Enterprises are generating vast volumes of interconnected data across customers, partners, platforms and regulators, exposing the limits of traditional database approaches built for linear, transactional workloads.
Suhail Gulzar, Senior Manager, Solutions Engineering at Neo4j, tells TechObserver.in that graph-driven models help organisations interpret relationships at enterprise scale, improving how they ground AI outputs in business context, detect fraud and cyber threats in real time and build traceable data lineage for explainable decisions.
“The problem is not a lack of data; it is the inability to interpret interconnected data quickly and meaningfully at enterprise scale,” Gulzar says.
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Edited Excerpts:
Indian enterprises are generating vast volumes of interconnected data. Where are traditional databases proving inadequate, and what concrete problems are organisations failing to solve at scale today?
Indian enterprises are operating in environments where data is no longer linear or isolated. Customer interactions span multiple channels, supply chains stretch across ecosystems, and financial and operational data flows between partners, platforms, and regulators. While relational databases continue to perform well for structured transactions, they tend to struggle when the primary challenge is understanding how entities relate to one another over time.
In areas such as fraud detection, customer lifecycle intelligence, network operations, and supply-chain risk, organisations are attempting to analyse connections across millions of data points. This leads to complex joins, duplicated datasets, and slower query performance, which in turn delays decision-making.
As a result, many enterprises still lack a unified, real-time view of customers, risks, or operational dependencies. The problem is not a lack of data; it is the inability to interpret interconnected data quickly and meaningfully at enterprise scale.
Generative AI adoption is accelerating, but concerns around accuracy and hallucinations persist. How do graph-based approaches such as GraphRAG change the way enterprises ground AI outputs in real business data?
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The reliability challenges seen in enterprise AI deployments are often tied to a lack of structured context. When large language models rely primarily on documents or embeddings, they retrieve information based on similarity rather than organisational reality. This is where inaccuracies and hallucinations emerge.
Graph-based retrieval approaches are being explored to address this gap by grounding AI responses in relationships across enterprise data, connecting people, transactions, systems, and decision pathways. Instead of retrieving isolated content, models can reference how information is connected, which improves both contextual relevance and explainability.
This shift reflects a broader movement in enterprise AI from document-centric retrieval to knowledge-driven reasoning. Organisations are beginning to treat structured context and provenance as essential components of AI architecture, particularly in use cases such as copilots, compliance workflows, and internal decision-support systems.
Fraud, cybersecurity threats, and identity misuse are growing in complexity across sectors like banking and telecom. How are graph analytics being deployed in real-time, and what measurable improvements are organisations seeing?
Fraud and cyber threats have become increasingly networked. Attack patterns often involve linked accounts, coordinated behaviour, synthetic identities, and rapidly evolving device ecosystems. Traditional monitoring systems, which rely heavily on rules or isolated anomaly detection, often struggle to identify these patterns early.
Graph-driven analytics enables organisations to map how entities connect across accounts, transactions, devices, and behaviours and detect unusual patterns in real time. This allows teams to identify coordinated threats rather than isolated incidents.
Enterprises deploying such approaches report improvements in early detection, prioritised investigation, and reduced false positives. In cybersecurity contexts, relationship-based analysis has also helped map infrastructure dependencies and surface vulnerabilities more quickly than traditional monitoring approaches, strengthening both response times and resilience. The broader shift is from reactive detection to understanding the structure of threats as they form.
Regulators are demanding greater transparency, traceability, and explainability in AI-driven decisions. How can graph data lineage and knowledge graphs help companies meet these requirements without slowing innovation?
As AI systems influence financial decisions, customer interactions, and operational workflows, regulators increasingly expect organisations to explain not just outcomes, but also the decisions, sources, and pathways that led to them. This requires visibility across datasets, models, and decision chains. Knowledge graphs and lineage frameworks provide a way to map how data flows, is transformed, and contributes to decisions. This creates an auditable structure that supports accountability without forcing organisations to slow down experimentation.
By embedding traceability into the data architecture itself, enterprises can maintain governance while continuing to build and deploy AI-led solutions. Over time, this approach shifts compliance from a separate process to an integral part of the operational fabric of data and AI systems.
Neo4j says a large share of Fortune 100 companies use its technology. What is driving adoption in India and Asia-Pacific, and which sectors are emerging as the fastest movers?
Across Asia-Pacific, adoption is being driven by the growing complexity of digital ecosystems, telecom networks, financial platforms, e-commerce, and public digital infrastructure. Organisations are prioritising initiatives such as fraud prevention, customer intelligence, and supply-chain resilience that depend on understanding relationships at scale. This is reflected globally as well, with graph technologies now becoming an integral part of enterprise data and AI architectures, particularly among large financial institutions, telecom operators, retailers, and public-sector organisations.
In India, the pace of digital infrastructure, identity, payments, and platform-led services is accelerating this shift, with BFSI, telecom, logistics, and government programmes emerging as early adopters.
As India builds digital public infrastructure and scales AI-led services, where do graph databases fit into this ecosystem, and what constraints could limit adoption over the next few years?
India’s digital public infrastructure, spanning identity, payments, healthcare, mobility, and governance, is built on interconnected data flows. Delivering services at this scale requires understanding relationships across citizens, institutions, and systems in near real time.
Relationship-driven data models can support initiatives such as fraud prevention in public schemes, cross-agency coordination, citizen service delivery, and AI-led operational insights. They also enable the creation of unified knowledge layers that connect data across departments and platforms.
However, adoption will depend on organisational readiness. Skills availability, integration with existing relational and data-lake systems, cost considerations, and governance frameworks will all influence how quickly such approaches scale.
The key constraint is not technological capability but the transition from siloed data management to connected intelligence, a shift that requires new skills, architectures, and long-term data strategies.
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