.png){kind=logo}
In a World Economic Forum piece, tech magnate and philanthropist Bill Gates cited AI as one of only two revolutionary demonstrations of technology he witnessed. The rapid progress of AI development and the unprecedented capabilities it showcased awed the tech visionary.
Gates fondly recalled how he gave the OpenAI team the challenge of creating an AI system capable of passing an Advanced Placement biology examination and answering questions it was not specifically trained for. He thought the team would be busy working on it for a couple of years or so. To his surprise, the team came up with the system in just a matter of months.
How did this rapid advancement of AI technology happen? There are many factors involved, from the faster processing power of modern computers to the bigger funding and collaboration among AI experts. However, the biggest reason is arguably the abundance of readily available data.
It is easier to train AI systems now because of the vast amounts of data shared online. The years of analog content digitization and the continuous production of digital information have accumulated enormous volumes of data in various languages across different disciplines. The OpenAI team and other AI development groups are enjoying the fruits of data because they know how to make sense of it.
One of the tools AI developers employ to harness big data is called data fabric. This modern data architecture enables the better management of data in different forms and from various sources. It provides a scalable, flexible, and unified way to manage data to make the most out of massive volumes of data created in different ways and stored in various formats.
The concept of data fabric is not exactly new. It started in the earlier noughties out of the need for an efficient way to handle the growing complexity of managing data in large organizations. Gartner coined the term in 2002 to provide an appropriate term for a new data architecture that supports diverse data integration and unified management. Eventually, data fabric advanced into becoming a data management approach that is not only concerned with integration but also the evaluation of data quality, analytics, and security.
The security aspect is particularly important in light of the rapid changes in the cyber threat landscape and the persistent ingeniousness and aggressiveness of threat actors. Data fabric security is a crucial part of making sure that data is properly protected and the resulting work is similarly secure.
It can be said that data fabric and AI share a symbiotic relationship. On one hand, data fabric provides the infrastructure to support AI development. On the other, AI enables organizations to thoroughly analyze huge amounts of data and extract useful insights.
As a data architecture and management approach, data fabric is suitable for machine learning (ML) and building artificial intelligence. It effectively consolidates data in various forms and from different sources to be useful in efficiently training ML systems. It provides centralized data governance, ensures data quality, and supplies apt tools for data analysis and visualization. It does not only help train machines; it also plays a crucial role in refining artificial intelligence algorithms.
Correspondingly, AI helps establish useful data fabric by identifying patterns, anomalies, and correlations in data automatically, continuously, and meticulously. AI tools can be employed to automate data analysis and ensure the accuracy and integrity of the huge amounts of data an organization maintains. It ensures that all details are taken into account in coming up with insights. It addresses the flaws of conventional data analysis, especially the tendency of human analysts to miss some details that have a significant impact on decision-making.
Predictive analytics, natural language processing, computer vision, and other advanced forms of artificial intelligence can leverage data fabric to learn more quickly and develop autonomous decision-making capabilities. This is possible because data fabric addresses the most significant flaws in big data management, including the lack of organization, inconsistencies, incompatibility, inaccuracies, and incompleteness.
Moreover, data fabric provides the following benefits to support the development of advanced AI.
Real-time data access – Advanced AI applications typically entail real-time operations, which would be impossible if data is not accessible in real-time. Data fabric allows AI algorithms to process data in real-time, regardless of the data's location.
Silo prevention – By providing a unified and comprehensive view of all data assets, data fabric addresses the emergence of silos, which counteracts the development of AI systems. Silos make machine learning slower, as they adversely affect data quality, consistency, and governance.
Agility and scalability – Data fabric is designed to be agile and scalable. It is not stuck to specific or fixed conditions that fail to take changing requirements into account. Also, it is built to easily scale up (and sometimes, down) in response to the varying needs of advanced AI applications.
Accuracy – Since data fabric entails a unified view of all data, it helps address data problems such as errors, inconsistency, and lack of context. As such, it ensures that machine learning algorithms are trained with the right and accurate data to achieve the expected outcomes as quickly as possible.
EY Global Consulting Data and Analytics Leader Beatriz Sanz Sáiz believes that data fabric helps companies keep up with others that have more resources and headway when it comes to AI development. "Data fabric also supports automatic machine learning, enabling business units to apply the data without having to become data specialists. In this way, it places AI and data in the hands of business leaders," Sáiz explains.
Creating advanced AI applications does not have to be exclusive to those that have been working on AI for a long time and have access to vast resources to pursue endless experimentation, tweaking, and refinement. With the democratization of data and analytics afforded by data fabric and the availability of AI analytics and generative tools, more organizations are enabled to innovate and create more advanced AI solutions.
Data fabric is crucial for organizations to unlock the full potential of their data. As it transforms the way organizations manage and take advantage of data, it also drives the development of advanced AI applications. Data fabric's real-time data access, silo prevention, agile and scalable nature, and inherent compulsion over accuracy create a suitable foundation for advanced AI application development initiatives.
