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MOSTLY AI has gathered a team of exquisite experts and data science enthusiasts. We would like you to meet our team 'eye to eye' and get to know all our devoted Mostlies who ensure that we deliver the best product and stand out in the market. Today, we bring into the spotlight Andreas Ponikiewicz, Vice President of Global Sales at MOSTLY AI, who will share more about himself, his role, and his experience with clients who foster innovation with the help of synthetic data.

Can you tell us a bit more about your background and how you ended up working for MOSTLY AI?

Andreas Ponikiewicz, VP of Global Sales at MOSTLY AI

My background is actually in economics and finance. When you finish university, you try to work for big companies and get their logos on your CV, but I had the opportunity right before I finished my studies to join an Austrian startup in the finance industry, and I very quickly realized how great such an environment is. Small companies are more innovative. I liked people's mindsets and how a small company with the potential for growth offers products that the big guys don't have. Every client you win is very big, so you celebrate as a family.

Even though I started as a financial data analyst, I soon became curious about who our clients are and why they buy our products. I wanted to talk with and understand them, so I switched to a product management role in which I developed products based on our clients' feedback. I got to travel across Europe and collect feedback from our new and existing customers and then build, together with my team, a product that sold 30 times more than previously — and this is how I realized that communicating with clients is very important. I also realized that I like to talk to customers instead of sitting at the office. So, I switched to a more sales-oriented role, selling the startup's complete product portfolio and eventually becoming Head of Sales. After some successful client wins, we got acquired by a very big company, which led to my corporate life.

However, when you come from a dynamic startup, the regular corporate life is not as interesting. I came to know that MOSTLY AI was looking for a Vice President of Sales to lead the team and build up the business. I applied and got a call back soon after, and that is how I ended up being at MOSTLY AI.

How do you see your role at MOSTLY AI, and what brings excitement to you at work?

It's the big impact, I think. The business we are in is very innovative and exciting. We see that the big industries are only beginning to understand the potential of synthetic data. When I look at the competition, I see us very well-positioned because when we do POCs and talk to clients, we get very good feedback that we are top-notch here, and this is very motivating. I like the idea that data protection and innovation are not enemies and that there is a smart way to combine both. Also, I believe the mindset shift is coming to the industry, and the companies that can show that they care about the privacy of their clients will have a big advantage compared to those who don't. Apple is one of the first who started this trend, and I believe many will follow.

How would you describe synthetic data to an alien?

'I want to know everything about your behavior, but I don't want to know any personal details about you.'

What do you think about synthetic data? Is it really so beneficial for companies as everyone keeps saying?

Synthetic data is not a new concept. Today's AI and ML (machine learning) technology and processing power are so big that now you have the capability to recreate highly realistic data sets, which was not possible a few years back. That is what brings the traditional anonymization techniques at risk, because first of all, they are not 100% safe, and secondly, they destroy a lot of the data. Now companies are actively researching this area and see a very good opportunity in synthetic data.

What do you think about data privacy nowadays? Is it impossible to preserve it with all the new technologies?

Some people say that privacy nowadays is an illusion. The moment you have a mobile phone, you give up privacy. If you want to have a bank transaction, you do it online, so people know. If you want to buy something on Amazon, you give up privacy. If you search for something on Google, you give up privacy. If you have a LinkedIn account, you give up privacy.

In the end, you can choose between convenience or living the life of Robinson Crusoe. Somewhere in between, you need to decide if you want to have convenience or privacy. When it comes to business, that is a different story. As a company, you can say we truly care about your privacy, what you buy, how much you earn, where you live, and what you like, and we do not share it with anyone else because your data is safe with us. That is a business-to-business value proposition.

Can you tell us about some interesting data leaks you know and how they happened? How could the company have avoided it?

There was one major data leak when someone found out how Amazon had a super-biased algorithm that favored white male candidates, so every woman who applied was just discarded because of the algorithm. This was then leaked somehow, which seriously damaged their reputation. Netflix also had a data leak when researchers identified sensitive information based on subscribers' movie ratings. Many financial institutions also lost data, which is a disaster from a reputational perspective. The more you search, the more leaks you find. It is not something that happens from time to time; it is actually a constant danger. That is why you should choose privacy by design; it is the only way to protect your data.

Where are you originally from, and how much is data privacy regulated there?

