What is the future of AI in Europe and what does it take to build an AI solution that is attractive to investors and customers at the same time? How do we reimagine the battle of “AI vs Human Creativity” in Europe? 

Is there any company that is not using AI or isn’t AI-enabled in some way? Whether it is startups or corporates, it is no news that AI is boosting digital transformation across industries at a global level and hence it has traction not only from investors but is also the focus of government initiatives across countries. But where does Europe stand with the US and China in terms of digitization and how collective effort could push AI as an important pan-European strategic topic? 

First things first: According to McKinsey, the potential of Europe to deliver on AI and catch up against the most AI-ready countries such as the United States and emerging leaders like China is large. If Europe on average develops and diffuses AI according to its current assets and digital position relative to the world, it could add some €2.7 trillion, or 20 per cent, to its combined economic output by 2030. If Europe was to catch up with the US AI frontier, a total of €3.6 trillion could be added to collective GDP in this period.

What comprises the AI landscape and is it too crowded?

I recently attended a dedicated panel on “AI vs Human Creativity ” as a part of the first day of the Noah conference 2019 in Berlin.  Moderated by Pamela Spence, Partner, Global Life Sciences Industry leader, EY, the discussion started with an open question on whether the AI landscape is too crowded? According to a report by EY, there are currently 14000 startups globally which can be associated with the AI landscape. But what does this mean when it comes to the nature of these startups? 

 Minoo Zarbafi, VP Bertelsmann Investments Digital Partnerships, added perspective to these numbers,” There are companies that are AI-enabled and then there are so-called AI-first companies. I differentiate because there are almost no companies today that are not using AI in their processes. From an investor perspective, we at Bertelsmann like AI-first companies which are offering a B2B platform solution to an unsolved problem . For instance, we invested in China in two pioneer companies in the domain of computer vision that are offering a B2B solution for autonomous driving.” Minoo added that from a partnership perspective Bertelsmann looks at AI companies that can help on the digital transformation journey of the company. “The challenge is to find the right partner with the right approach for our use cases. And we actively seek the support of European and particularly German companies from the startup ecosystem when selecting our partners”, she pointed out. 

The McKinsey report too states that one positive point to note is that Europe may not need to compete head to head but rather in areas where it has an edge (such as in business-to-business [B2B] and advanced robotics) and continue to scale up one of the world’s largest bases of technology developers into a more connected Europe-wide web of AI-based innovation hubs.

Growing share of funding from Series A and beyond reflect increased maturity of the AI ecosystem in Europe. Pamela Spence from EY noted, “One in 12 startups uses AI as a part of their product or services, up from 50 about six years ago. Startups labelled as being in AI attract up to 50 per cent more funding than other technology firms. 40 per cent of European startups that are claimed as AI companies actually don’t use AI in a way that is material to their business.”

AI and human creativity go hand-in-hand

Another interesting and important question is how far are we from the paradigm of clever thinking machines? Why should we be afraid of machines?  Hans-Christian Boos, CEO & Founder, Arago compares how machines were earlier supposed to do tasks which are too tedious or expensive and complex for humans. “The principle of machine changes with AI. It used to earlier just automate tasks or standardise them. Now, all you need is to describe what you want as an outcome and the machine will find that outcome for you- that is a different ballgame altogether. Everything is result-oriented,” he says.

Minoo Zarbafi adds that as human beings, we have a limited capacity for processing information. “With the help of AI, you can now digest much more information which may cause you to find innovative solutions that you could not see before. One could say, the more complexity, the better the execution with AI. At Bertelsmann, our organisation is decentralised and it will be interesting to see how AI leverages operational execution.”  

AI and the Political Landscape

Why discuss AI when we talk about the digital revolution in Europe? According to the tech.eu report titled ‘Seed the Future:  A Deep Dive into European Early-Stage Tech Startup Activity’, it would be safe to say that Artificial Intelligence, Machine Learning and Blockchain lead the way in Europe. The European Commission has identified Artificial Intelligence as an area of strategic importance for the digital economy, citing it’s cross-cutting applications to robotics, cognitive systems and big data analytics. In an effort to support this, the Commission’s Horizon 2020 funding includes considerable funding AI, allocating €700M EU funding specifically.

Chiara Sommer, Investment Director, Intel Capital reflected on this by saying, “In the present scenario, the implementation of AI starts with workforce automation with a focus on how companies could reduce cost and become more efficient. The second generation of AI companies focuses on how products can offer solutions and solve problems like never before. There are entire departments can be replaced by AI. Having said that, the IT industry adopts AI fastest, and then you have industries like healthcare, retail, a financial sector that follow.” 

Why are some companies absorbing AI technologies while most others are not? Among the factors that stand out are their existing digital tools and capabilities and whether their workforce has the right skills to interact with AI and machines. Only 23 percent of European firms report that AI diffusion is independent of both previous digital technologies and the capabilities required to operate with those digital technologies; 64 percent report that AI adoption must be tied to digital capabilities, and 58 percent to digital tools. McKinsey reports that the two biggest barriers to AI adoption in European companies are linked to having the right workforce in place.

