Today I am at the Data Fest Data Summit 2018, two days of data presentations, showcases, and exhibitors. I’m here with my EDINA colleagues James Reid and Adam Rusbridge and we are keen to meet people interested in working with us, so do say hello if you are here too!
I’m liveblogging the presentations so do keep an eye here for my notes, updated throughout the event. As usual these are genuinely live notes, so please let me know if you have any questions, comments, updates, additions or corrections and I’ll update them accordingly.
Intro to the Data Lab – Gilian Doherty, The Data Lab CEO
Welcome to Data Summit 2018. It’s great to be back, last year we had 25 people with 2000 people, but this year we’ve had 50 events and hope to reach over 3500 people. We’ve had kids downloading data from the space station, we’ve had events on smart meters, on city data… Our theme this year is “Data Warrior” – a data warrior is someone with a passion and a drive to make value from data. You are data warriors. And you’ll see some of our data warriors on screen here and across the venue.
Our whole event is made possible by our sponsors, by Scottish Enterprise and Scottish Government. So, let’s get on with it!
Our host for the next two days is the wonderful and amazing Maggie Philbin, who you may remember from Tomorrow’s World but she’s also had an amazing career in media, but she is also chair of UK Digital Skills and CEO of Teen Tech, which encourages young people to engage with technology.
Intro to the Data Summit – Maggie Philbin
Maggie is starting by talking to people in the audience to find out who they are and what they are here for…
It will be a fantastic event. We have some very diverse speakers who will be talking about the impact of data on society. We have built in lots of opportunities for questions – so don’t hesitate! For any more information do look at the app or use the hashtag #datafest18 or #datasummit18.
I am delighted to introduce our speaker who is back by popular demand. She is going to talk about her new BBC Four series Contagion, which starts tonight.
The Pandemic – Hannah Fry
Last year I talked about data for social good. This year I’m going to talk about a project we’ve been doing to look at pandemics and how disease spreads. When we first started to think about this, we wanted to see how much pandemic disease is in people’s minds. And it turns out… Not many.
[Redacted pending show broadcast tonight – then this post will be updated!]
Business Transformation: using the analytics value chain – Warwick Beresford-Jones, Merkle Aquila
I’ll be talking about the value chain. This is:
Data > Insight > Action > Value (and repeat)
Those two first aspects are “generation” and the latter two are “deployment”. We are good at the first two, but not so much the action and value aspects. So we take a different approach, thinking right to left, which allows faster changes. Businesses don’t always start with an end in mind, but we do have accessible data, transformatic insights, organisational action, and integrated technology. In many businesses much of the spend is on technology, rather than the stage where change takes place, where value is generated for the business. So that a business understands why they are investing and what the purpose of this.
I want to talk more about that but first I want to talk about the NBA and the three point line, and how moving that changed the game by changing basket attempts…And that was a tactical decision of whether to score more points, or concede fewer points, enabling teams to find the benefit in taking the long shot. Cricket and Football similar use the value chain to drive benefit, but the maths work differently in terms of interpreting that data into actions and tactics.
Moving back to business… That right to left idea is about thinking about the value you want to derive, the action required to do that, and the insights required to inform those actions, then the data that enables that insight to be generated.
Sony looked at data and customer satisfaction and wanted to reduce their range down from 15 to 4 handsets. But the data showed the importance of camera technology – and many of you will now have Sony technology in the cameras in your phones, and they have built huge value for their business in that rationlisation.
BA wanted to improve check in experiences. They found business customers were frustrated at the wait, but also families didn’t feel well catered for. And they decided to trial a family check in at Heathrow – that made families happier, it streamlined business customers’ experience, and staff feedback has also been really positive. So a great example of using data to make change.
So, what questions you should be asking?
- What are the big things that can change our business and drive value?
- Can data analytics help?
- How easy will it be to implement the findings?
- How quickly can we do?
Q1) In light of the scandal with Facebook and Cambridge Analytica, do you think that will impact people sharing their data, how their data can be used?
A1) I knew that was coming! It’s really difficult… And everyone is also looking at the impact of GDPR right now. With Facebook and LinkedIn there is an exchange there in terms of people and their data and the service. If you didn’t have that you’d get generic broadcast advertising… So it depends if people would rather see targeted and relevant advertising. But then with some of what Facebook and Cambridge Analytica is not so good…
Q2) How important is it for the analysts in an organisation to be able to explain analytics to a wider audience?
A2) Communication is critical, and I’d say equally important as the technical work.
Q3) What are the classic things people think they can do with data for their business, but actually is really hard and unrealistic?
