ITTech Pulse Exclusive Interview with Máté Gulyás, CEO of Datapao

IT Tech Pulse covers an interview with Máté Gulyás on Datapao’s vision, data modernization, and AI strategies. Get inside the interview to see what we asked and discussed:-

Datapao has been growing rapidly in the data and AI consulting space. Can you share your vision for the company and what drives your mission?

At Datapao, our vision is to help organizations unlock the full potential of their data, not just as a technical resource, but as a driver of real business transformation. From the very beginning, we’ve believed that data engineering and AI should not be siloed initiatives they need to be woven into the fabric of an organization’s operations, decision-making, and culture.

We see our role as a catalyst: enabling companies to modernize their infrastructure, adopt AI responsibly, and, most importantly, bridge the gap between ambition and execution. Too many organizations have bold  Artificial Intelligence strategies on paper but struggle to operationalize them. Our mission is to ensure that data and AI initiatives don’t stall at the proof-of-concept stage, but instead become scalable, secure, and impactful systems that deliver measurable value. That’s what keeps us motivated turning “what if” into “what works.”

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How does Datapao approach modernizing data platforms for enterprises? What makes your methodology effective for IT leaders?

Modernizing a data platform is never just about swapping technologies it’s about rethinking the architecture to meet today’s demands for speed, scalability, and flexibility. At Datapao, we start by understanding the organization’s long-term business goals, not just their immediate technical needs. That way, the solution we design is not only performant but also future- proof.

Our methodology blends deep technical expertise particularly with Databricks and major cloud providers like Azure and AWS with a strong focus on operational excellence. We’ve developed repeatable patterns for migrations, including our “One-Stop Cloud Data Migration” approach, which accelerates Hadoop-to-cloud transitions while minimizing downtime and risk. We also emphasize governance and monitoring from day one, so IT leaders can be confident the platform won’t just run well on launch day, but will remain reliable and cost-efficient as workloads grow.

You’ve worked with industries ranging from manufacturing to financial services. How do you tailor your solutions for different sectors?

Every industry has its own data DNA. In manufacturing, for example, we see a heavy focus on real-time analytics for predictive maintenance and supply chain optimization. In financial services, compliance and security dominate the conversation, which means architecture choices and governance frameworks need to be much stricter.

Our approach is to embed ourselves in the client’s operational reality. We don’t just deliver a technical blueprint we map the platform design to the industry’s regulatory, performance, and integration requirements. For energy clients, that might mean optimizing for massive streaming datasets from IoT sensors; for pharmaceuticals, it might be ensuring data lineage and auditability for regulatory submissions. The key is adaptability: our technical foundations are consistent, but the configuration, tooling, and governance layers are always tailored.

Also Read: Agentic AI in Financial Services: Redefining Productivity, Efficiency, and Security

Skills gaps often slow down cloud and AI transformation projects. How does Datapao help organizations upskill their teams?

Technology alone can’t drive transformation people need to be empowered to use it effectively. We’ve built training and enablement into the heart of our consulting model. This means that every engagement includes a learning component, whether it’s hands-on workshops, formal training sessions, or shadowing opportunities where client teams work directly alongside our engineers.

As an official Databricks training partner, we offer customized curricula that address both foundational skills like Spark and Delta Lake basics and advanced topics such as MLOps, real-time streaming, and large-scale data governance. Our goal is that, by the end of a project, the client’s IT team isn’t just maintaining the system, but actively enhancing it. That’s how you ensure long-term ROI from a data platform investment by building internal capability, not dependency on external consultants.

With growing regulations around data and AI, how does Datapao help IT leaders navigate compliance without slowing innovation?

Regulation is no longer an afterthought it’s shaping how IT leaders design and deploy their platforms from the start. Whether it’s GDPR in Europe, financial sector mandates, or emerging AI governance frameworks, compliance now touches everything from data ingestion to model deployment.

At Datapao, we integrate compliance into the architecture itself. This means designing systems that have data lineage, access controls, encryption, and auditability baked in not bolted on later. For IT leaders, this approach reduces the risk of costly rework when regulations tighten. And importantly, it allows innovation to continue, because compliance is treated as a design constraint, not a blocker. We also partner closely with legal and compliance teams so technical and policy considerations evolve together, keeping the organization agile.

Looking ahead 3–5 years, what trends in data and AI excite you most, and how is Datapao preparing to lead?

The next big shift is going to be about making AI and data truly operational at scale integrated into business processes in real time, with robust governance and clear ROI. I see three trends driving this:

The rise of real-time analytics as a baseline expectation, not a premium capability.

The maturity of MLOps and DataOps, making model deployment and monitoring as routine as software releases.

The embedding of Generative AI  into domain-specific workflows, rather than as standalone chatbots or demos.

Datapao is preparing by investing in our own internal accelerators, strengthening our partnerships with Databricks and cloud providers, and expanding our training portfolio for the skills these trends demand. The future belongs to organizations that can connect technical capability with business value quickly and repeatably and that’s exactly where we’re focusing our energy.

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