Ramon Chen Speaker Profile

Ramon Chen, Chief Product Officer, Reltio, will be part of our expert panel at the next Comma Live Podcast: The Future of AI – is Skynet coming? Here, he tells us about his experience in the MDM world – and shares his insight on how GDPR could put AI and Machine Learning applications on the back foot.

What is your background?

I’ve always been fascinated with programming. When I first got my hands on a Sinclair ZX 80 with a whopping 1kb RAM and 4kb ROM, I spent many long nights trying to backup 50 line BASIC programs to cassette tapes, only to have it fail time and time again. I graduated from the University of Essex with a Degree in Computer Science, specialising in Artificial Intelligence and Expert Systems under the guidance of my tutor Richard Bartle - the inventor of MUD (Multi-User Dungeon). MUD is one of the first examples of a virtual world. It was founded with LISP and Prolog AI languages, as a precursor to today's role-playing games.

My early programming career was on the IBM System 38 / AS400, where I learnt CASE (Computer Aided Software Engineering), a type of 4GL. I was then one of the first people to train in Oracle (and Informix!) a data administration system, before becoming the principle architect for Synon - at the time, the leading global application development tool.

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After 10 years of coding, I moved into product management. I wanted to be responsible for delivering a whole product, with a role that combined programming with business, and got me closer to customers. This passion took me through several CMO/VP of Marketing roles to reach where I am now - as Chief Product and Marketing Officer at Reltio: continuing to bridge the gap between technology and business.


How did you get into the world of data?

Throughout my career I've been involved in many aspects of data processing, from creating app development tools and databases to business applications. As Chief Product and Marketing Officer at Reltio, I’ve been lucky enough to help shape a market strategy and vision that aligns perfectly with my own experience and involves all aspects of the management, usage, governance, and even monetisation of data. 


What are the biggest lessons you have learned?

One key lesson is that reliable data is the foundation for all initiatives. The saying ‘garbage in, garbage out’ rings true. Without continuous data quality (with emphasis on the word 'continuous' since data is always changing), nothing else matters. Regulations and data privacy laws like GDPR make things increasingly stringent, so governance, auditing and fine-grained controls on the management and maintenance of data are vital.

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How do you see the world of data changing in the near future?

Data is fast becoming a company's most valuable asset. All industries have been affected by the value of data; retail, manufacturing, healthcare, life sciences, financial services, you name it. They all rely on the efficient organisation of trusted data to deliver products and personalise customer experiences.

However, the major shift I see happening between the data of today and tomorrow comes down to compliance and the regulatory emphasis on data privacy. The movement to declare all personal data as ‘property’ has the potential to leave us in a very different data position compared with now, and could render traditional data management processing tools impractical. This may severely impact the promise of AI and Machine Learning before it even gets going. 


What are your opinions on AI and Machine learning? 

How applicable are they right now?

Unfortunately, most companies are not ready to execute AI and Machine Learning. This is not down to lack of data, but rather because they cannot guarantee the reliability or compliance of the data they end up using. It’s an ironic joke in the industry that expensive data scientists spend about 80% of their time trying to clean and source data, and the remaining 20% talking about the fact they need to clean and source data. 

A little known fact about GDPR is the consumer's right to ask how their data is being leveraged in decision making. A consumer may request the rule or algorithm that resulted in the rejection for a loan, for example. This means that AI and Machine Learning as a ‘blackbox’ is not satisfactory. It will be very interesting to see how this plays out. I believe it has the potential to put AI and Machine Learning, as well as any form of automated processing and targeted marketing, on the back foot. 

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What is your favourite use of AI in pop culture?

Tough question. There are endless great examples; the improvement of healthcare, autonomous self-driving cars, how close we are to Terminator-capable robotics… When I majored in AI back in 1987 I had no idea what was to come, but it is gratifying to see that we are so far beyond my first encounter with the concepts of AI in role-playing games and virtual worlds. But as I believe the future is bright, the most honest answer is that my favourite use is yet to come. 

John will be on the panel at our next Comma Live Podcast: The future of data – is Skynet coming? It’s taking place on 27th September at the Hoxton Grill. To register your interest, click here.