A3) A few years ago I was meeting with a company, and they gave an example of when Manchester United had a bad run, and Paddy Power had put up a statue of Alex Ferguson with a “do not break glass sign” and they asked how you can have that game changing moment. And that is really hard to do.
Q4) You started your business at your kitchen table… And now you have 120 people working for you. How do you do that growth?
A4) It’s not as hard as you think, but you have to find the right blend of raw talent with experience – lots of tricky learning.
- Mapping the water footprint of your crops – a project with the University of Edinburgh, funded by Data Lab. This brings together a wide range of crop data layers. We have an overlay based on water for crop growing, and overlays of gray water, or the erosion potential – for instance there is high erosion potential on the west coast of Scotland, mmostly low erosion in the east of Scotland.
- Forests 2020 is a Mexican application supported by the UK Space Agency, and we work with University of Edinburgh, University of Leicester, and Carbomap. Here we can see deforestation patterns, and particular crop areas.
- Innovate UK: farm data, which is a collaboration with Rothamsted Research, Environment Systems, and Innovate UK – this is at an early stage looking at crop rotation data for UK and export markets. And you can also see the soil you are growing on, what can be planted, what sort of fertilisers to use.
- Sustainability risk – supports understanding of risks such as water depletion, and the various factors impacting and shifting that.
- We also have tools for government to know how to plan what type and locations they should be building power plants in.
So, in conclusion, layering data allows us to gain new insights and understanding.
After a good lunch and networking session we are now back in the main hall, starting with a video on the use of data in Heineken production process. And an introduction to Stefaan Verhulst, a former Glasgow graduate now based in New York.
Data Driven Public Innovation In Partnership With The Private Sector: The Emerging Practice Of Data Collaboratives – Stefaan Verhulst, Co-founder and Chief Research and Development Officer, The Gov Lab
I’m delighted to be back in Scotland for this event looking at how data can be help society, and how society can be. That is also the focus of The Gov Lab in New York. And we also look at how we can unleash data for good.
An example want to give you is the earthquake in Nepal a few years ago. It was a terrible event but it was also inspiring too, because Ncell, a cell phone operator, and Flowminder (based in Sweden and the UK) worked together to map the flow of people to intervene, to save lives. It is a great example of using data in the public good. And it’s an example of the growth of available data, including web crawling/scraping/search analysis; social media; retail data etc. all collected by the private sector. But we also have new data science to address this data, to gain meaning from this data. And often that expertise to extract meaning is sitting in the private sector.
So, the real question is how we extract value and engage with the private sector around data they collect. That’s a whole different ballgame from open government data. It’s not just about data sharing, but about new kinds of public-private sharing around data for the public good. So we have set up new programmes of Data Collaboratives. So we set up the Data Collaboratives Explorer allows you to explore those collaborations taking place – there are over 100 in there already. From that collaborative work we have gained some insights that I will share today.
So, firstly, data collaboratives are important across the policy lifecycle:
- That starts with situation analysis. Corporations in the US have worked together in the US to understand the scale of the opioid epidemic, for instance.
- Our second value proposition is about knowledge creation. For instance, post hurricane season how does the mosquito population change and how does that change mosquito born diseases.
- Our third value proposition is prediction, fr instance projects to predict suicide risk from search results – a project in Canada and also in India.
- And then we have evaluation and impact assessment. An example here is Vision Zero Labs looking at traffic safety and experiments in spatial composition to influence and reduce risk of accidents.
In those collaboratives we see different models in use. These include: data pooling – enabling sharing and analysis across the collaboration; prizes and challenges – opening some data as a source of generating new insights through innovative ideas and projects that benefit both public and private sector, e.g. BBVA’s Innova challenge; research partnerships – with collaboration across private sector and public or academic sector – such as work on fake news on Twitter; intelligence products – JP Morgan Chase has an institute to extract insights from their own data and actually that can be hugely detailed and valuable; API – for instance Zillow allows you to access real time mortgage and housing market data; trusted intermediary – for instance Dalberg who acts between telecommunications companies and others.
So, there are many ways to set up a data collaborative. But why would the private sector want to do this? Well, they may be motivated by reciprocity – sharing data may lead to access to specialist expertise; research and insights; revenue; regulatory compliance; reputation and retainment of talent – often corporations need to retain talent through solving harder or more interesting problems; responsibility.
But there are challenges too. For instance the taxi and limousine agency in New York regulates all taxi operations, including Uber. In their wisdom they shared the data… But that exposed some celebrity locations (and less salubrious locations). The harm here wasn’t huge but that data in a different cultural contexts could present a much higher risk. So, some of the concerns around sharing data include:
- privacy and security
- generalisability and data quality (e.g. not everyone has a cell phone)
- competitive concerns
- cultural challenges – there is something of a culture of hoarding data within organisations.
