Data, Graphs and AI
In the worlds of data management and AI, it's time to embrace a more integrated perspective: AI needs data, and just as crucially, data needs AI. This symbiotic relationship underpins a shift in how we should approach our strategies, moving away from viewing AI and data management as two separate operations and starting to think about them as two sides of the same coin.
In an enterprise context, AI models unlock their true potential not merely through the vast quantities of information they process, but when that information is relevant, connected, cleaned, structured, and enriched with semantics—all tasks that lie at the heart of data management expertise. Yet, the reverse is equally true: our data strategies gain direction and sophistication when guided by the insights and capabilities AI brings to the table. Moreover, AI can help you connect, clean, structure, and enrich your data with semantic metadata.
The synergy between Large Language Models like ChatGPT and Knowledge Graphs is a testament to this interdependence. Knowledge Graphs have travelled through the Gartner hype cycle twice, once on the data side and once on the AI side, but really Knowledge Graphs are just one thing: a way of structuring your data that makes it ready for AI.
Our business differentiation and competitive edge lie not in the data or AI capabilities alone but in our proficiency in merging the two harmoniously. The challenge of "garbage in, garbage out" transforms under this lens, reminding us that disorganised data not only hampers AI's effectiveness but also that AI can play a pivotal role in connecting and cleansing data.
Knowledge Graphs sit at this nexus; they offer a disciplined way of structuring your data so that your AI models can use it, and in this sense, they are a way to bring the two worlds closer together.
