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The financial institution is seeking to patent a system for "managed data services" on cloud platforms, helping them declutter in a way Kondo would appreciate. This filing details a number of ways that Goldman's system aims to sift through Wall Street's data stockpile, but the goal of it is to gather, clean and organize data in real-time, and provide actionable analysis with little to no work on the client's part.
The focus of this patent is specifically "timeseries data," or data that is recorded consistently over time, "often at high rates of ingestion," for example, price fluctuations of a material or stock price data.
Basically, data is replicated across the "nodes" of this system, each of which handles a "microservice." These microservices can be anything from data intake to answering client questions. All of these small services come together to handle requests with low latency, even during usage peaks, because each node has consistent access to all of the data it needs to do its individual job. Goldman noted that this system can be set up on a company's proprietary cloud platform, or one that they're renting from (aka AWS, Azure, etc.).
While this all may sound abstract, the outcome is clean, easy-to-access data that can be used for a number of business processes, including answering queries, generating reports and analysis "presented in real-time, down to nanosecond granularity," that can inform decisions.
Investment banks and financial firms have a constant and growing hunger for market data – and that hunger comes at a steep price.
Demand for financial data has bubbled over the past few years, with the London Stock Exchange Group's $27 billion acquisition of Refinitiv in 2019 being an early signal of a craze to come. Since then, it's only gotten more pronounced: According to an April report from Burton-Taylor International Consulting, global spending on financial data reached a record $37.3 billion in 2022.
But actually putting this pricey cache of data to use is a time suck, Goldman said in its filing. Firms often spend "an inordinate amount of time is spent finding, cleaning and organizing or maintaining data feeds, such as up to 80%, leaving little time to spend performing data analysis." By securing a patent on a system that can do all the tedious work for you, Goldman could lock down a lucrative market.
Goldman isn't the only company interested in making data easier to understand. Rival JPMorgan Chase is seeking to patent no-code machine learning that basically eats client data and pops-out a ready-to-use AI model for business intelligence.
While the companies' methods are vastly different, the goal remains the same: To straighten out data so that their clients – and in-turn, themselves – can more quickly turn their money into more money.