So, to move towards data responsibility we really need risk and value assessment that recognises data as a process, and part of a wider value chain. We need fair information practices and processes – our principles are about 30 years out of date and we urgently need new principles and processes. GDPR helps, but not all the challenges we may have. We need new methods and approaches. And that means having a decision tree across the data cycle.
There are risks in sharing data, but there are also risks in not sharing the data. If we had not have used the NCell data in Nepal, we would have had more deaths. So we have to respond not just to risks, but also to opportunity cost of not sharing data. What is your responsibility as a corporation?
I’ve given lots of examples here… But how do we make data driven public innovation systemic? We need data stewards in organisations so there is someone who can sign off on data collaboratives, we need that profession in place in organisations to enable work with the public sector. We need methods – like the Unicef collaboratory around childhood obesity, that’s a new methology. We also need new evidence, of how data can be used and what impact it will have. And finally we need a movement – this all won’t happen without a movement to establish data collaboratives, and I’m delighted to be here today as part of this movement, and ultimately use data to improve peoples lives.
Q1) In light of Cambridge Analytica and Trump, aren’t we misusing data?
A1) I think use is part of that value chain and we have to have a debate about what kind of use we are comfortable with, and which we are not. And that case also raises questions about freedom of expression, and a need to regulate against deceptive behaviours.
Q1) Several years ago hashtags brought down governments in the Middle East, and now we have governments in those countries controlling the public through hashtags. It’s scary.
A1) I’ve been working in privacy for many years, and I really encourage a comparison of risks and value. And to do a cost-benefit analysis. We need to rebalance that.
Gillian is introducing our special guest… Minister Derek MacKay
Message from the Scottish Government – Derek Mackey, MSP, Cabinet Secretary for Finance & Constitution, the Scottish Government
I’m not sure that I’ve thought of myself as a data warrior before, but I did teach the Social Security Minister how to use Instagram the other week! I say that partly as I have an appeal and a plea for you… The First Minister has a huge set of followers on Twitter, but I’m stuck just below 18k… Maybe you are the audience to take me over that line!
There’s a lot I want to cover in terms of the excitement of this event. We have a strong reputation and record in Scotland. With responsibility for the budget and internationalisation, this is really exciting. I’m particularly enthused by the international representation including Brazil, Singapore, USA, and Ireland too. This event allows us to put the spotlight on data science in Scotland. It is a natural place for people to come and do business. And this is a great event with business leaders here, with experience to share with others.
Our government, Scottish Enterprise and Data Lab are working together to build innovation and business in Scotland. We are fortunate in Scotland to have world class data resources. Scotland has Universities, 5 of which are in the top 100, and we have 70% of reseach rated as excellent in the last REF. We can feel this group. Data Driven Innovation has the potential to deliver £20bn value to Scotland in the next five years. This buzz can be harnessed to make Scotland the Data Capital in Europe. I paricularly support the growth in FinTech. Many people describe themselves as disruptors – that would have once been seen as a negative but is now a real positive, about opening new opportunities. And data helps us deliver our work, one example of which is the Cancer Challenge which is helping us understand how best to use our resources for the best outcomes.
The Scottish Government Innovation Action Plan seeks to build a sustainable economy, with skills crucial to that, including funding for business growth, innovation, etc. We’ve also launched the Scottish Digital Academy and the Data Science Accellerator to look at how things are changing, to innovate working methods – such as CivTech’s innovative models. We are really serious about business growth, the economy and skills. We have invested in innovation, education and internationalisation. We are the strongest part of the UK outside London and the SouthEast.
So, the Scottish Government supports your enthusiasm for data, for what can be done with data. High tech, low carbon is the future we see that, and we want to be country welcome in Europe and the rest of the world – we don’t support the UK government’s view on Europe.
I commend your work and hope that you have a fruitful and enjoyable time here. And we hope the collaboration of our agencies helps to bear fruit now and in the future.
Improving Transparency In The Extractives Industry Using Data Science – Erin Akred, Lead Data Scientist, DataKind
I am a data scientist from DataKind where we harness data for the improvement of humanity. We exist to use data to see the kind of world we want to see. The challenge we face is that many not for profits, charities, government agencies etc. do not have the resources to do the types of datascience that the private sector (e.g. Netflix) can. So we link pro bono data scientists with organisations with a social mission.
