Powering the future: Why knowledge graphs are the key to data intelligence
By Rajiv Shesh, Chief Revenue Officer, HCLSoftware
For organisations in India to truly unlock the power of Artificial Intelligence (AI), the foundation must be high-quality, trustworthy data. As AI adoption grows across the country – from finance to healthcare, reliable data is no longer a luxury; it is essential for creating scalable and dependable AI solutions.
However, many organisations face a significant challenge: they are keen to deploy AI but are not confident in the quality or preparation of their data. Building AI solutions on shaky data foundations leads to errors, poor results (often called ‘hallucinations’), compliance problems, and costly fines. For instance, using outdated patient records in a healthcare setting could lead to serious misdiagnoses, while flawed financial data can result in poor investment decisions.
The Next Step in Data Management: Graph Power
To keep pace with rapid AI advancements, organisations need a modern data management approach that prioritizes quality, reliability, and speed. Traditional data systems often struggle to handle complex, evolving business needs because their rigid models are hard to update and rarely used effectively by business teams.
This is where knowledge-graph-powered data intelligence platforms come in.
Think of a knowledge graph as an intelligent ‘map’ for all your company’s data. Unlike simple tables and columns that do not show how pieces of data connect, a knowledge graph creates a structured, interconnected representation.
It essentially acts as a smart search engine for your enterprise data.
Instead of wasting time manually searching through thousands of data assets, business users can visually navigate their data landscape and get precise answers instantly, often just by asking a simple question in plain language. This capability is transformative for decision-making across the Indian market.
The Advantage of Federated Knowledge Graphs
Today, critical business data – whether in the cloud or on-premises legacy systems – is spread across many distinct locations. Data silos are a major problem.
While some solutions try to fix this by gathering all data into a single, centralized knowledge graph, this creates new issues like more data movement, slower access, and complex governance.
A better solution is the federated knowledge graph approach.
Decentralized Integration: It connects data across multiple distributed sources without forcing you to move all of it into one repository.
Faster and Governed: This reduces the need for large-scale data movement, speeds up real-time data access, and makes data governance easier.
A Complete View: By creating a unified layer that links disparate systems, organizations get a complete, contextual view of all their data without sacrificing speed or agility. This empowers business users with richer, more relevant insights.
Real-World Applications Across India
Federated knowledge graphs offer powerful solutions for industries across the Indian market:
Financial Services: For real-time fraud detection, federated knowledge graphs can analyse transaction patterns across multiple systems without having to move sensitive financial data. This reduces financial risks instantly and effectively.
Retail: Retailers deal with a massive variety of data-from e-commerce platforms and sales records to customer service logs. A federated graph can connect all these sources seamlessly. Non-technical users can simply ask, ‘What were our total sales last quarter?’ or ‘What are our customer satisfaction scores?’ and get meaningful, correlated results immediately.
Healthcare: In healthcare, keeping patient data secure and private is paramount. Federated graphs maintain compliance with data residency and privacy regulations by keeping sensitive data under local control. They still allow for crucial cross-institutional insights by linking diverse patient and research data, providing the rich context AI models need to develop personalized treatment plans with verified, secure information-significantly reducing the risk of AI hallucinations.
Choosing the Right Platform
For Chief Data Officers and IT leaders, the key consideration when evaluating data solutions is how well they support distributed governance and cross-system collaboration without compromising speed.
A data intelligence platform using a federated knowledge graph model achieves this balance:
1. Distributed Control: It allows for distributed data governance while maintaining central oversight, letting data stay localized to meet data residency and security requirements.
2. Deeper Context: It goes beyond simple data catalogs by capturing the semantic relationships between data assets, providing a deeper understanding and more meaningful insights.
3. Breaks Down Silos: It prevents the formation of new data silos, ensuring interoperability across diverse systems and with external partners.
By adopting this graph-powered approach, organizations in India can build a truly trusted, intelligent data foundation that is essential for sustainable, high-impact AI innovation.