A knowledge graph is a network of interconnected data points representing these real-world entities and their relationships. Its power lies in its ability to provide context between different pieces of information, which is essential for AI systems to understand and utilize your brand data effectively. Google and Bing have been building out the largest knowledge graphs to power search for more than a decade. 

Implementing a knowledge graph involves identifying key entities relevant to your business, defining their attributes and relationships, and managing this structured data in a system that keeps it up-to-date and accessible to various AI and search platforms. While you don’t have to use a knowledge graph design, the point is that you do need to incorporate a culture of structured data across your organization that identifies, stores, updates, and shares the critical knowledge every customer needs to know about your brand. 

There is a strong correlation between synchronized data and Google leading to your brand; in other words, sending the data ecosystem updates (like signals of life) has a statistically significant impact on clicks.

Brands must recognize that their AI strategy is fundamentally their data strategy. Emerging AI systems rely on data consistency across platforms, both in training and in “grounding”—which, in the context of LLMs, refers to anchoring AI-generated responses in factual, up-to-date information. This is where knowledge graphs become critical: They provide a structured, comprehensive source of accurate brand information that AI systems can rely on to ground their responses. 

Along with data consistency, it’s also critical to get your brand data everywhere and update it frequently for Google engagement. All initial indications and testing of Gemini show that if you can continue to power great search experiences, those are likely to be leveraged by AI systems. By maintaining a consistent presence, you’re improving your visibility in traditional search and positioning your brand to be a reliable source of information for AI-powered search experiences. 

Today, brands can enter their data into one knowledge graph or database and have it update from a few to several hundred platforms in real-time. This is the fastest way to ensure not only update frequency, but also consistency across the entire data landscape where consumers engage with Search and AI. 

The key to all of these strategies is to view your data as a dynamic, living asset that needs constant care and distribution. By organizing your data in a structured manner and ensuring its wide distribution, you’re preparing your brand to thrive in an increasingly fragmented and AI-driven search landscape. This proactive approach not only makes your information more accessible and manageable internally, but also positions your brand to meet consumers effectively at every possible digital touch point.