Last year we did a project looking at automating detecting mines from earth observation imagery. We are used to using this data for other purposes, but this is a challenging problem. I will talk more about this but I wanted to talk more about DataKind.
Our founder, Jake, was working at the New York Times on data science, and saw people volunteering and attending hack events at the weekend, giving back on their talents… So he thought perhaps I could partner with a mission driven organisation, could I organise a similar event and make this happen… He started DataKind and we’ve been developing what we can offer these mission-driven organisations who also want to benefit from Data Science. So we now pair data scientists with mission driven projects. We have over 18k community members worldwide, 6 chapters in 5 countries (US, Bangalore, Singapore, Dublin, London, San Francisco, Washington DC), we have chapter applicants in 40+ global cities; 228 events worldwide; and we’ve worked on over 250 projects generating about $20m value generated in volunteer effort.
On example project has been with the Omidyar Network to look at data science solutions that might enable social actors to operate more effectively and efficiently in their efforts to combat corruption in the extractives industry. Now we don’t start with the data that is out there. Our funders really want impact, and we think of that as impact per dollar. So, anyway, the context of this work was illegal mining which can cause conflict in Eastern Demographic Republic of Congo, it includes poor environmental outcomes, and social challenges. As data scientists we partner with other organisations to ensure we know how to get value out of data insights.
To understand illegal mining we have to know where it is taking place. So we did work on machine learning from images. We worked with Global Forest Watch and IPIS.
Now, not all of our projects are successful… Usually projects fails because of issues in:
- Problem statement – a well thought through problem statement is really important.
- Data Scientists
- Subject Matter Expertise
- Social Actors
Now, I spoke to someone last night who has run lots of Kaggle projects – crowdfunded data science challenges. Now in those projects you have data, data scientists but you don’t have subject matter experts – and that’s crucisl knowledge and skills to have on board. For instance when looking at malaria, there was a presumption that mosquito nets would be helpful, but the way they work looks like a shrine, like death… And they don’t want to sleep in them. So they used them as fiishing nets.
When we work with an organisation we do want a data set, but we also want an organisation open to seeing what the data reveals, not trying to push a particular agenda. And we have subject matter experts that add crucial context and understanding of the data, of any risks or concerns with the data as well.
We start with, e.g.:
We want to create image classification models
Using publicly available earth satellite imagery
So that those owrking in the transparancy sector can be made aware of irregular mining activity
So that they can improve environmental and conflict issues due to mining.
Some of the data we use is open – and a lot of data I’ve work with is open – but also closed data, data generated by mission-driven organisational apps, etc.
And the data scientists on these projects are at the top of their game, who these organisations could not afford to work with or recruit earlier.
So, for this project we used a random forest analyser on the data, to find mine locations. We had had generated training data for this project which determined that we can pick out where illegal mining work has occured with good accuracy.
To find out more and get involved – and I’d encourage you to do that – go to: dataking.org/getinvolved
Q1) Where do you see DataKind going?
A1) We do a lot with not a lot of money. I had assumed that DataKind was 100 people when I joined, it was less than 10. I would love to see this model replicated in other countries. And conferences… Bringing volunteer data scientists together with providers enables us to increase the opportunity for these things to happen. Bringing these people together, those conferences are rich experiences that amplify the impact of what we are doing.
Q2) For the mining project you can access the data online. The US Federal Government is hosting the data, and we used Google Earth engine in this work.
From Analytics To AI: Where Next For Government Use Of Data? – Eddie Copeland, Director of Government Innovation, Nesta
I’ve been talking to anyone who will listen over the last 5 years about the benefits of public sector data. We have been huge proponents of using open data, but often data has been released in a vague hope that someone else might do something with it. And we have the smart cities agenda, generating even more data that often we have no idea how to use. But there is a missing link there… The idea that public organisations should be the main consumer of their own data, for improving their own practice.
Now you’ll have read all those articles asking if data is the new “oil”, the new “fuel”, the new “soil”! I don’t much care about the analogy but the key thing is that data is valuable. Data enables the public sector to work better, it enables many of the tried and tested ways of working better. Doing more and better with less. But that’s hard to do. For a public sector organisation with lots of amazing data on opportunities and challenges in my area, but not the next door area, how can I understand that bigger picture. We can target resources to the most vulnerable areas, but we need data to tell us where those are. Without visibility across different organisations/parts of the public sector (e.g. in family and child services), how can that data be used to understand appropriate support and intervention?
