Graph analytics is a crucial element in extracting insights from Big Data because it helps discover hidden relationships by connecting the dots. A graph, meaning the network of nodes and relationships, treats the linkage between objects as equally important as the objects themselves. Social networks or supply chains are obvious examples, but graphs include any network of objects such as customers, products, purchase orders, customer support calls, product inventory, etc. HiperGraph, PARC’s breakthrough Big Data technology, is a high-performance graph analytics engine. Through a four-month research project with SAP, we added HiperGraph’s analytics to SAP HANA to demonstrate a live, real-time marketing insights use case. Graph reasoning technologies provide the ability to contextualize relational data with the tapestry of information and can go beyond simplistic reporting and dashboards. This creates opportunities to rapidly experiment, gain new insights, and identify root causes. The demonstrated technology match between HANA and HiperGraph has great disruptive potential, especially in the identification of key patterns within datasets (e.g., via clustering). With HANA and HiperGraph we can finally deliver on the promise of a closed feedback loop in the enterprise where transactions are analyzed and reacted to in real-time. The intelligence that is implicit in large volumes of structured and unstructured data from varieties of sources from inside or outside of the enterprise can be delivered to the users in the form of smart business applications. We concluded that the existing commercial or open source algorithms either did not provide the real-time response or were unable to scale to the large volumes of data. The requirements from our customer (an online retailer) required real-time response from their Big Data system. PARC’s graph reasoning, versatile goal-directed clustering, egocentric recommendations, and real-time recommendation algorithms combined with the power of HANA in-memory technologies far exceeded the expectations. Brand managers can use this solution to automatically find clusters of customers with similar purchases, clusters of products that are frequently bought together, clusters of products that tend to be purchased on sale vs. those that are purchased at full price, and so on, and act on these insights during the customer’s shopping experience. There is a great opportunity for businesses to gain value by combining the HANA in-memory technology with HiperGraph reasoning, recommendation, matrix factorization, egocentric collaborative filtering, and versatile goal-directed clustering. With SAP and PARC co-innovation in Big Data analytics we can now reduce and/or eliminate the need for complex extract, transform, and load (ETL) processes; increase speed in clustering; and introduce new accessibility for business users to directly explore data clusters. We are democratizing data science for all business users in the enterprise. PARC and SAP Co-innovation: High-performance Graph Analytics for Big Data Powered by SAP HANA