It is certainly a collective effort of industries, the government, policy makers, corporates to have effective and impactful use of AI. Instead of asking how AI will change society Hans-Christian Boos rightly concludes, “We should change the society to change AI.”

Note: The quotes used in this article are derived from a panel discussion at NOAH Conference Berlin 2019.

The success of data-driven projects has quite a few challenges and barriers. Here is a look at how you could overcome them by simply asking yourself three questions.

Data has become probably the most valuable asset that companies could have nowadays. It can give you insights into your customers’ behaviour and your business operations, drive sales and optimize delivery chains, predict product and service needs. The most exciting and promising technologies such as AI, the Internet of Things or blockchain are possible exactly thanks to the abundance of data. But does this mean that the availability of data is a guarantee for the success of a data-driven project?

Unfortunately, some of the recent studies suggest that even with all that data floating around, the majority of projects still fail.  In 2017, Gartner said that 60% of big data projects fail, but in his recent tweet, the Gartner-Analyst Nick Heudecker corrected this number, saying that back then Gartner was “too conservative” about it and the closing rate is around 85%. Another study from McKinsey revealed that companies have captured only about 10% – 40% of the value that is available in their data. Why is that?

There are many barriers and challenges to successful data-driven projects. There is a need for a change in the organisation’s culture, unrealistic goals and expectations, lack of skilled professionals. But since we are talking about data, let us focus on the issues that surround particularly this matter. There are three critical questions that data scientists and data experts need to answer immediately after a viable proof of concept for a project has been identified.

“Do I have the right data?”

It is easy to assume that your company has enough data to embark on the project right away. After all, companies have been sitting on data for years, and now it is time to finally make sense of it. The problem that is often overlooked, though, is that this data might give you specific insights into past operations, failures and successes. However, if you train your algorithms with this historical data, they will merely find patterns that could have been applied to the scenarios in the past but are only sub-optimal or even completely irrelevant for the future.

You need fresh data, preferably, real-time or near real-time. Or maybe you have identified an entirely new problem that you want to address with your project, for which there is no consistent dataset available yet. So, before you actually start a project, you need to identify the datasets you will need and how you can get them. Which brings us to the next question.

“Do I know where this data is?”

There is not a company that doesn’t have database/s. Many companies have additionally built data warehouses or maybe even started using data lakes. Surely, with these rich data pools, the data that you need is just there, at an arm’s length. Or is it really?

As the recent Gartner paper “How to Avoid Data Lake Failures”, data lakes, for one, “are rarely started with a definite goal in mind, but rather with nebulous aspirations to ‚create a single version of the truth’ or to ‚democratize our data‘.” This can hardly be called a strategic goal. As Databricks’ CEO Ali Ghodsi eloquently put it in one interview: “Many of these companies have built these data lakes and stored a lot of data in them. But if you ask the companies how successful are you doing predictions on the data lake, you’re going to find lots and lots of struggle they’re having.”

Besides, according to the same Gartner paper, the assumption that data lakes will be “the one destination for all the data in their enterprise” can be misleading because there are simply too many data sources.

Data warehouses and databases are no better in this sense. Most of the times, they are created to address quite a concrete issue, so the data they store may or, most likely, may not be applicable to this specific AI or IoT project you are about to start.

It might be, therefore, necessary to think more broadly and look for data residing outside the data warehouse or consider combining several databases. Maybe you even need to get data from third-party sources or from the company’s partner network. For example, in order to build a profile of a taxpayer’s total income to find irregularities and establish illegal activities, HMRC’s software system connect links data from multiple government and corporate sources.

And that leads us to the next question.

“How can I get all this data together?”

No matter which research or study on why data-related projects fail you refer to, almost each of them is citing siloed data as one of the culprits. This is not surprising because big data comes in many shapes and from various sources – enterprise software applications, users’ mobile phones, IoT sensors, partners’ systems, social media streams…, the list is practically endless. Aggregating all these data sources in order to start reconciling the data and getting meaningful insights from it can be incredibly difficult.

The problem necessarily does not lie in the lack of technology. There are numerous tools and software systems available on the market that can simplify and speed up data integration between the cloud and on-premise, in batches and in real-time, between data warehouses, software applications and IoT platforms – you name it. It is often the lack of understanding the critical role of data integration that poses the biggest challenge. After all, it is much easier to ensure funding for an AI pilot project – because AI is fancy and cool –, than securing budget to properly address the question of how to integrate data and application more efficiently.

This is changing, albeit slowly. The recent report by Corinium Digital “The State of Data & Analytics in Europe” states that, while AI / machine learning and predictive analytics continue to secure the largest investments, they are closely followed by data integration with 78% of respondents saying that they are planning to invest £1-2 Million or more into it within the next months.

And yet, however promising this sounds, these results rely on interviews with only 130 data and analytics practitioners in Europe. Many more organisations still fail to realize that, in order to get the full potential out of data and analytics, they need to ensure a solid foundation, which means making sure that they work with the right data, from every relevant source.

