Headline Partner

Paul Moxon, Senior Vice President Data Architectures and Chief Evangelist, Denodo Technologies

Describe your career to date

My career has been varied and interesting while never leaving the IT and computing space. I started as a programmer developing business applications in DEC Basic 2. My own personal interests took me into more technical software development until, five or so years later, I was developing real-time control systems for oil and gas pipelines in the Netherlands.

Working directly with the customer end users in the Netherlands led me into a consulting role and a customer advocate within software companies. I enjoyed the interaction with customers, trying to help them solve their problems and guide them into newer, different approaches to a challenge. This led me to my current role at Denodo Technologies as Chief Evangelist.

I work with customers helping them to define and realize their data strategies and data architectures in a way that is going to allow them to grow and adapt in the future. I also act as a champion and evangelist for the use logical data architectures and frequently present at conferences about the value of these architectures and how they can accelerate the adoption of new technologies, such as artificial intelligence (AI) and generative AI (genAI).

What challenges do you see for data in the year ahead that will have an impact on your clients and on the industry as a whole?

I would paraphrase the realtor mantra of “location, location, location,” and say that the biggest challenge for every organization in the coming year is going to be “AI, AI, AI” – especially genAI. This is a hot topic and the hype around ChatGPT over the past year has driven discussions about how organizations can adopt and take advantage of genAI. How is this a data challenge? Organizations need to get their data to a level where it can be used by the AI and genAI models. This means that it must be available to the AI models in a format that is easily accessible, understandable, and usable without requiring the AI model to perform mental gymnastics.

The data also need to be relevant to the task at hand to avoid confusion and hallucinations. The quality, accuracy, and timeliness of the data also needs to be addressed. Most organizations are nowhere near this state of readiness with their data. Their challenge will be to achieve some semblance of data AI readiness to enable the adoption of these new technologies.

How are you developing the data literacy of a) your own organization and b) your clients?

As a company, Denodo is very active in addressing data literacy. We are a data-driven organization, and we ensure that employees have access to the data they need for their work and understand the data they are using. Being data-driven without employees understanding the data is a contradiction in terms!

How are you preparing your organization and your clients for AI adoption and change management?

I passionately believe that the data readiness side of AI adoption is the biggest challenge facing organizations. It is not that difficult to implement an AI model, especially when they are being offered as-a-service and the internet has many examples of building and integrating with AI models.

The real challenge is getting the data to be AI ready, and this is an area where we have a lot of experience and knowledge. We are having many discussions with our clients about how they can get their data AI ready to accelerate the adoption of AI. It is a fascinating topic and I am sure that I will have many discussion about this in the coming year.

Paul Moxon
has been included in:

100 Enablers 2022 (USA)
100 Enablers 2023 (USA)
100 Enablers 2024 (USA)

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