Innovation

What does a data scientist do at a design company?


Having literally just applied for a data science role in at Melbourne-based design company, I couldn’t help but smile when I saw this short blog post about data science at IDEO. Only a few* design companies have integrated data science into their work; IDEO of course, and Dalberg Advisory.

As a global leader in design it’s no surprise that IDEO is leading the charge in this space, and at least once a week I find myself looking at the the IDEO career pages thinking “could I move back to the Bay Area?”. I know most people would probably think “Heck yes! put me on that plan”, but after spending my entire first career moving country/continent every 3 years in order to stay in the field – from Australia, to San Francisco, to Liverpool, Oxford, and back to Australia again – I’m not so sure. Data science was supposed to be the career change that meant I could stop doing that.

So I love seeing articles like this, because a) it makes it easier to explain my data science/design obsession to my family; b) it means that change is definitely coming (“I can feel it in me bones”), and; c) it makes it much easier to convince others about the enormous potential and opportunities that data science brings to design.

Just over a year ago IDEO acquired Datascope Analytics, which is how Lisa Nash started out at IDEO, and why their data science team is still based in Chicago. But it appears they are expanding their data science efforts, with positions advertised in Palo Alto… And while I was in London – working as a data science fellow for a tech startup, IDEO advertised for a Quantitative/Digital Design Researcher, that contained more than a splash of data science. I won’t be surprise me if IDEO London (or perhaps IDEO Europe?) establishes cross continental data science hub.

Meanwhile in Brussels, Dalberg Data Insights is working on a bunch of fantastic international development projects – mainly in Africa, around food security, healthcare, and micro-financing. Dalberg Advisory established their data science arm in 2017. At the end of my data science fellowship I couldn’t help but swing by Brussels on my way home, just to check out the city…. you never know…

Fortunately we have some fantastic design agencies in Melbourne.

Portable recently dabbled with data science as part of a their Design for Justice project, and for at least one of their internal projects. More recently Paper Giant advertised for their very first data science role** which is really exciting. I’ll keep you posted on that front. I have no doubt there will be some tough competition.

 

So who benefits the most when data and design come together?
The data scientist? or the designer?

 
 

The answer…

 

Both. Equally.

Designers benefit from having a data-driven design approach, where data is brought to the forefront of the decision making process. Rather than trusting their gut, or intuition, they have hard data to back things up. I can imagine scenarios where hard data might be really useful, where user interviews or anecdotes are hard to synthesise, or in cases where they may be misleading, or where there are many different perspectives.

 

The job of a good data scientist is not only to solve problems, but also to discover the questions worth asking.

 

Data scientists benefit from having a human-centred approach, where decisions around how to best to select and analyse the data, or to build the right algorithm or visualisation, ensure that the design goals are met. Combine that with rapid prototyping, where feedback can be integrated quickly, and you will undoubtably end up with a better design-driven data science solution.

– A.B.



*based on the two that I know of…

**she says with fingers tightly crossed….

Footnote:

I couldn’t help but love that podcasts made an appearance in her post, and that they’re considered part of her everyday work. About a year ago a friend of mine set up a private Love to Listen Facebook group and my podcast playlist has grown steadily ever since. After finishing Serial Season 3 (amazing — talk about wanting to redesign a justice system… ) I’ve moved on to IDEO Creative Confidence Series and the surprisingly amusing IDEO Futures. Definitely worth checking out…

Designing for Resilience


A few months ago IDEO published an article about designing for resilience and using technology can be leveraged to make services more intelligent and empathetic. Which got me thinking (yet again…) How do you actually do this?

 

How do you back up anecdotes or interviews with hard data? and what are the most commonly used algorithms, platforms and tools that data scientists, designers, and social entrepreneurs are using?


 

Kuja Kuja is one product that has made significant impact in Africa – the world’s first real-time customer feedback platform for refugee environments, I’d read about the development of this before and it’s a great example of how a successful platform co-designed and built for one particular organisation can become a tool help communities throughout the continent.

So one of my goals for this week is to…

Find as many examples of data science projects that have a significant level of co-design involved, whether that be for the social sector, for government, for research, the tech industry, or business; and try and identify the common tools, technologies, algorithms that underpin each one.


– A.B.



How AI can save our humanity

A wonderful talk by renowned computer scientist Kai-Fu Lee (@kaifulee).

AI is massively transforming our world, but there's one thing it cannot do: love. In a visionary talk, computer scientist Kai-Fu Lee details how the US and China are driving a deep learning revolution -- and shares a blueprint for how humans can thrive in the age of AI by harnessing compassion and creativity.

Data Science at Made.com

 

MADE.com is a UK-based online retailer with an award-winning business model. Foregoing traditional storefronts, Made has only three UK-based showrooms and works directly with designers and manufacturers to produce high-quality, thoughtfully designed furniture at remarkable prices.

As a made-to-order online company with furniture moving from ship to lounge room, leveraging production and customer data is critical to managing their supply chain. If too many products are made the company loses money is dock or storage fees; too little and customers wait longer to receive the orders. Made also prides itself on keeping ahead of trends, so predicting customer behaviour is vital to their business.

What I love most about Made.com is their TalentLAB crowdfunding platform. It’s a place to discover new talent and unique products you won’t find anywhere else. TalentLAB was born out of the MADE Emerging Talent Award – an annual competition for up-and-coming designers to break into the industry, and get their product made and sold.

We believe that great design is for everyone. It surprises, tells a story and makes the everyday a little less ordinary. At MADE we design for how people live today. No champagne, no private view, just affordable high-end design at your fingertips.
— Made.com

An evening of networking & talks

We had the privilege of spending the evening with Made’s CEO, Talent Acquisition team, and Data Science team. Being a design focussed retailer, it should come as no surprise that visiting Made’s London Headquarters was a really lovely experience. We were welcomed with drinks and nibbles, and more food following talks from Made’s leaders. Talks focussed on Made’s current and future data science aspirations, and why growing their team is critical to their future success.

Where does Data Science fit in?


 

Understanding the Customer Lifecycle

  • Considered purchases means long sales cycles.

  • Cross device browsing is hard to track – but here to stay

  • User journeys do not start at their site, they start in search engines.

  • There is untapped potential to drive repeat purchases.


Supply Chain

& Forecasting

  • Accurate sales forecasting is key to good lead times

  • Predicting forecast hits at the design stage could be game changing


Customer Service
Automation

  • The customer service team currently scales with sales

  • Advances in AI means many simple customer service queries should be able to be automated.


Visual Analysis
& Recommendations

  • Visual search – finding products of a similar colour, shape, or style.

  • Identifying products that work well together.

  • Learning to understand a customers style.

 
 







Deep Learning with Microsoft Azure

 

Discussing Ethics & Empathy in AI