Why do we focus on data issues? Well, there is a technology challenge as so many public sector organisations have different IT services. And you have outrageous private sector organisations who charge the public sector to access their own data – they should be named and shamed. Even when you get the data out the format can be inconsistent, it’s hard to use. Then there is what we can do with the data – we often urge on the side of caution, not what is useful. Historically the main data person in public sector organisations was the “data protection officer” – the clue is in the title! It takes an organisational leap to collaborate on issues where that makes sense.
I used to work for a think tank and I got bored of that, I really wanted to be part of a “do tank”, to actually put things into action. And I found this great organisation called Nesta and we have set up the London Office of Data Analytics:
- an impactful problem – it takes time, backing, support you have to have a problem that matters
- a clearly defined intervention – what would you do differently if you had all the information you could want about the problem you want to solve (data science is not the innovation)
- what is the information asset you would need to undertake that intervention?
- what intervention do you need to undertake to solve that issue?
So when we looked at London the issue that seemed to fit these criteria was unlicensed Houses of Multiple Occupancy, and how we might predict that. We asked housing officers how they identified these properties, we looked at what was already known, we looked at available information around those indicators. And then developing machine learning to predict those unlicensed HMOs – we are now on the third version of that.
We have also worked on a North East Data Pilot to join up data across the region to better understand alcohol harms. But we didn’t know what intervention might be used, which has made this harder to generate value from.
And we are now working on the Essex Centre for Data Analytics, looking at the issue of modern slavery.
Having now worked through many of these examples, we’ve found that data is the gateway drug to better collaboration between organisations. Just getting all the different players in the room, talking about the same problem in the same way, is hugely valuable. And we see collaborations being set up across the place.
So, things we have learned:
- Public sector leaders need to create the space and culture for data to make a difference – there is no excuse for not analysing the data, and you’ll have staff who know that data and just need the excuse to focus and work on this.
- Local authorities need to be able to link their own data – place based and person based data.
- We need consistent legal advice across the public sector. Right now lots of organisations are all separately getting advice on GDPR when they face common issues…
So, what’s next? Nesta is an innovation organisation. There is excitement about technologies of all types. For this audience AI probably is overhyped but nonetheless that has big potential, particularly algorithmic decision making out in the field. Policy makers talk about evidence based decision making, but AI can enable us to take that out into the field. Of course algorithms could do great things, but we also have examples that are bad… Companies hiring based on credit records is not ok. Public sector bodies not understanding algorithmic bias is not ok. For my own part I published 10 principles for a code of conduct for public sector organisations to use data centres – I’d love your feedback at bit.ly/NestaCode.
It is not OK to use AI to informa a decision if the person using it could not reasonable understand its basic objectives, function and limitations. We would face a total collapse of trust that could set us back a decade. And we’ve seen over the last week what that could mean.
Q1) Aren’t the problems you are talking about are surely people problems?
A1) Public organisations are being asked to do more with less, and that makes it difficult for that time to be carved out to focus on these challenges, that’s part of why you need buy in and commitment at senior level. There is a real challenge here about finding the right people… The front line workers have so much knowledge but you have organisations who
Q2) Your comment that you have to understand the AI, GDPR require a right to explanation to use of data and that’s very hard to do unless automated.
A2) Yes, that’s a really untested part of GDPR. If local authorities buy in data they have to understand where that data is from, what data is being used and what that means. In the HMO example local front line staff can look at those flags from the prediction and add their own knowledge of the context of, for instance, a local landlord’s prior record. But that understanding of how to use and action that data is key.
Data Driven Business. It’s Not That Hard.- Alex Depledge, Founder Resi.co.uk,, Former CEO Hassle.com
That’s a deliberately provocative title – I knew that this would be a room full of intellectuals and I’m going to bring back down to earth. I’m known for setting up hassle.com, and I think it’s fitting that I am following Eddie talking about the basics and the importance of getting the basics right. So many companies that say they are running a data driven business, and they are not… Few are actually doing this.
I started my professional life at Accenture. I met my co-founder there. About 7 years into our friendship she emailed me and said “I’ve got it. I need a piano teacher, I’ve been Googling for four hours, we need a place to find music teachers”. And I said “that’s a rubbish idea”. And then I needed a wysteria trimmed… And we decided we wanted to build a marketplace for local services… We had a whole idea, a powerpoint deck, and thought that great, we’ll get a team in India or Singapore to build it… Sounded great, but nothing happened.