Igor Drobiazko will be speaking at Data Natives 2018– the data-driven conference of the future, hosted in Dataconomy’s hometown of Berlin. On the 22nd & 23rd November, 110 speakers and 1,600 attendees will come together to explore the tech of tomorrow. As well as two days of inspiring talks, Data Natives will also bring informative workshops, satellite events, art installations and food to our data-driven community, promising an immersive experience in the tech of tomorrow.

We caught up with Jason Moss (Co-founder of Metis) and John Harnisher (VP of Data, Analytics and Insight at Kaplan) to discuss the new Data Science course at Metis. Metis offers comprehensive courses in digital practices, designed to teach the skills needed to succeed in an increasingly digital world. The new Data Science course is a bootcamp that runs in-person for 12 weeks, Monday through Friday, from 9 am – 6 pm. We find out more details below.

Can you tell me a little bit about the motivations behind launching Metis and the Data Science course?

As we’ve been thinking about how we want to grow as a company and accelerate our digital footprint, we started looking into bootcamps. The reason for this was simple: they represent the perfect mix of great teachers and great curriculum with short intensive learning. We realised that this recipe works well and genuinely transforms people’s futures. At its core, this is Kaplan’s philosophy and bootcamps were really a way for people to move into high demand job areas.

With various reports like McKinsey’s suggesting that nearly 200,000 data science jobs will be unfilled because of the lack of skills, launching a course in this field was inevitable. The idea of a data science course really fit with what Metis was trying to do in terms of new economy skill training and bringing together great instructors with an intensive learning format to accelerate or change people’s careers.

How do you see the course matching the demand for Data Scientists?

The course we have created focuses on five practice-based projects, where each project goes through the same cycle of asking the most crucial questions and equipping the students with the skills to not only answer these questions but also execute the solution. The whole course is centred on this because we believe that it is important for candidates to show their prospective employers real-life projects in their portfolio, and to showcase a whole range of skills.

The second way we are filling this gap is by really focusing on communication. Of course, when we screen our applicants, we are looking for a certain level of programming, modelling and statistics skills. Crucially, however, we are also trying to assess whether the applicant communicates well. Why do we ask this, you may be wondering?

Well, at its core, Data Science is fundamentally about communication and we want to make sure that students have solid communication skills coming into the course. Once accepted, we ensure that there is a heavy emphasis on communication and visualisation because this is so pertinent to Data Science today.

There are a lot of other courses out there focusing on this. What is it about Metis that makes it different?

You can categorise these data sciences courses into three categories: MOOC’s, Masters programmes, and boot-camps. They all have their own value proposition.

With boot-camps, they are designed for people to have a very efficient path, develop a network, and opportunities to meet employers who are interested in hiring entry level data scientists. This is very different from a Masters programme, which requires a serious commitment of two years and considerable cash, and it is also different from MOOC’s, which cost but are usually free form.

Now, within bootcamps, there are a couple of data science courses but I think the way we have designed our course is unlike anything on the market. The ability to partner with a world-class practitioner in the data science space – Datascope Analytics – is also a huge advantage because they have real content expertise and have created a robust course that will give our graduates the skills they need for the roles they’re applying for.

Put Kaplan’s market leadership into the mix, and what you have is a programme that is backed by incredible experience, a forward thinking vision and a world-renowned syllabus.

How would you answer the sceptic who says “you cannot learn Data Science in 12 weeks”?

There are a couple of things here that we need to bear in mind. Firstly, we do not intend to recreate all of the knowledge someone would get over 5 or 10 years studying at a university. What we aim to do is teach a set of skills and get experience in these 12 weeks to make the person a great junior data scientist.

Also, it’s important to remember that this course is not designed for someone who has done a tiny bit of statistics, who knows a little bit about excel macros and programming. Rather, it’s a course people who have strong skills in a particular field – say analytics – but need to polish his or her programming skills, for example. We cannot take a total beginner and make them an entry-level data scientist, and we do not claim to do this.

What are they key things you look for in applicants?

Programming, stats and communication are the three core pieces we are looking for upfront. The application is designed to understand whether the applicant has these basics. After this, we look for a separate set of character traits that we believe is essential for a successful data scientist – curiosity, grit and creativity.

If someone is not inherently curious, they will struggle with the most fundamental and essential questions. Grit is all about the willingness to keep pushing because the questions data scientists deal with do not have easy answers. Creativity is simply the ability to think out of the box and use techniques that you may not be familiar with.

Our application is designed to make sure we get people who have the right skills and the right character traits.

A sidenote: we are currently accepting applications for our next bootcamp, which starts on January 12, 2015 in New York City. The early application deadline is November 17.

Do you have any plans to expand into Europe?

Absolutely. With a company that is as big as Kaplan, this idea is not designed to stay solely in New York. Of course, we need to prove that this models works before we start expanding overseas, but ultimately we believe that this course has a global audience and will only increase in popularity.

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