I was born in Poland, but I grew up in Austria and went to kindergarten here. I can say that privacy is taken very seriously in Austria. Germany and Austria, especially, are territories that invest a lot in data protection. This sometimes makes it difficult for them to innovate and leverage data. It is one thing to protect your data, but if you don't have a technology, like synthetic data, to unlock the value of data assets, you might not be able to innovate. The problem is that if you want to innovate, you need data, but on the other side, you need to be careful what data you share and what information you disclose. This is why I see the potential of our business in bringing both together — unlocking the data that companies have but in a 100% private way.

Do you have any good documentaries or movies related to AI and big data that you could recommend to our readers?

We had a virtual movie night here at MOSTLY AI just a few days ago, and we watched this documentary called 'Coded Bias,' and that is a pretty interesting film for someone who wants to dive into this AI world a bit and learn about why we have data bias, why fairness in AI matters, and what are its risks and challenges. On the other hand, data bias is an issue that, aside from privacy concerns, can be solved with synthetic data. I would recommend watching this because it can definitely trigger some thinking.

Aside from an obvious interest in AI and machine learning, what do you like to do in your free time?

I listen to tons of music. I like to read a lot about history. I am interested in all of the big developments over the last centuries because I can derive some understanding of what is happening today, and I believe it is all somehow connected. I go to the gym if it is open, but now, during the COVID pandemic, it is a bit difficult to do so, unfortunately. Then, of course, I like to meet friends afterwards. I am a guy who likes to talk to people.

Since we are in the middle of the world's pandemic, how much has your life changed?

Dramatically. Before the pandemic, I was traveling almost every week across Europe because that is the main part of my business role — meeting clients — one week in Switzerland, the next week in London, and so on. Also, I used to meet friends often, and not being able to do that or travel for business has been quite a lot to deal with. So, from time to time, it would be nice to do that again.

Regarding the spread of pandemics, how do you think synthetic data could help solve this issue?

People say it is just a matter of time before the next pandemic pops up because there are so many interactions between humans and nature with the potential that a new virus could spread from animals to humans. I think that, in the case of the city of Vienna, for example, synthetic data could be applied to analyze some patient data, the spread of the virus, and how people move. If this could be anonymized and they could still track the movement of people on a global level without leaking any patients' private information and who went where and when. I believe this could be very helpful when trying to understand the dynamics of the pandemic.

We hope to see that application in the future. What other possible applications of synthetic data are currently in practice?

There are so many applications and use cases for synthetic data already. One can analyze people's behavior anywhere they interact. So, in companies such as those in finance, insurance, telecommunications, retail, health, pharmaceuticals, and all others that depend on interactions between people, if you want to analyze their behavior, synthetic data can be applied. The scope is so broad that I believe it is a good thing we are focusing on certain areas because otherwise, we would be lost in all those potential applications that exist. I believe that in the next few years, many companies will focus on a different niche.

How do you think the regulatory landscape will change in the next year or so?

I think that the regulators will also catch up when it comes to understanding how AI and ML work. Currently, many regulations are still based on an old economy and old-world design, and they will get more sophisticated about AI, how AI should be controlled, what it can be used for, and what requirements it should fulfill to ensure that it is not used against the good of humankind. This is still like a black box to many regulators and companies. It is the wild west. No one is really focused on what impact AI has because we see it being used everywhere. I think the regulatory bodies need to acquire a better understanding of AI and derive regulations that show they really understand the impact of AI because I don't believe they do.

What is the most pressing issue for enterprises on the IT and data front? In your experience, what makes large organizations succeed?

Being innovative. Understanding that every product offering and every service you have has a certain lifecycle. That means a product that is new today might be irrelevant in a few years. The companies that realize that you have to change your offer, that you have to adapt your product, that your customers change, and new clients have new requirements. Companies that understand the cycle of innovation will remain in the market, while the ones that rely too much on what they have will become dinosaurs and disappear sooner or later. The question is if a company has enough data to understand its clients and is able to derive decisions from that and innovate and bring new products and services to market. Those companies are the ones that will remain the leaders in the market.

What would your best advice be for those looking to implement synthetic data in their companies?

Think big! When you face a challenge — for example, not being able to use the data you have because it violates privacy laws — you need to think of what your potentials are and how you can apply this to different areas, such as using it for ML models, to understand the behavior of your clients, to understand what do they buy and why, how do they spend their money, etc. You can derive insights from that and develop products you couldn't even think of before because now you have the insights into data you didn't have before. So, while before you worked with assumptions, now that changes to knowledge, and this is enabling marketing and product management to be customer-centric.

Any final remarks?

I am very much looking forward to the next one or two years because I believe we are at the beginning of a wave, and we see that big institutions but also small companies are now becoming very interested in the potential of synthetic data. I believe that in the foreseeable future, many companies will have a big advantage if they can leverage this potential compared to the ones who still live in the traditional world.

