2.5 billion gigabytes (quintillion bytes) of data are created every day, with 80-90% of data classified as “unstructured”. This article examines how graph technology can help the data analytics industry solve this growing “data deluge” problem to find efficient and reliable insights.
Tech titans like Google, Facebook, and LinkedIn have long harnessed the power of graphical data models to understand patterns and connections in their data. This information was used to improve web searches and better understand user behavior.
Today, graphics and graphics processing have become ubiquitous in many other industry verticals and are being applied to find innovative solutions to new problems.
One example is the financial services industry, which is a huge growth area for graphics technologies. Analytics firm Gartner predicts that banks and investment firms will spend $ 623 billion on technology products and services in 2022. Digital fraud attacks against financial services firms increased 149% in the first four months of 2021, compared to the previous four months, for example, and have become one of the most profitable scams for scammers. To combat this problem, interaction charts are used to delve into the complex interrelationships between customers, accounts and transactions, improving fraud detection. Likewise, interaction charts can be constructed and analyzed to prevent money laundering by looking for anomalous patterns of transactions.
Another example is the pharmaceutical industry’s drug discovery space, which has received additional attention in light of the global COVID-19 pandemic. Graph technology can analyze a variety of medical knowledge data on drugs, treatments, outcomes and patients and perform “hypothesis generation” to determine promising treatments for particular diseases. Equally important, this technology can be used to rule out proposed treatments for diseases as well. This allows scientists to reduce the number of expensive and time-consuming wet lab experiments they have to perform to discover treatments for diseases.
In addition to accelerating the drug discovery process (which can cost an average of over $ 1 billion and last 12 years or more), graphics technology is also an integral part of the emerging field of precision medicine, moving away from a one-for-all. “. “Approach in medicinal treatment to a personalized therapeutic approach in which data on a single patient are used to find targeted treatments for that patient. This allows us to build a more personalized approach to medicine.
Graph 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 to fringe areas, such as space exploration, oncology, and even the deciphering of ancient languages!
Despite graph computing’s ability to provide data intelligence at speed and scale, there are two obstacles that have limited its widespread adoption: the lack of understanding of its capabilities and the difficulty many graphing platforms have had in interacting with third-party libraries and other systems in the data processing pipelines. These hurdles are now being addressed by chart vendors.
As the amount of data continues to increase and organizations continue to struggle with managing unstructured data, organizations must find new and innovative approaches to use this information to extract timely information. Graph technology is a key part of the overall solution, and graph systems, along with other analytics technologies, will enable organizations to gain insight from the huge amount of data they already have.
This is the first in a two-part series. In the next article, Keshav Pingali will explain what best practices systems developers should follow to take advantage of graph technology.
Keshav Pingali is the CEO and co-founder of Katana Graph, an AI-powered graphics intelligence platform that provides insights into huge and complex data. Keshav holds the WA “Tex” Moncrief Chair of Computer Science at the University of Texas at Austin and is a member of ACM, IEEE and AAAS.