In today's digital era, enterprises are generating massive volumes of data. Research estimates that 328.77 million terabytes of data are created every day. All this data can be used to gain insights into customer behavior, improve operational efficiency, and develop new products and services. Forecasts predict that the global big data analytics market will increase from its 2023 valuation of US$307.52 billion to an astounding US$745.15 billion by 2030. The data explosion is fueled by digital interactions, internet of things (IoT) devices, and cloud platforms. By 2025, a staggering 55.7 billion IoT devices will be interconnected, generating a colossal volume of nearly 80 zettabytes of data.
However, managing and analyzing large volumes of data can be challenging. The fusion of data moving to the cloud and generative AI can be leveraged to automate processes, personalize individual customer interactions, provide real-time insights for faster decision-making, and help accelerate business growth for enterprises.
How Data Cloud and Generative AI Work Together
The data cloud's network of data repositories offers diverse, high-quality data that enhances the effectiveness of generative AI. It provides varied contexts for AI models, enabling them to generate content across domains. The data cloud's vast storage ensures sufficient data for robust training, and curated data improves model accuracy for large language model processing. The ecosystem supports ongoing learning, allowing AI models to evolve, processing large datasets to uncover hidden patterns resulting in improved decision-making across product development, marketing, and customer service.
The mutual reinforcement between the data cloud and generative AI sets in motion a virtuous cycle of innovation. As the data cloud continually refines generative AI models, AI's improved capabilities lead to more precise analysis, prediction, and creative generation within the data cloud. The heightened performance sparks new ideas, solutions, and applications, which are then fed back into the cycle for further refinement. The iterative process of enhancement fosters an exponential growth of capabilities and solutions, resulting in a transformative impact across industries.
Generative AI plays a pivotal role in enhancing both product design and manufacturing processes. It is achieved by leveraging data derived from an amalgamation of sensors, simulations, and historical records stored within the data cloud. Generative AI models generate and evaluate various design possibilities, thereby catalyzing a more resource-efficient and pioneering approach to product development. Pharmaceutical companies have been using AI to accelerate drug discovery for a while. In 2019, Insilico Medicine, a biotechnology firm, harnessed AI to create a molecule that targets a protein linked to fibrosis. This feat was accomplished in just 46 days. In June 2023, the company started Phase 2 clinical trials in humans for a drug discovered and designed by generative AI. It marks a groundbreaking moment in the pharmaceutical sector, as drug discovery becomes swifter, cheaper, and better than before.
How Source Data Quality Matters for Generative AI
One of the biggest concerns about data cloud and generative AI is the quality of the data at source. If the data is not accurate or reliable, then the insights and output generated by generative AI will be inaccurate and unreliable as well. As per research findings, 54% of the participants are concerned about the potential inaccuracy of output from generative AI, while 59% are apprehensive about potential biases in the output. Also, 60% are uncertain about effectively using reliable data sources and ensuring the security of sensitive data, and the majority of leaders acknowledge data readiness is a top challenge to accelerate adoption of AI.
It is important to have a strong data quality process in place before using data cloud and generative AI. The process must include steps to clean, validate, and enrich the data before it is used. Another concern is that generative AI can be used to create fake or misleading content. The content could be used to deceive people or to spread misinformation. One must be aware of this risk and take steps to mitigate it. It could mean using generative AI in a transparent way and providing clear disclaimers about the nature of the content. In a recent CXO pulse survey of 2,300 executives, 93% surveyed support some form of government regulation on generative AI.
How Interdependencies Between Data Cloud and Generative AI Can Impact the Future
The potential of developing integrated cloud solutions that capitalize on the interdependencies between the data cloud and generative AI becomes immense. One of the emerging trends and research areas is federated generative AI. Federated learning techniques are being extended to generative AI, enabling models to be trained collaboratively across different data sources without sharing raw data. The extension is particularly valuable for industries dealing with sensitive data, such as healthcare and financial services.
Another promising trend is the integration of multi-modal generative AI, which involves combining generative AI with multiple modalities, such as text, image, audio, and more, to generate rich and coherent content across various domains, leading to more immersive and creative applications. Advancements in real-time and interactive, generative AI allow users to interact dynamically and responsively with models. The capability finds applications in virtual avatars, gaming, and conversational agents. Industries such as design, architecture, and fashion are exploring the uses of generative AI in creativity and design. Collaborative generative AI models are also being developed to facilitate teamwork in content creation.
The interdependencies between the data cloud and generative AI exemplify the extraordinary possibilities that arise when the transformative forces of technology unite. The integration enables AI-generated content and insights to be delivered directly at the edge, reducing latency and enabling real-time applications.
The fusion of data-driven insights and advanced AI capabilities heralds a new era of possibilities, where industries are propelled into uncharted territories of creativity, efficiency, and transformation. By synergizing these technologies, businesses can revolutionize their operations, gain a competitive edge, and pioneer innovative solutions in an increasingly data-driven world. As tech leaders, we stand at the threshold of a new era, one where data and AI converge to reshape industries, redefine innovation, and elevate human potential to unprecedented heights. The journey ahead is paved with opportunities for those who dare to embrace the synergy between the data cloud and generative AI.
Hiral Chandrana is CEO of Mastek Group.