Every day, 2.5 billion gigabytes (quintillion bytes) of data are generated, with 80-90% of the data classified as “unstructured”. This article examines whether graph technology can help the data analytics industry to solve this growing “data earth” problem in order to find effective and reliable information.
Technological titans like Google, Facebook, and LinkedIn have long harnessed the power of graphical data models to understand their data models and connections. This information has been used to improve web searches and better understand user behavior.
Nowadays, graphs and graph computing have become ubiquitous in more vertical industries, and are being applied to find innovative solutions to new problems.
An example is the financial services industry, which has a large growth area for graphics technology. Gartner analysts expect banks and investment firms to spend $ 623 billion on technology products and services in 2022. Attacks on digital fraud against financial services companies increased by 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 scammers. To address this issue, graphical interactions are used to deepen the complex relationships between customers, accounts, and transactions, improving the perception of fraud. Graphic interactions can also be constructed and analyzed to prevent money laundering by looking for anomalies in transaction patterns.
Another example is the pharmaceutical space discovery space, which has received additional attention in the wake of the global COVID-19 pandemic. Graphic technology can analyze a variety of knowledge data about drugs, treatments, outcomes, and patients, and can “create hypotheses” to determine promising treatments for specific diseases. This technology can also be used to rule out proposed treatments for diseases. This allows scientists to reduce the number of expensive and wet laboratory experiments they need to perform to find treatments for their disease.
In addition to speeding up the drug discovery process (which can cost more than $ 1 billion on average and last 12 years or more), graphical technology is also key to the emerging field of precision medicine, moving away from a “single treatment”. ”Approach to personalized treatment approach to personalized treatment in which data about an individual patient are used to find treatments aimed at that patient. This will allow us to build a more personalized approach to medicine.
Graphics technology is still in its infancy in some industries, so its applications in areas such as financial fraud, precision medicine and information security are violating the potential surface of the technology. Technology can be applied to marginal areas, such as space exploration, oncology, and even deciphering ancient languages!
Despite the ability of computing graphs to provide data intelligence at speed and scale, there are two barriers to its widespread acceptance: a lack of understanding of its capabilities and the difficulty of many graphical platforms interoperating with third parties. libraries and other systems in data processing pipelines. These barriers are now being addressed by graphics vendors.
As the number of data increases, and organizations continue to struggle to manage unstructured data, organizations need to find new and innovative approaches to using this information to extract timely information. Graphics technology is a key part of the overall solution, and graphics systems, along with other analytical technologies, will allow organizations to unlock in-depth insights from the large amounts of data they already have.
This is the first of two parts in a series. In the next article, Keshav Pingali will explain what best practices should be followed by system developers to take advantage of graphics technology.
Keshav Pingali Katana is the CEO and Founder of Graph, an AI-driven graphics intelligence platform that provides massive and complex data information. Keshav is a professor of computer science at WA “Tex” Moncrief at the University of Texas at the University of Austin, and is a member of the ACM, IEEE and AAAS.