And then Jules quit her well paid job and she said “it’s ok, I’ve brought a book!” – and it was a Ruby on Rails book… She started coding… And she built a thing. And that led to us going through a Springboard process… We had some data but I was trying to pull in money. We were attracting some customers, but not a lot of service providers… We were driven by intuition or single conversations… So one day I said that I’m quitting and going back to the day job… And I was frustrated… And a collague said “maybe we should look at what the data says?”… And so they looked. And they found that 1 in 4 people coming to the website wants a cleaner. And we were like “holy shit!”. Because we didn’t have any cleaners. So we threw away what we had, we set up a three page site. We went all in so you could put a postcode in, find a cleaner, and book them. We got 27 bookings, then double that… And we raised some funding – £250k just when we desperately needed it. We found cleaners, we scaled up, we got much bigger investment. And we scaled up to 100 people.
Then we really turned into a data driven business, building what people want, try it, check the data, iterate. Our VC at Axel pushed us to use mobile… We weren’t convinced. We checked the data that actually people booked cleaners from their desk at lunchtime. At our pinnacle we moved 10k cleaners around London at one point. We had to look at liquidity and we needed cleaners to have an average of 30 hours of work per week… too few and cleaners weren’t happy, too high and jobs weren’t taken up. So at 31 hours we’d start recruiting.
From there we looked at expansion and what kind of characteristics were needed. We needed cities like a donut – clients in the middle, cleaners at the outside. We grew but then we got some unwanted attention and chose to sell. For £32 million. And the company that brought us had 80 engineers.. And they migrated 16 countries onto our platform which had been built by 8 engineers.
So, we sold our business…. And I thought I’m not going to do that again…
And then I wanted a new kitchen… So I had an architect in… spent £@500… 45 days later I got plans… and 75 days later I had an illustration of how it would look so I could make a decision. And so I started Resi, the first online architect. And it took me just 4 months to be convinced that this could be a business. We set up a page of what we thought we might do. I spent £10 per day on Facebook A/B testing ads. And we’ve had a huge amount of business…. We wanted to find the sweet spot for achitects and how long the work would take. Again we needed to know how much time was needed for each customer. So 3 hours is our sweet spot. Our business is now turning over £1 million a year after one year. And only one person works with data, he also does marketing. He looked at our customers and when they convert and how our activities overlaid. After 10 days we weren’t following up, and adding some intervention (email/text etc.) tripled our conversions.
We’ve also been able to look at hotspots across the UK, and we can target our marketing in those areas, and also understand that word of mouth… We can take advantage of that.
I’m a total data convert. I still don’t like spreadsheets. Data informs our decisions – not quite every decision as instinct matters too. But every piece of data analysis we did was doable in a spreadsheet by someone in high school… It doesn’t take machine learning, or AI, or big data. Even simple analysis can create tremendous results.
Q1) What next?
A1) I always said I didn’t want to dine out on one story… Like Hassle. But I don’t know the end for Resi yet… Invite me back in a few years!n
Q1) The learning for a few hours of work was huge.
A1) Our entire business was based on a single piece of analysis – what were our customers looking for led to £32m.
The AI Race: Who’s Going To Win? – Vicky Brock (VB – chairing), CEO, Get Market Fit; Alex Depledge (AD), Founder Resi.co.uk, Former CEO Hassle.com; Joel KO (JK), Founding CEO, Marvelstone Ventures; Chris Neumann (CN), Early Stage Investor
CN: I’m a recovering entrepreneur. As an investor I’ve had a global purview on what’s going on in the AI race. And I think it’s interesting that we see countries and areas which haven’t always been at the cutting edge of technology, really finding the opportunities here. Including Edinburgh.
JK: We are funders based in Singapore and investing in FinTech. The AI technology has been arising… I’m hoping to invest in AI start ups and incubators.
AD: You already know who I am. In my brief hiatus between companies I was an entrepreneur in residence in Index Ventures, and I saw about 300 companies come in saying they were doing AI or Machine Learning so I have some knowledge here. But also knowing a leading professor in data ethics I don’t care who wins, but I care that Pandora isn’t let out of her box until governments have a handle on this because the risks are great.
VB: I’m a serial entrepreneur around data. And machine learning or AI can kind of be the magic words for getting investment. There is obvious hype here… Is it a disruptor?
CN: I’ve seen a lot of companies – like Alex – say they use ML or AI… In some ways its the natural progression from being data driven. I do think there will be an incredible impact on society over the next 10 years from AI. But I don’t think it will be the robots and tech from science fiction, it will probably be in more everyday ways.
VB: Is AI the key word to get funding…
JK: I see many AI start ups… But often actually it’s a FinTech start up… But they present themselves that way as funders like to hear that… There is so much data… And AI does now spread into data lives… Entrepreneurs see AI as a way to sell themselves to investors.
VB: At one stage it was “big data” then “AI” but you’ve had some little data… What did you see when you were entrepreneur in residence?
