Hardial Singh has spent over 15 years designing data and AI systems for organizations where failure carries real consequences. His work in healthcare, finance, insurance, and telecommunications has focused on building architectures capable of handling petabyte-scale data while meeting strict regulatory requirements.
At organizations including Kaiser Permanente and Cleveland Clinic, he led initiatives that balanced analytical ambition with the privacy and compliance demands unique to healthcare data. His technical expertise spans hybrid cloud modernization, zero-trust security, MLOps, and generative AI governance.
Singh holds a Master of Science from California State University, Northridge, and a background in mechanical engineering that continues to shape his approach to system design. In this conversation, he discusses what regulated industries teach you about architecture and why governance determines whether AI initiatives succeed or stall. The capabilities enterprises will need as AI becomes more deeply embedded in business operations.
You work across the full data platform stack, from Hadoop ecosystems to modern lakehouse architectures. When organizations are choosing between data mesh, data fabric, and more traditional centralized approaches, what factors should drive that decision?
I usually start by taking the conversation away from tools and architecture diagrams and looking at how the organization actually operates day to day. Data mesh, for example, requires a high level of organizational maturity. Domain teams need to truly own their data, not just produce it. That means accountability for quality, security, documentation, and lifecycle, and that’s a big cultural shift for many enterprises.
If those foundations aren’t already in place, mesh often becomes an organizational experiment rather than a sustainable data strategy. Teams end up building pipelines, but governance, consistency, and trust suffer.
Data fabric tends to work better in large enterprises that already have platform fragmentation—multiple clouds, legacy systems, different storage engines, and strong regulatory requirements. In those environments, consistent access controls, lineage, and policy enforcement usually matter more than pushing ownership all the way to the edges.
Traditional centralized models still have a place, especially in regulated industries where auditability, compliance, and risk control are non-negotiable. In practice, what I see working best is a hybrid approach. Governance, security, and standards remain centralized, while execution and innovation are distributed where teams are ready. The key mistake is treating these models as ideological choices rather than as composable building blocks.
Real-time streaming and batch processing serve different analytical needs. How do you determine the right balance for an enterprise, and what architectural patterns help organizations support both without duplicating infrastructure?
I typically frame this around decision latency. I ask stakeholders what decisions need to happen in seconds versus which ones are still effective with minutes or hours of delay. In most enterprises, truly real-time use cases are limited but extremely important, such as fraud detection, operational alerts, and real-time customer signals.
The majority of analytics, reporting, and even a lot of machine learning still work very well in batch or micro-batch. The challenge is that organizations often overbuild real-time systems because they sound more modern, not because the use case actually requires it.
Architecturally, the goal is to avoid parallel stacks. Shared storage layers, unified metadata, and consistent security controls allow streaming and batch workloads to coexist on the same platform. Event-driven ingestion patterns work well when the same data feeds both low-latency consumers and downstream analytics.
When streaming and batch are built as separate platforms with different governance and security models, cost and operational complexity grow very quickly. In my experience, balance comes from convergence and reuse, not duplication.
Data quality problems often derail AI initiatives before they start. What practices have you found effective for building data quality into the engineering process rather than treating it as a downstream cleanup effort?
Data quality tends to fail when it’s treated as someone else’s problem. The most effective change I’ve seen is enforcing quality at the point of ingestion, before data ever reaches analytics or machine learning pipelines. That includes clear schema contracts, validation rules, and explicit ownership from the start.
Another critical piece is observability. Teams need visibility into data freshness, drift, anomalies, and failure patterns as operational signals, not as reports reviewed weeks later. When issues surface immediately, they get fixed quickly and don’t silently propagate into models or dashboards.
The real shift happens when data quality is tied to business outcomes. When teams see that poor data delays model deployment, introduces compliance risk, or erodes trust with stakeholders, quality stops being optional. It becomes part of the normal engineering discipline rather than a cleanup activity at the end.
