years, there will be apps that can tell by your facial expression if you’re lying. Imagine what that could do to the judicial system!
Wait! Don’t all these examples simply illustrate the criticality of building technical capabilities? No. Not necessarily. Let me share one final statistic to explain why.
- Over the next decade, modern manufacturing in the US will create 3.5 million new jobs. But, up to 2 million high-tech manufacturing jobs may go unfilled for lack of higher-skilled factory workers.
You read that right. In this case the gap will not be in IT programmer availability but in factory workers who know just enough digital technology to operate high-tech machines.
Follow your passion, but in a high-tech way
The future world will need lawyers, and bankers, and CEOs, and businesspeople, and teachers, and nurses, and sanitation workers, and cooks, and accountants, and priests, and factory and farm workers, and yes, politicians. Recent studies on the workforce of the future have demonstrated that beyond a technical digital skills shortage, the bigger skills gap will be related to right brained work. The future of the ice cream business isn’t necessarily a dystopian one where bits and bytes replace a snow cone, but in reimagining how we might better meet the need of ice cream consumers using digital technology. Design thinking, imagination, visual and intuitive product and service design, change management, bringing your organization along – these are all on the critical path to a digital future.
Obviously, this doesn’t take away from the need for a minimum level of digital literacy. We will all need a certain minimum amount of high-tech WITHIN OUR RESPECTIVE FIELD. That technical knowledge doesn’t have to be a one-size-fits-all skill like AI programming. It must be relevant to your field. So, if your field of passion is say, teaching, then keep building capabilities in that area. But ensure that you study enough digital teaching skills so that you can be the most relevant leader within the teaching field.
Agile science or why we need a change of mindset about project management for academic research (and how)
Image from Pixabay
Science advances through projects, and projects are the very basis of scientific research. From master and doctoral thesis involving a handful of people to large international collaborations with hundreds of team members, academia is full of examples of projects. Fieldwork campaign, satellite missions, laboratory analysis, numerical modelling experiments are only a few examples of the type of projects one can find in science. For scientists, project management is a tool that helps us carry out our research in an organised and sensible way to decrease the chances of errors and failure and increase the impact of our research. Scientific projects are most often international, interdisciplinary, intercultural and intersectoral and thus require tailored project management approaches.
Research project management is pervasive and becomes more and more required by funding agencies as an integral part of research project proposals. Together with scientific creativity, good research project management is one of the keys for a successful project. Good management ensures a high impact and helps demonstrate the effective use of tax money within science.
However, research project management is still not implemented as a standard procedure in science and is often also not properly acknowledged as instrumental for the success of a research project. In many contexts within science, in particular when it comes to training at the early stages of a scientific career, project management is often considered a “soft skill”, something that adds value to a curriculum, but not as essential as other more technical aspects of science (e.g., programming, laboratory methodologies, sampling or fieldwork). This needs a shift in the current cultural mindset and this shift is only possible if all science stakeholders, including project scientists, funding agencies representatives, organisation executives and project managers themselves, contribute.
An interesting opportunity to change this mindset presented last February 2020 when I was invited to participate in a 2-days symposium organised by the German Project Management Association specifically aimed at exploring how some modern project management techniques popular in sectors outside academia could be implemented to boost scientific research.
The symposium, first in its kind, was hosted at the German Research Centre for Environmental Health in Munich and opened by its scientific director Prof. Stephan Herzig who stressed the importance to combine world-class science with world-class project management to ensure scientific advancement and how investment in project management is needed to make science more effective.
Modern project management uses appropriate methods depending on the situation. It can include traditional plan-based methods as well as agile management tools. Accordingly, the content of the symposium focused on five methodologies: design thinking (how to create new project ideas), project canvas (how to plan the project), lean start-up (how to start own company from your scientific idea), agile/scrum (how to develop services), and Kanban (how to manage the workflow). The format of the symposium included general introductions of the methodologies and activities in groups to practice how to implement them in scientific activities. The idea was to give the participants an overview of the techniques for us to pick the most appropriate depending on the project or context.
Each of these methodologies has its strengths and can be applied to the many tasks of the researcher/science manager (e.g., write manuscript/prepare conference paper, write progress reports for a funding agency, review manuscripts and conference papers, develop a strategy for the research group, prepare and conduct lab experiment, work on PhD thesis, project team meetings, recruitment, write travel grants/proposals for a new project).
Some project management tasks in science include structure, assign, and schedule tasks, organise meetings, ensure the quality of results, report on performance indicators, manage costs, facilitate creativity. Modern project management methodologies can be applied to each of these tasks and help save time for research.
One major benefit I gained through this symposium is learning how to frame these methodologies in my daily work. I realised that I was already partly applying them to perform my tasks, but understanding the full methods made me appreciate their potential for more applications.
Design thinking is extremely helpful to boost scientific creativity and guide the brainstorming process to develop new scientific ideas which will be the basis on which to build the research project. Design thinking is a structured process to come up with new ideas for solutions to existing problems so it is ideal to explain the motivation for scientific research.
Project canvas is absolutely valuable when planning your project at a high level but detailed enough to appreciate its value. A canvas can be also used to explain your project in one page and have all the important information clearly visible.
Many research results have the potential for start-ups so why not use lean startup methodologies to transform an idea into innovation and start-up (spin-off)? Personally I haven’t used this so far in my work but I can see that this method will become useful for example when identifying a project’s key exploitable results and their potential applications.
Agile is the next frontier for science. As the academic world and research funding structures are by definition inflexible, there is huge potential to apply agile methodologies and scrum in particular in the smaller bits composing scientific research. For example, we apply agile methodologies for software project management in the context of Earth system modelling but also in the coordination teams of large international projects to coordinate the research in a work package leading to a defined deliverable.
Kanban is a fantastic way to visualise and follow on the work to do and to collect new ideas. In the context of scientific management, we use it to develop the project communication and outreach strategy and to organise events. Not only Kanban is useful to guide brainstorming discussion about new ways to communicate science and our project results, but also to keep track of progress, manage the workflow and implement feedback.
All these project management tools can be adapted to the type of problem we want to address. You might have a favourite methodology to manage your project but generally, and moreover in science research, project management methodologies need to be tailored to the project (e.g., size, budget, context) and the task, and it’s up to a (good) project manager to choose the most appropriate.
Only good project management practices can turn ideas into solutions for scientific challenges. Project management has long been considered opposite to creativity and science, but innovation needs to be managed and supported in order to have an impact, therefore project management is, now more than ever, necessary to make science effective.