AD: No disrespect to investors but they focus on financials and data, but actually I’d often be asking about what was happening under the bonnet… So if they were were using machine learning, ask about that, ask about data sets, ask where it’s coming from… But often they do interesting data work but it’s a good algorithm or calculation… It’s not ML or AI. And that’s ok – that’s something I wanted to bring out in my presentation.
VB: What’s looking exciting now?
CN: We see really interesting organisations starting to do fascinating work with AI and ML. I focus on business to business work, but that often looks less exciting to others. So I am excited about an investment I’ve made in a company using BlockChain to prove GDPR compliance. I spoke with a cool company here using wearables and AI for preventing heart attacks, which is really amazing.
JK: I have been here almost a week, met start ups, and they were really really practical. They have the sense to make a revenue stream from the technology. And these very new start ups have been very interesting to me personally.
VB: You’ve started your next company, did you cross lots of ideas off first…
AD: Jules and I had a list of things we wouldn’t do… Chris talked about B2B… We talked about not doing large scale or consumer ideas. We whittled our list of 35 ideas down to 4 each and they were all B2B… But they bored us. We liked solving problems we’ve experienced. My third business I hope will be B2B as getting to £10m is a bit more straightforward than in B2C.
VB: AI requires particular skillsets… How should we be thinking about our skillsets and our talents.
CN: Eddie talked earlier about needing to know what the point in. It can be easy to get lost in the data, to geek out… And lose that focus. So Alex just asking that question, finding out who gives a damn, that’s really important. You have to do something worthwhile to somebody, there’s no point doing it .
JK: With AI… In ten years… Won’t be coding. AI can code itself. So my solution is that you should let your kids play outside. In Asia lots of parents send kids to coding schools… They won’t need to be engineers… Parents’ response to the trend is too early and not thought through…
AD: I totally agree. Free play and imagination and problem solving is crucial. There aren’t enough women in STEM. But you can over focus on STEM. It’s data and digital literacy from any angle, it could be UX, marketing, product management, or coding… In London we hav ethis idea that everyone should be coding, but actually digital literacy is the skills we need to close. And actually that comes down to basic literacy and numeracy. It’s back to basics to me.
VB: I’d like to make a shout out for arts and social sciences graduates. We learn to ask good questions…
AD: Looking at recent work on where innovation comes from, it comes from the intersectionality of disciplines. That’s when super exciting stuff happens…
Q1) Mainly for Alex… I’m machine learning daft… And I love statistics. And I know the value of small scale statistics. And the value of machine learning and large scale data – not so much AI. How do you convey that to business people?
AD) We don’t have a stand out success in the UK. But with big corporates I tell them to start small.. Giving engineers space to play, to see what is interesting… That can yield some really interesting results. You can’t really show people stuff, you need to just try things.
VB) Are you trying to motivate people to use data in your company?
JK) Yes, with investors you see patterns… I tell kids to start start ups as early as possible… So they can fail earlier… Because failures then lead to successful businesses next time.
CN) A lot of folk won’t be aware that for many organisations there is a revenue stream around innovation… It’s a really difficult thing to try to bring in innovative practices into big organisations, or collaborate with them, without squishing that. There are VCs and multinationals who will charge you a lot of money to behave like a start up… But you can just start small and do it!
The Revolutionary World Of Data Science – Passing On That Tacit Knowledge! – Shakeel Khan, Data Science Capability Building Manager, HM Revenue & Customs
I’ve been quite fortunate in my role in that I’ve spend quite a lot of time working with both developed and developing economies around data science. There is huge enthusiasm across the world from governments. But there is also a huge fear factor around rogue players, and concerns about the singularity – machines exceeding humans’ capabilities. But there are genuine opportunities there.
I’ve been doing work in Pakistan, for DFID, where they have a huge problem with Dengy Fever. They have tracked the spread with mobile phone data, enabling them to contain it at source. That is saving lives. That’s a tremendous outcome. Closer to home, John Bell at Cambridge University has described AI as the saviour of our health services, as AI can enable us to run our services more effectively and more economically.
In my day job at HMRC, you can’t underestimate what the work that we do enables in terms of investment in the country and its services.
I want to talk about AI at three stages: Identify; Adopt; Innovate.
In terms of data science and what is being done around the world… The United Arab Emirates have set up their Ministry of AI and a 2031 Articificial Intelligebce Strategy. We have the Alan Turing Institute looking at specific problems but across many areas, some really interesting work there. In Edinburgh we have the amazing Data Lab, and the research that they are doing for instance with cancer, and we have the University of Edinburgh Bayes Centre. Lots going on in the developed world. But what about the developing world? I’ve just come back from Rwanda, who had a new Data Revolution Policy. I watched a TED talk a few weeks back that emphasised that what is not needed in sub0-saharan Africa is help, what they need is the tools and means to do things themself.
