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?
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 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 this data. It is one thing to protect your data, but if you don’t have a technology, like synthetic data, to unlock this 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 afterward. 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 can 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.”
If you would like to know more about synthetic data and innovation, feel free to reach out to Andreas!