I’m thrilled to sit down with Dominic Jainy, a seasoned IT professional whose expertise in artificial intelligence, machine learning, and blockchain has positioned him as a thought leader in navigating the complexities of modern data landscapes. With a passion for applying cutting-edge technologies across industries, Dominic brings a wealth of insights into how large enterprises can scale their data engineering efforts amidst unprecedented data growth. In our conversation, we explore the challenges of managing massive data volumes, the intricacies of real-time and unstructured data processing, industry-specific data needs, the importance of governance and compliance, and the architectural and team-building strategies that drive success in enterprise data management.
How do you see the explosive growth of data—projected to reach 181 zettabytes by 2025—shaping the biggest challenges for large enterprises today?
I think the sheer scale of data growth is forcing enterprises to rethink everything about how they operate. We’re talking about an almost doubling of data in just a couple of years, and most organizations simply aren’t built to handle that. The biggest challenges are around scalability—both in terms of infrastructure and strategy. You’ve got to process and store this data without breaking the bank, while also ensuring it’s accessible and useful. Then there’s the issue of extracting real value; with so much noise in these massive datasets, figuring out what matters for business decisions is like finding a needle in a haystack. It’s a constant balancing act between capacity, cost, and insight.
Can you share how the daily deluge of 2.5 quintillion bytes of data on the internet has influenced the way your organization approaches data engineering?
Absolutely. That daily flood of data has pushed us to prioritize agility and efficiency in a way we hadn’t before. We’ve had to shift from just collecting data to focusing on how to process it quickly and meaningfully. It’s not enough to have the information; we need systems that can filter, analyze, and act on it almost instantly. This has meant investing heavily in real-time processing capabilities and rethinking our pipelines to handle high-velocity data streams. It’s also forced us to be more selective about what data we store long-term, because keeping everything isn’t sustainable financially or operationally.
With real-time data processing being so critical—where 63% of use cases need data within minutes—how does your organization balance speed with accuracy?
That’s a great question, and it’s something we wrestle with every day. Speed is non-negotiable in many of our use cases, especially when dealing with streams from IoT devices or financial transactions. Our approach has been to build layered architectures that prioritize rapid ingestion and initial processing, while running parallel validation checks to ensure accuracy isn’t compromised. We’ve also leaned on automation and machine learning to flag anomalies in real time, so we’re not just pushing data through quickly but also catching errors before they impact decisions. It’s about creating systems that are both fast and smart, which often means constant tuning and investment in the right tools.
Unstructured data is set to make up 80% of global data by 2025. How does your team tackle the complexity of processing formats like social media posts or multimedia files?
Unstructured data is a beast, no doubt about it. With things like social media posts, emails, or video content, the lack of a predefined structure makes traditional processing methods fall apart. Our strategy has been to invest in flexible frameworks that can ingest and categorize this data using AI-driven tools for natural language processing and image recognition. We focus on extracting context—turning raw text or media into structured insights by identifying patterns or sentiments. It’s not perfect, but by pairing these tools with human oversight for critical applications, we’ve been able to manage the variety and volume without getting overwhelmed. It’s a continuous learning process as new formats emerge.
Different industries have unique data needs. Can you talk about the specific data engineering challenges in your sector and how you address them?
Certainly. In the sectors I’ve worked with, like finance and healthcare, the challenges are often tied to both volume and sensitivity. For instance, in finance, the need for real-time fraud detection means our systems have to process transactions with zero latency while adhering to strict regulatory standards. We’ve built specialized pipelines that prioritize speed and security, using blockchain-inspired techniques for immutable audit trails. In healthcare, the challenge is often around integrating disparate data sources—like patient records and genomic data—while ensuring privacy. Here, we’ve focused on interoperable systems and robust encryption to make data usable without risking compliance. Each industry demands a tailored approach, and that’s where deep domain knowledge becomes critical.
Data governance has become a business-critical capability. How does your organization approach managing and protecting large data volumes through governance?
Governance is the backbone of everything we do with data. Without it, you’re just sitting on a ticking time bomb of risks. Our approach starts with clear ownership—every dataset has an accountable steward who defines how it’s handled, accessed, and protected. We’ve implemented frameworks that enforce policies across the data lifecycle, from ingestion to archival, ensuring quality and security at each step. This includes regular audits and automated compliance checks to catch issues before they escalate. It’s also about culture; we train teams to think of governance as everyone’s responsibility, not just a checkbox for the IT department. That mindset shift has been key to managing data at scale while minimizing vulnerabilities.
Navigating complex regulations like GDPR or HIPAA can be daunting. How do you ensure compliance across regions or industries without hindering operations?
Compliance is a tightrope walk, especially when you’re operating globally with regulations like GDPR in Europe or HIPAA in the US. Our strategy is to build compliance into the architecture from the ground up, rather than treating it as an afterthought. We use automated tools to monitor data flows and flag potential violations in real time, which helps us stay proactive. We also maintain a centralized repository of regulatory requirements mapped to our processes, so teams know exactly what’s needed in each region. The trick is to standardize where possible—like with data encryption or access controls—while allowing flexibility for local nuances. This keeps operations smooth while ensuring we’re not exposed to fines or reputational damage.
What role does data architecture play in enabling scalability for enterprises dealing with growing data demands, and how have you seen this work in practice?
Data architecture is the foundation of scalability. Without a well-designed structure, your systems will buckle under growth. We’ve adopted a layered approach—staging, refinement, and serving layers—that allows us to process data systematically, from raw input to actionable output. Decoupling storage and compute has been a game-changer; it lets us scale each independently based on demand, slashing costs by focusing resources only where they’re needed. In practice, I’ve seen this reduce infrastructure expenses significantly while maintaining performance during peak loads. It’s not just technical—it’s strategic, ensuring we can grow without constant overhauls.
Team structure is often as important as technology in scaling data efforts. Can you describe how you’ve organized data engineering teams to handle enterprise-level complexity?
You’re absolutely right—technology alone won’t cut it. We’ve adopted a hub-and-spoke model, where a central hub of data experts sets standards, builds core tools, and ensures governance, while spokes—embedded in business units—focus on domain-specific needs. This balances centralized control with localized agility, so finance or retail teams can tailor solutions without reinventing the wheel. We also prioritize cross-functional collaboration and mentorship to break down silos and retain expertise. I’ve seen this structure scale seamlessly as we’ve added new units, keeping everyone aligned while fostering innovation. It’s about empowering teams without losing coherence.
What is your forecast for the future of data engineering as data volumes continue to expand toward 181 zettabytes by 2025?
I believe the future of data engineering will be defined by automation and intelligence. As data volumes hit 181 zettabytes, manual processes or even traditional automation won’t keep up. We’ll see AI and machine learning become integral to data pipelines, not just for analysis but for managing the pipelines themselves—optimizing resource use, predicting bottlenecks, and ensuring quality. I also expect a deeper focus on sustainability; the energy cost of processing this data will push enterprises toward greener architectures. Lastly, privacy and ethics will take center stage, with governance frameworks evolving to address public and regulatory scrutiny. It’s an exciting, challenging road ahead, and organizations that adapt proactively will turn this data deluge into a competitive edge.