Data Science Investment Here's a tally of just how much has been invested in data science this year.
£ 54,700 Million

BGF Ventures’ Simon Calver Talks Data Science and AI


SHACK15 News had a chat with BGF Ventures’ partner Simon Calver (pictured) on London’s data science and AI environment.

BGF Ventures is a London-based investment fund, and Britain’s largest fund focused on UK companies only.

It can rely on a £2.5 billion balance sheet, which allows it to devote most of its time into investing in companies—early stage startup get £1-6 million in funding— and supporting them, rather than spending energies and resources raising new funds.

The fund has led several investment rounds in data science and AI companies— the latest, just last week, was in in AI-fuelled research platform Streetbees. But what is BGF Venture’s general take on London’s data science and AI startup environment?
SHACK15 News had a chat with BGF Ventures’ partner Simon Calver — erstwhile boss of LoveFilm and Mothercare— to discover.

Hello Simon, great to meet you. To start, how would you describe the state of data science and AI in London and the UK?
Well, in these days, there isn’t a single meeting with companies or startups when at some stage they don’t mention AI and machine learning. For established companies especially, these technologies have moved from the back of the business right into the product. What has been changing with AI and machine learning is that now everybody wants to use them to improve the algorithms by which they run their business and deal with their customers. And I think that over time saying you use machine learning or AI will be equivalent to some people saying they use html for their website.

What kinds of data science companies are you seeing right now?

You see, it’s very hard to think about data science and AI as a single sector, because it all depends how those tools are applied.
At the moment, for instance, we are first of all seeing  a number of companies that build AI products other companies can use—products that are based on specific problems or verticals, like for instance the way retailer look at customers. Think of Ravelin, which analyses customer order data to detect fraud.
Then you have companies creating tools aimed at replacing human decision-making, by means of expert systems able to carry out boring human tasks a lot more quickly. You get, for example, some AI tools that can read reams of legal papers and understand what the differences between two sets of legal documents are.
You also see companies that are creating rule-based systems, or personal assistants, which help driving efficiency in a company, even if they are not making decisions.
Another type you get is what we could call “deep tech AI”: companies working on robotics, autonomous vehicles, “humanoid” systems, which could lead to very interesting R&D developments. Things like DeepMind, which are pushing the boundaries of AI.
Finally, you have the so-called “AI-as-a-service” companies.

As an investor, how do you recognise a good data science startup?
What you have to try and do is separate the hype of it from the reality of what is a very disruptive technology. You have to separate those startups that are consulting-type businesses helping companies to understand data science and AI, versus those startups providing a scalable international product. There are a lot of businesses out there that are evolving but don’t have a minimum viable product yet. When we go to invest we look for startups behind globally scalable products that are low-touch and can actually help solve real problems.
More in general, to list the main boxes a startup has to tick to be considered for an investment, I’d say: it must have a team with a demonstrated track record; it has to be catering to an existing market, in an important, relevant way; and it has to be solving a problem.

What is the biggest risk, today, for a founders who wants to create a data science or AI startup?
One big risk is that it’s going to be an incredibly competitive space. In the last 18 months in particular I have witnessed a massive surge in AI companies: there is probably two or three times the number of companies that were in the space only a couple of years ago. One thing founders have to remember is that just because you can do something that potential customers need , it doesn’t mean you’ll do it better than your competitors. You have to think about how to differentiate yourself in the broader market.

BGF Ventures focuses UK-based companies. Have you noticed any worsening in London’s and the UK’s startup environment following the Brexit vote in June?
We have seen no change after Brexit: we are still seeing a lot of high quality companies. In general, the UK is still a great place to start, grow and scale a business.


Co-working space and blog dedicated to all things data science.

Subscribe to our newsletter