So far in our video series, Real World Data Science Big Questions, our panel of expert data scientists have explored the biggest challenges facing the field, how to balance innovation with regulation, and the key role of statistics in AI. In today’s episode, the conversation centers on the most exciting developments coming down the track in the world of data science.
Watch the video and read on below for our analysis of vibe coding, digital twins and more.
Watch the discussion
Takeaways at a glance
- Reusability – rather than just reproducibility – of code and methods is the next frontier.
- Digital twins have huge potential but are still in an early, exploratory phase.
- New tools (especially coding tools) are lowering barriers to entry and improving communication of complex ideas.
- The rise of “vibe coding” and similar approaches is dramatically increasing development speed.
Key themes and analysis
From reproducible to reusable data science
Rhian highlights an important shift: reproducibility (being able to rerun someone else’s work) is becoming baseline, while reusability (adapting and building on it) is where the real value lies.
This reflects a maturation of the field when you consider that reproducibility supports verification and confidence (“can we trust this result?), where reusability supports acceleration and scale (can we take this and build on it?) The implication is that tooling, standards, and culture should increasingly prioritize modular, well-documented, interoperable outputs. “Clever but opaque” solutions become less valuable; simplicity and interface design become more important and standardisation becomes a force multiplier.
Acceleration through tooling, but not automation without expertise
Janet is excited about the speed gains from modern tools, including AI-assisted coding (“vibe coding”), but is careful to frame this as augmentation of experts rather than unregulated democratisation. In the hands of experts, new tools can lead to exponential productivity. In the hands of non-experts, they create a real risk of misuse.
This reinforces a key tension in modern data science, where an influx of new tools are making it easier to build solutions, but not necessarily easier to build correct solutions.
Lowering barriers to communication
Beyond technical productivity, tools are also improving how data science is communicated, making complex ideas more accessible, supporting scientific communication and bridging the gap to general audiences
Digital twins as an emerging frontier
Edith mentions digital twins — a high-potential but not yet fully realized concept that would allow for replicating real-world systems digitally, and enabling simulation, testing, and prediction at scale.
A digital twin is a continuously updated simulation of something real — a machine, a city, a supply chain, or even a human system — fed by real-world data. Moving far beyond a typical static model, a digital twin provides a live, evolving digital model of a real system that you can test against, experiment on, and optimise without touching the real thing. This means that instead of asking “what is likely to happen?” you can ask “what happens if I change this in the real system?”, shifting from prediction to experimentation.
Applications of the digital twin concept are already emerging in manufacturing and industrial systems (simulating production changes before implementing them) and urban planning (simulating effects of new housing developments or managing congestion manually). At a more advanced frontier, digital twins could be used in a healthcare context to simulate how a patient responds to treatment, test the effects of drugs before prescribing them, or even to plan surgeries using personalised anatomical models.
For now, the concept is promising but the practical frameworks, standards, and widespread adoption are still evolving.
Real-time responsiveness as a new capability
A key implication of faster data science workflows is the move toward real-time or near real-time responsiveness. Where analysis once took days or weeks, improved tooling and reusable components now allow teams to identify and respond to new patterns much more quickly. For example, emerging fraud patterns can be detected and addressed without building solutions from scratch.
As a result, latency — the delay between observing and acting — becomes increasingly important, and iteration cycles become more continuous. Rather than producing insights at fixed intervals, data science is more closely embedded in ongoing decision-making.
Overall, this reflects a shift from batch-style analysis to more responsive systems, where value depends on how quickly insights can be turned into action.
An inflection point
Hearing what our panel is most excited about at the moment makes clear that data science is at an inflection point. Technically, the field is accelerating rapidly, driven by advances in tooling and AI. In practical terms, there is a clear shift toward reusability, greater speed, and more real-time application of methods and models. At the same time, there are increasingly urgent social considerations, particularly around ethics, communication, and governance.
Taken together, these trends point to a broader change in where value lies. The most significant gains are unlikely to come solely from developing better algorithms, but from how effectively those algorithms and methods are reused, communicated, and deployed in a responsible way.
We want to hear what you’re most excited about, so get in touch.
- About the author:
- Annie Flynn is Head of Content at the Royal Statistical Society.
Copyright and licence : © 2026 Annie Flynn
This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence.
How to cite :
Flynn, Annie 2026. “RWDS Big Questions: What excites you in data science at the moment?” Real World Data Science, 2026. URL