Semantic meaning

semBy Angela Guess

contributor Tom Endyke recently wrote, “While organizations realize the potential of effectively incorporating data into multiple facets of their business, many are unsure as to how to best take action upon this insight. Consider the amount of data generated on an on-going basis in the financial services space alone; there is transaction data, consumer data, market data, regulatory data—the list goes on. The volume of information alone can be staggering and many organizations may not even have the appropriate tools to access all of their data. This is where semantic technology comes into play. At the highest level, semantic technology gives meaning and context to both structured and unstructured data, and makes it actionable, thereby solving major challenges financial institutions are facing when it comes to realizing the full value of their data.”

Endyke goes on, “At the heart of semantic technology are graph databases, also known as triplestores. Triplestores are made up of triples, or pieces of information stored in the format of subject-predicate-object. For example, ‘Bank of America is a company’ or ‘Jim is a person.’ In this way, triples can be used to describe just about anything and can also infer relationships between people, places, events, organizations and other entities. There are three areas of financial services on which semantic technology is having a significant impact.”

Endyke explains that the first area is customer experience: “When you consider all of the different sources of customer data (and much of it siloed) it becomes easy to see how difficult it can be to integrate all of that information into one standardized format to arrive at a complete picture from which to act upon. The traditional process for doing this, Extract, Transform and Load (ETL), is costly, time-consuming and doesn’t work well with non-relational data. However, semantic technology addresses this issue by quickly and easily bringing together both structured and unstructured customer data, including demographic information, transactional data, online data, call center transcripts, major life events, social media and more.”

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