Rwanda is a hugely progressive country. They have more women in parliament (62.8%) than any country in the world. Their GDP is $8.3bn. They have a Data Revolution Policy. They are at the start of their journey. But they are trying to bring tacit knowledge in, to leapfrog development… Recognising the benefit of that tacit knowledge and of those face to face engagements.
For my role I am split about 50/50 between international development and work for HMRC. So I’ll say a bit more about the journey for developed economies…
Defining Data Science can be quite abstract. You have to make a benefits case, to support the vision, to share a framework and some idea of timeline, with quick wins, to build teams, to build networks. Having a framework allows organisations to build capabilities in a manageable way…
A new Data Science Centre going up in Kigali, Rwanda, will house 200 data scientsists – thats a huge commitment.
The data science strategic framework is about data; people skills; cultural understanding and acceptance – with senior buy in crucial for that… And identifying is also about data ethics, skills development – we have been developing frameworks for years that we can now share. For Rwanda we think we can reduce the time to develop data capabilities from maybe 5 years to perhaps 3. Similarly in Pakistan.
When you move to the adopt phase… You really need to see migrationa cross sectors. I started my career in finance. When I came to HMRC I did a review of machine learning and how that was being used, how that machine learning was generating benefit. We managed to bring in £29 bn that would otherwise be lost, partly through machine learning. One machine learning model can, effetively, bring in tens or hundreds of millions of pounds so they have to be well calibrated and tested. So, I developed the HMRC Predictive Analytics Handbook (from June 2014), which we’ve shared across HMRC but also DWP, across collaeagues in government.
In terms of Innovate, it is about understanding the field and latest developments. However HMRC are risk averse, so we want to see where innovation has worked elsewhere. So I did some work with Prof David Hand at Imperial College London about 20 years ago, and I got back in touch, and we developed a programme of data science learning. Not about Imperial providing training, it was a partnership between HMRC and Imperial. We looked closely at the curriculum and demonstrate value added, and look at how we could innovate what we do.
University of Edinburgh Informatics is a really interesting one. I read a document a few years ago by the late Prof. Jon Oberlander about the way that the academic and public and private sectors working together could really benefit the Scottish economy. Two years of work led to a programme in natural language processing that was the result of close collaboration in HMRC. Jon Oberlander was hugely influential, and passionate about conversational technology and the scourge of isolation. And was able to ask lots of questions about AI, and when that will be truly conversational. I hope to continue that work with Bayes, but also wanted to say thank you to Jon for that.
AI is increasingly touching our lives. Wherever we are in the world, sharing our tacit knowledge will be incredibly important.
Q1) Rwanda has clearly made a deep impression. What were the most suprising things?
A1) People have stereotypes about sub saharan Africa that just aren’t true. For instance when you get off the plane you cannot take plastic bags in – they are an incredibly environmental country. I saw no litter anyway in the country. The people of Rwanda are truly committed to improving the lives of people.
Q2) Do you use the same machine learning methods for low income and high income tax payers/avoiders?
A2) There are some basic machine learning methods that are consistent, but we are also looking at more novel models like boosted trees.
Q3) I worked in Malawi and absolutely back up your comment about the importance of visiting. You talked about knowledge from yourself to Rwanda, how was the knowledge exchange the other way?
A3) Great question. It wasn’t learning all from developed to developing. We learnt a great deal from our trip. That includes cultural aspects. I terms of the foundations of data science, we in the UK have used machine learning in financial services and retail for 30 – 40 years, that isn’t really achievable in these countries at the moment and there it is learning going from developed to developing.
Closing comments – Maggie Philbin
I’ve been reflecting on the (less serious) ways data might influence my life. My son in law is in a band (White Lies) and that has given me such an insight into how the music industry use data – the gender and age of people who access your music, whether they will go to gigs etc. And in fact I was very briefly in a band myself during my Swap Shop days… We made a mock up Top of the Pops… Kids started writing in… And then BBC records decided to put it out… We had long negotiations about contracts… But I was sure no-one would buy it… It reached number 15… So we went from parodying Top of the Pops to being on Top of the Pops. And thank you to Scotland – we made number 9 here! But I hadn’t negotiated hard – we just got 0.5%. And if we’d had that data understanding that White Lies have, who knows where we would have been.
So, day one has been great. Thank you to The Data Lab, and to all the sponsors. And now we adjourn for drinks.