Innovation

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.




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

The value of a Human Centred Design approach to Data Science

A flurry of 3am (ugh) thoughts…

  • The ability to plan, build, iterate, and test solutions quickly. 

  • A way to identify the most important questions. To understand the broader context.

  • The ability to hone in on the most useful problems to solve.

  • Real collaboration across team. Better still co-creation,,,

Most likely from reading this (yay!) Harvard Business Review article a few days ago…