“And the thing is, it’s about leverage. You can leverage social data. You can leverage Twitter data. You don’t have to start from scratch.” There is already a wealth of information to be tapped into. All that’s needed is a dynamic way to synthesize and apply it.

According to Max De Marzi, software field engineer with Neo Technology, graph searches offer real value to the enterprise by making practical use of big data. There are insights and patterns to be uncovered simply by being able to see how things are connected. De Marzi uses Neo4j, Neo Technology’s graph database, and Cypher, the Neo4j query language, to build proof of concepts for professional customers. In other words, he demonstrates how this technology can be applied and what business problems it could solve.

His more recent proof of concept was inspired by Facebook’s Graph Search, a search engine providing user-specific search results based on “natural language” queries. At this year’s Big Data TechCon, de Marzi will teach a class on how to build your own Facebook Graph Search using your own big data.

Putting big data to work

Industries that most directly benefit from big data are customer-facing ones — namely, retail. “I logged in to the retailer website and it asked me what my general interests were, and then that’s as far as they go,” De Marzi said. He went on to postulate that if it had access to data on his family, his kids and his wife, and if it had identified their ages and the things they liked, the website would have been able to precisely target products that might interest him.

“And the thing is, it’s about leverage. You can leverage social data. You can leverage Twitter data. You don’t have to start from scratch.”  There is already a wealth of information to be tapped into. All that’s needed is a dynamic way to synthesize and apply it.

While retail is the most obvious beneficiary of graph searches, De Marzi cited use cases from a wide variety of industries. For example, banks can use big databases to protect themselves from fraud by detecting suspicious connections. “If some credit card is being used by 20 different people, it may be a fraudulent card,” De Marzi explained.

More sophisticated scams, such as fraud rings, rely on burying data under layers of misdirection. Traditional methods would process this information as individual data points and would miss their incriminating interrelatedness — such as 20 people using the same card.

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