Every day, 2.5 billion gigabytes (quintillion bytes) of data are created – 80-90% of the data is classified as “unstructured”. This article looks at how graphics technology can help the data analytics industry solve this growing problem with a “flood of data” to find effective and reliable insights.
Technology titans like Google, Facebook and LinkedIn have long used the power of graphical data models to understand patterns and connections in their data. These insights have been used to improve web searches and better understand user behavior.
Today, graphs and graphical calculations have become ubiquitous in many more industrial verticals and are being used to find innovative solutions to new problems.
One example is the financial services industry, which is a huge growth area for graphics technologies. Analyst firm Gartner estimates that banks and investment firms will spend $ 623 billion on technology products and services in 2022. Digital fraud attacks on financial services companies increased 149% in the first four months of 2021 compared to the previous four months, for example, and have become one of the most lucrative scams for fraudsters. To combat this problem, interaction charts are used to develop complex relationships between customers, accounts and transactions, improving fraud detection. In a similar way, interaction graphs can be built and analyzed to prevent money laundering by looking for anomalous transaction models.
Another example is the drug discovery space in the pharmaceutical industry, which has received additional attention in light of the global COVID-19 pandemic. Graphic technology can analyze various data from medical knowledge about drugs, treatments, results and patients and perform “hypothesis generation” to identify promising treatments for certain diseases. It is equally important that this technology can be used to exclude proposed treatments for diseases. This allows scientists to reduce the number of expensive and time-consuming experiments in wet laboratories that they have to perform to find cures for diseases.
In addition to accelerating the drug discovery process (which can cost more than $ 1 billion on average and span 12 or more years), graphics technology is also an integral part of the emerging field of precision medicine, deviating from “one treatment is appropriate for all”. ”An approach in medical treatment to a personalized approach to treatment, which uses data for an individual patient, finds treatments aimed at that patient. This allows us to build a more personalized approach to medicine.
Graphic technology is still in its infancy in some industries, so its applications in areas such as financial fraud, precision medicine and information security are only scratching the surface of the technology’s potential. The technology can be applied in peripheral areas, such as space exploration, oncology and even decryption of ancient languages!
Despite the ability of graphics to provide intelligence for data at speed and scale, there are two obstacles that have limited its widespread adoption – a lack of understanding of its capabilities and the difficulty many graphics platforms have had in interacting with third-party libraries and other systems. data processing pipelines. These obstacles are now being addressed by graphics providers.
As the amount of data continues to increase – and as organizations continue to struggle with the management of unstructured data – organizations need to find new and innovative approaches to using this information to retrieve timely information. Graphics technology is a key part of the overall solution, and graphics systems, in conjunction with other analytics technologies, will allow organizations to unlock deep insights from the vast amount of data they already have.
This is the first of two series of parts. In the next article, Keshav Pingali will explain what best practices systems developers need to follow to use graphics technology.
Keshav Pingali is the CEO and co-founder of Katana Graph, an AI-powered graphics intelligence platform that provides insights into massive and complex data. Keshav holds the WA “Tex” Moncrief Chair in Computer Science at the University of Texas at Austin and is a Fellow of the ACM, IEEE and AAAS.