Think Bigger with Graph
2018 was the “Year of the Graph”, heralding advancements in graph products primarily addressing graph analytics use cases like fraud and community detection. This momentum continues in 2019, with thought leaders lauding the promise and benefits of graph and uniquely graph applications. At Cambridge Semantics, we challenge the world to think even bigger with graph. We believe, and have proven with our customers over the last decade, that the destiny and greatness of the graph data model sits in mainstream data management in support of line-of-business analytics. The killer app is integration…read on to find out more.
Arguably, adopters are sliding into graph technology and are beginning to realize they need more capacity to query any substantial amount of data in the graph. From an architecture perspective, many organizations feel they possess enough ETL processes and analytics tools. But adopters are realizing they lack the ability to develop meaningful enterprise scope applications using graph data models. In other words, organizations are slowly realizing they need to integrate information at scale, and awareness of the graph is driving interest as a means to create context.
Many thought the great promise was graph analytics, but that is not the true gold nugget. The goal of information technology is information on demand. Yet, we collectively have only managed to link documents – a la the World Wide Web. Information on demand involves question answering, but we still only derive fragmented subsets of answers to questions, and we rely on a high human touch to synthesize multiple query results to arrive at conclusions. This manually intensive process is inherently error prone, results in conjecture, missed opportunities and other deleterious outcomes. Why is it so hard to contextualize data from multiple sources and create more complete answers?
Large and broad scale data integration is the killer app for the graph data model. There are several useful graph analytic techniques and algorithms that answer certain well-suited questions. But these are not the killer applications for graph technology. Pivot your focus to imagine end users interacting with enterprise information as a service. On demand, users ask complex questions of the enterprise and the enterprise responds with all it knows in a business-oriented and more complete manner—with no human “stitching” of information. Imagine automated processes interacting with the enterprise to execute sophisticated analytics and workflows. We might call this artificial intelligence. It is through this completeness, richness and immediacy of information that graph will change the world.
Graph technology provides superior ability to process disparate and complex data, while incorporating semantics to add richness and context. Until recently, graph technology struggled to cope with the volume of data which constrained broad application of graph technology to more narrowly scoped analytics applications.