You’ve worked extensively with cross-functional teams, including developers, business analysts, and compliance officers. How do you bridge the communication gap between technical teams and business stakeholders who may not share a common vocabulary?
I focus on outcomes rather than abstractions. Engineers often think in terms of scalability, performance, and correctness, while business and compliance teams think in terms of risk, cost, and timing. My role is to translate between those perspectives without oversimplifying either side.
With compliance and risk stakeholders, I ground the conversation in concepts like traceability, access controls, and accountability. With engineers, I translate those same requirements into concrete design decisions instead of vague constraints, so they understand the “why” behind the architecture.
Over time, trust builds when stakeholders consistently see their priorities reflected in technical decisions. Once that trust exists, conversations become collaborative instead of adversarial, and alignment happens much more naturally.
Edge computing is pushing data processing closer to where data originates. How do you see edge architectures changing enterprise data strategies, particularly for industries with distributed operations?
Edge computing changes where decisions are made, not just where data is processed. In industries like manufacturing, telecom, and healthcare, latency, availability, and local autonomy often matter more than central aggregation.
What edge architectures really expose is whether an organization has discipline around models, policies, and governance. Running intelligence closer to the source requires consistent versioning, security controls, and lifecycle management across environments. Without those foundations, edge deployments tend to exacerbate existing problems rather than solve them.
I don’t see edge as replacing centralized platforms. It extends them. And in doing so, it forces enterprises to standardize how intelligence is deployed, monitored, and governed across a much wider operational footprint.
Many enterprises struggle with tool sprawl across their data and analytics stack. How do you approach platform consolidation without disrupting teams that have built workflows around specific tools?
I approach consolidation as an evolution rather than a mandate. Tool sprawl usually exists because teams solved real problems under real constraints, and dismissing that history almost always leads to resistance.
The most effective approach I’ve seen is consolidating shared capabilities first—things like security, metadata, governance, and orchestration—while allowing teams to keep familiar tools where they still add value. That creates immediate benefits without breaking existing workflows.
Over time, new projects naturally gravitate toward the consolidated platform because it’s easier, safer, and better supported. Older tools phase out organically. Forced migrations create friction, while enabled adoption builds momentum.
You hold certifications from IBM, Google Cloud, and Cloudera in AI and data architecture. How do you approach continuous learning in a field where platforms and best practices shift so frequently?
I anchor my learning in fundamentals. Distributed systems, security principles, and data lifecycle management don’t change nearly as fast as vendor features, and they give me a stable way to evaluate new technologies.
I also learn primarily through implementation. Building systems, troubleshooting failures, and seeing how designs behave under real pressure teaches far more than documentation alone. Those experiences create intuition that you can’t get from courses alone.
Certifications help validate knowledge, but real expertise comes from navigating ambiguity, especially in situations where there isn’t a clean or obvious answer.
Career transitions in tech often follow unpredictable paths. For engineers looking to move from hands-on development into architecture and solution design, what capabilities should they prioritize building?
The biggest shift is moving from solving isolated problems to owning end-to-end outcomes. Architecture is fundamentally about trade-offs, not perfection. Performance, cost, speed, security, and risk all pull in different directions, and architects are responsible for balancing them.
Communication becomes just as important as technical depth. You need to explain decisions clearly, defend them under scrutiny, and adapt when constraints change. Writing, presenting, and listening become critical skills.
Engineers who succeed in this transition take responsibility for decisions and their consequences, not just for producing diagrams or recommendations.
Looking at the current state of enterprise data platforms, what problems do you think the industry is still solving poorly, and where do you see the most promising progress?
We’re still struggling to operationalize AI responsibly at scale. Many platforms make experimentation easy, but governance, monitoring, explainability, and lifecycle management often lag, particularly in regulated environments.
Where I see real progress is in convergence. Data engineering, analytics, and machine learning platforms are increasingly aligning rather than competing. The most successful organizations aren’t adding more tools; they’re simplifying the experience for teams while strengthening control behind the scenes.
I think the future belongs to platforms that hide complexity without sacrificing accountability.
