Knowledge graphs are structured, graph-based representations of data entities and their relationships. They can handle any combination of structured and unstructured data and, essentially, transform all the disconnected data silos in large organizations into a single data source that provides decision-makers with actionable information. Knowledge graphs form the connective layer running on top of cloud data platforms. They contextualize the structured and unstructured data accessible to the platform. They enable people — and AI algorithms — to understand, reason about, and extract insights from the enormously complex sets of data available to the platform.
Key Challenges
Let’s briefly consider the challenges associated with unstructured data, which may include the vast numbers of emails generated in a large company, records of internal and customer-facing chat sessions, PDFs, pictures, and many other types of information. Working with unstructured data means doing much more than simply cleansing the data as you would with the information stored in relational databases or spreadsheets. Just automating the process of interpreting, categorizing, tagging, and summarizing unstructured data is impossible without the right technology.
Scale and Performance
The right knowledge graph for your business will be scalable enough to handle all the data you expect to become available in the coming years. The idea of “scale” in this context is nontrivial given the huge data volumes involved. Consider the time needed to load and transform your data and your latency requirements for complicated queries. Look at how the graph uses massively parallel processing (MPP) query techniques to handle the computational intensity required in your operation. The right will also use your established metadata schemas, data models, and data governance processes to automate knowledge graph construction. A good platform will obviate manual processing and automatically use relational database schemas.
User Experience
It’s crucial that your graph-powered system be accessible to executives, product managers, R&D leads, and other stakeholders. They must be able to understand the system, run queries, and obtain meaningful insights without IT support. Be sure you understand how the vendor’s software works to build and access a knowledge graph and verify that the process is clear and simple. Make sure your own technical team can learn to build and manage knowledge graphs with the same level of facility they use to handle tables, datasets, and data products in your current cloud data platform. Verify that business users can use their familiar tools to ensure seamless integration into their workflows.
Security, Privacy, and Compliance
It is imperative that the system protect sensitive data with robust access controls, comprehensive audit trails, and compliance with regulations like GDPR, HIPAA, and more, depending on your industry. Examine the vendor's approach to mitigating security risks and maintaining existing security protocols and learn how the software supports your internal governance and compliance requirements.
Use of Ontologies
Ontologies are fundamental to how knowledge graphs work and are crucial to the development of effective and accurate generative AI applications. They describe data in business language terms, help with data interoperability, and facilitate transparency and trust. When selecting a knowledge graph supplier, be sure you understand how the software uses ontologies to describe your data, that it supports the industry standards you need, and will enable you to deal with all your current requirements as well as anything new you think might be on the horizon.
Support for Enhanced AI through Graph RAG
Retrieval-Augmented Generation (RAG) is a popular technique that confines generative AI models’ responses to verified information. Graph RAG (also called GRAG) goes further and uses contextual data from a knowledge graph to minimize hallucinations. This causes natural language user interfaces to produce even more accurate and complete responses. Graph RAG is great for dealing with any combination of structured and unstructured data and makes your data platform even more valuable to your people.
How Long Will It Take to Realize Positive ROI?
Above all, be sure to assess how quickly the system can be implemented and start delivering value to your organization. It’s not good enough to simply check a bunch of boxes on an implementation. Decision-makers must be able to use the system effectively and trust its output. When that happens, you can measure the ROI for your total investment in the data platform along with the knowledge graph and get to the real business of running your company.
Click to learn more: altair.com/knowledge-graphs.
