New Delhi: Hoping to unlock faster insights, better predictions and smarter operations, companies across industries are pouring money into artificial intelligence. Research suggests that the true bottleneck may lie much earlier than building AI models, right at the way data is stored and managed.
Recent studies published on IEEE Xplore reveal that database choices, often made before any AI project even begins, can have a profound effect on performance and costs. Milan Parikh, enterprise data architect lead and co-author of the study, explains, “Many organisations don’t realise how much their database architecture influences results. Even with powerful AI, inefficient data handling can eat up time and resources.”
He, who has over 15 years of experience across finance, manufacturing and life sciences, points out that many companies still rely on single-model relational databases to handle a mix of structured records, documents, graphs and streaming data. While this approach seems straightforward, the research shows it can lead to hidden inefficiencies that often go untracked.
Multi-model databases pull ahead
The study compared single-model databases with multi-model systems, which allow different types of data to stay in their native formats. Multi-model systems scored 86 on the Composite Performance Index, outperforming both single-model and polyglot setups in speed, flexibility and reliability.
Multi-model systems showed lower latency for complex cross-domain queries and faster schema evolution, while polyglot systems created extra operational complexity and higher costs.
“Hidden costs are where most companies get caught. It is not just about storage or query time. Engineers spend hours managing transformations, keeping schemas consistent and writing custom integrations, instead of working on AI itself,” Parikh says.
In banking, for instance, teams handling transactions, contracts and real-time market data can face delays because information is scattered across multiple systems, slowing decision-making.
The study identified three main areas where inefficiencies hit hardest – delays in cross-domain queries, slow updates to schema changes and the operational burden of juggling multiple databases. Using a synthetic dataset that could run across all systems, researchers applied uniform queries and measured latency, adaptability, consistency and resource use. Across all these tests, multi-model setups offered the most balanced results.
Why it matters for AI
Enterprise AI usually needs three types of data – structured datasets for training, unstructured text or documents and graph data capturing relationships. Traditional single-model databases often force these into a single format, which adds latency and can reduce model accuracy.
Parikh says, “The question is not whether teams understand their data. It is whether their systems handle it correctly. Many platforms are still built for simple and structured formats.”
The research recommends starting small. Instead of overhauling entire systems, companies can pilot multi-model pipelines in areas where present limitations are visible, such as slow queries or rigid schemas. Tools like Debezium can also modernise legacy systems by streaming updates in real time, without major code rewrites.
As AI adoption grows, the findings highlight one clear truth – even the best models and largest budgets can struggle if the underlying data foundation is not ready. The path to better AI returns may not be more algorithms, but smarter data architecture.