"Those who innovate will remain in the market; the others will become dinosaurs and soon become extinct."

Andreas Ponikiewicz
Vice President of Global Sales at MOSTLY AI

If you would like to know more about synthetic data and innovation, feel free to reach out to Andreas!


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And is it really possible to securely anonymize the location data that is currently being shared to combat the spread of COVID-19?

To answer these and more questions, SOSA’s Global Cyber Center (GGC) invited our CEO Michael Platzer to join them on their Cyber Insights podcast for an interview. For those of you, who don’t know SOSA: it’s a leading global innovation platform that helps corporates and governments alike to build and scale their open innovation efforts. What follows is a transcript of the podcast episode.

William: Wonderful, now Micheal, when you think about the broad array of cybersecurity trends that are unfolding today – ranging from new threats to new regulations – what is really top of mind for you in 2020?‍
 

Michael: Thanks for having me! We are MOSTLY AI and we are a deep-tech startup founded here in Europe while preparing for GDPR. Very early on, we had this realization that synthetic data will offer a fundamentally new approach to data anonymization. The idea is quite simple. Rather than aggregating, masking or obfuscating existing data, you would allow the machine to generate new data or fake data. But we rather prefer to say “AI-generated synthetic data”. And the benefit is, that you can retain all the statistical information of the original data, but you break the 1:1 relationship to the original individuals. So you cannot re-identify anymore – and thus it’s not personal data anymore, it’s not subject to privacy regulations anymore. So you are really free to innovate and to collaborate on this data – but without putting your customers’ privacy at risk. It’s really a fundamental game-changer that requires quite a heavy lifting on the AI-engineering side. But we are proud to have an excellent team here and to really see that the need for our product is growing fast.

William: Very interesting! Now, we know that location data is among our most accessible PII – we kind of give it out all the time via our mobile device. In the wake of the coronavirus, we are seeing calls to use our location data to track the spread of this pandemic. Is it possible to really effectively anonymize and secure our location data? Or can this data just be reverse engineered? Could using synthetic data help?

Michael: Yes definitely, and we are also engaging with decision-makers at this moment in this crisis. Location data is incredibly difficult to anonymize. There have been enough studies that show how easy it is to re-identify location traces. So what organizations end up with is only sharing highly aggregated count statistics. For example, how many people are at which time at which location. But you lose the dimension at the individual level. And this is so important if you want to figure out what type of socio-demographic segments are adapting to these new social distancing measures, and for how long they do that. And is it 100% of the population that’s adapting, are social contacts reducing by 60% or is it maybe a tiny fragment of segments that is still spreading the virus? To get to this kind of level to intelligence you need to work at a granular level. So not on an aggregated level, but on a granular level. Synthetic data allows you to retain the information on a granular level but break the tie to us individually. We just, coincidentally, in February wrote a blogpost on synthetic location traces – so before the corona crisis started – because we were researching this for the last year. It’s on our company blog and I can only invite people to read it. Super exciting new opportunities now to anonymize location traces!

William: That is exciting – and it sounds as if it could be very helpful, especially given what we are all going through! Now, Micheal, there is an expanding list of techniques to protect data today; from encryption schemes, tokenization, anonymization, etc. Should CISOs look at the landscape as a “grocery shelf” with ingredients to be selected and combined or should they search for one technique to rule them all?

Michael: Well, I don’t believe that there is a one-size-fits-all solution out there. And those different solutions really serve different purposes. It’s important to understand that encryption allows you to safely share data with people that you trust – or you think that you trust. Whether that’s people or machines, at the end, there is someone sitting who is decrypting the data and then has access to the full data. And you hope that you can trust the person. Now, synthetic data allows you to share data with people where you don’t necessarily need to rely on trust, because you have controlled for the risk of a privacy leak. It’s still super valuable, highly relevant information. It contains your business secrets, it contains all the structure and correlations that are available to run your analytics, to train your machine learning algorithms. But you have zeroed out your privacy risk! In that sense, synthetic data and encryption serve two different purposes. So every CISO needs to see what their particular challenge and problem is that needs to be overcome.

William: Well Michael, we’re coming up on our time here. Are there any concluding remarks or anything you would like to add before we hang up?

Michael: Well, we just closed our financing round so we’re set for further growth both in Europe as well as the US. We’re excited about the growing demand for data anonymization solutions, also for our solution. Happy to collaborate with innovative companies, who take privacy seriously. And of course, I wish everyone best of health and that we get – also as a global community – just stronger out of the current crisis.

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