Ideation for Research

In any research career where you are responsible for conducting or leading a program of innovative research, your success will depend on your ability to generate good research ideas – a process known as ‘ideation’. In your career, you may have already met or worked with researchers who seem to have a near infinite source of exciting ideas for research projects, or can come up with endless new ideas on the spot. But behind those impressive feats is typically a well developed skillset in research ideation. In this video I dive into the ins and outs of becoming effective at research ideation, and the benefits it can have for your career.

I touch on key concepts, like the fact that much ideation is actually a continuous, spontaneous process, where ideas can pop up at any time, and where having a system to note and later follow up on those ideas is crucial. I talk about the various ways in which you can think about whether an early stage idea you have is potentially a good one – including aspects like potential benefit, specificity of benefit, and some meaningful additional contribution over what has already been done in the field. I give examples of ideation in a computer science context, and talk about the different stimuli you can use to drive ideation, from research methodology to end user problems. When ideation is done with a specific purpose in mind – for example for an academic paper or grant submission – there are extra factors to consider, like topicality, fit for your profile and interest level. Finally I highlight how being good at ideation is a universally useful skill, across a number of research-orientated career types, and for both early and late in your career.

Check it out, via this Podcast, on YouTube, or via podcast.

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Timestamps are as follows:

 (0:00) Introduction to Ideation in a Research Context
 (0:53) Research Ideation is an Ongoing Process
 (1:27) Ideas Can Pop Up at Anytime
 (1:51) Jotting Down Ideas For Later Consideration
 (2:39) Consider a More Detailed Ideas Log As Well
 (2:51) What Makes For a Good Idea?
 (3:02) The Need for Some Type of Benefit
 (3:11) Three Benefit Examples in a Computer Science Context
 (3:49) Separate Benefit Articulation from Chance of Success
 (4:05) Benefit Can Be Immediate and Stand Alone or Long Term and Integrative
 (4:18) Being Able to Articulate the Potential Benefit is Key
 (4:35) Specificity Can Help Refine Your Ideas
 (5:07) Specificity Example: Computational Advantage
 (6:23) Benefits are Very Rarely Evenly Spread
 (6:45) Ideas and Novelty
 (7:14) Not Everything Has to be Novel!
 (7:26) When Learning to Ideate, Don’t Obsess Over Novelty
 (8:08) Ideation Stimuli: Approach Versus End-User Problem
 (8:24) Research Approach Example: A New Machine Learning Technique
 (8:35) End-User Example Problem
 (8:54) Choosing Between an Approach versus Problem Perspective
 (9:12) Sharing Ideas with Peers: Pros and Cons
 (9:38) Ideation in Your Area of Expertise is Usually More Efficient
 (10:22) Ideation Driven by Areas of Interest
 (10:40) Remember Interest and Capability Don’t Overlap Exactly
 (10:55) Ideation with a Specific Outcome in Mind
 (11:13) Balancing Opportunity with Your Capability Fit
 (11:50) Ideation is Useful for Many Career Types and Stages
 (12:19) Having Some Ideas Helps Generate More Ideas!
 (12:34) The Joy and Excitement of Ideation