Guest blog from Data Lab MSc student, John Bell
I was lucky enough to have been one of the 130 Data Lab MSc students from across Scotland who took part in The Data Lab MSc Innovation Week at the start of June. Joshua Ryan-Saha, The Data Lab’s Skills Programme Manager, recently wrote a blog post on the event and this post is intended to be a companion piece, sharing my thoughts on what the event was like from the perspective of a student, shortly to embark on a career in data science in Scotland.
As Josh described, Data Lab’s aims were to help solve ‘real-world’ challenges, help students learn approaches to defining such problems, and foster a community of data science students from across the country.
And how did they fair in their aims? Read on, and I’ll give you my take.
Innovation Week? What’s that all about?
Let’s start with our expectations prior to the event. What did we as students expect the week to be like? Personally, my expectations were that the week would be an extended data hackathon where we’d be presented with challenges and relevant data sets and would be supported in applying the (predominantly technical) skills we’d developed over the course of the year in order to use this data to develop some ‘solution’ to the challenge we’d been presented with. In other words, a week of hard-core data analytics and coding.
And that would have been good. It would have been fun, and I’m sure we’d all have learned a lot and gone home happy at the end of the week. But it wasn’t what happened. What The Data Lab did was much more innovative and much more interesting than that.
The week that was
Before describing what was so innovative and interesting, let me first give a very brief summary of how the week was structured.
On the first day we were presented with six diverse and interesting challenges that had been posed by Heineken, NHS National Services Scotland, National Biodiversity Network, Tesco, ScotRail and marine tourism organisations in the Highlands & Islands region, challenges such as:
- How might we increase the benefit of marine tourism?
- How might we help develop high-performing teams?
- How might a better understanding of how people use prescriptions help improve patient outcomes? and,
- What makes a great beer? (a surprisingly popular challenge for some reason)
We formed teams and started to consider ways of approaching what we needed to research to understand more. (What we didn’t do, despite the fact that many of us were totally itching to do it, was to consider ways we could solve the problem. More on that later.)
Over the subsequent days, ably guided by the excellent facilitators from the Glasgow-based service design agency Snook and The Data Lab, we learned how to design data-fuelled user-centred products or services.
We had input from the challenge-owners in person with a chance to interview them over the week, and each day there was a talk from a guest speaker.
On the final day each group gave a 1-minute pitch about their product or service. There was everything from the standard ‘elevator pitch’ to a call-centre-based mimed drama which included an enactment of the worship of the ‘god of data’. I’ve never seen that before and am unlikely to see it again. The only thing missing was that no team tried to communicate insights from data through the medium of modern dance, but hey, you can’t have everything. This was followed by a ‘Show, don’t tell’ activity, where each team had set up an area to communicate their findings, their approach, what they’d learned, and of course, their product/service.
The week culminated in the teams being given feedback from a panel of experts comprising challenge owners, guest speakers and Sarah Drummond, founder and Managing Director of Snook. By which point everyone was elated, exhausted and ready to hit the pub.
Why was the Innovation Week innovative and interesting?
I think there’s a tendency for technical people (and I include data scientists in this category) to be very solution-focused, technocentric (i.e. to believe technology holds the key to solving many problems) and to be comfortable with clarity.
And I think that one of the things that the Innovation Week did very well was to shake that up. Had the week been the standard hackathon format, we’d all have felt very comfortable, but it wasn’t, and we didn’t.
Getting a brief from a client, doing some data analysis, and designing and building something based on that data is easy. (Well, relatively so). We’d all be well within our comfort zones with that. But understanding the genuine business needs of a client and using data as a tool to understand these needs better (along with all the other ways of researching) and designing and making something that really addresses these needs? That’s hard. Really hard.
The focus of the Innovation Week was not about applying technology to solve a problem. It wasn’t about technical skills. It wasn’t really about the data either. Sure, the data was a part of the jigsaw, but just a part.
What The Data Lab gave us during the week was a chance to complement the technical skills we had learned at university over the academic year with learning new user-centred design skills. This was the clever bit.
The focus of our learning was design research, research embedded in the process of design. Using the Double Diamond model for the design process, we learned a range of tools and techniques that can be applied to user-centred design: how can we design and build data-fuelled products and services that address a genuine need?
Key learning points
Here’s a brief summary of the key take-away points from the week for me.
Really understand your users: there are many techniques and tools to help you understand your users better such as problem definitions, personas, user journeys and stakeholder maps. Use them. Get as deep an insight into your users as possible their pain points, their motivations, their lived experiences.
Get used to not knowing: learn to be comfortable with the jelly-like not-knowing phase of design where everything feels messy.
Don’t jump into solutions: take time to understand. Research actively and understand more.
Iterate: Iterate again. And again. ‘Build it, break it, burn it to build the right thing.’ Don’t spend lots of time building. Keep it simple. Keep returning to your problem statement and the needs of the user. Test your ideas.
Don’t be led by the technology: it’s tempting, but it’s just a tool. Stay agnostic for as long as possible.
Understand the role of data in design: combine data analysis with design research and desk research.
‘Don’t fall in love (with your ideas)’: Ideas are just ideas. Ideas are cheap. Be prepared to let go in the light of new (and better) understanding.
‘Think with your hands, not your head’: Don’t have conversations that are just conversations. Ideas and information get lost. Make something that captures the conversation. A picture, a model, a post-it, whatever. An uncaptured interaction is a wasted one. ‘Do, don’t talk.’
Reach out: when you need help, use your network, other people’s network, social media. There are other people who may will have an interest in solving the problem you’re trying to solve. They may well be happy to help.
Think big: don’t be scared to take risks. Ask ‘What ifâ¦?’ questions. Open up the problem.
The data’s not perfect: it won’t give you all the answers. It’s messy and incomplete. It is what it is. Use it as one of the elements of design research. Knowing what the data isn’t telling you can be useful. Knowing what data should be collected can also be useful. Can you supplement it with another data source? Can you complement it with qualitative research?
Prototyping rocks: you’ll discover things about the pain points and needs of stakeholders using prototypes that you just wouldn’t do through interviewing them (even if you were an expert interviewer). Simple is fine. Paper is fine. Don’t invest lots of time making something wonderful.
‘Future evidence’ rocks too: future evidence helps you (and your stakeholders) envision the situation in which the needs they have are addressed by the product/service you’ll build. It’s motivational for the team too. Check out this video produced by The Yes Men it’s worth watching.
The Future’s Bright
Judging from the talent on display during the week, we’re in a good place. The Data Lab MSc students from 11 universities across Scotland, and originally from across the world, displayed a wealth of creativity and innovative thinking (and doing!) during the week. Collaboration within and across teams was strong, as was a willingness to think big and take risks.
The students are a talented and diverse bunch and will bring a lot of positives to the data science scene in Scotland.
I’d like to thank The Data Lab for organising the event and to Snook to facilitating it in such and energetic and creative way.
You can hear more from Data Science Rookie, John Bell, on Linkedin, Twitter, and on his website.