There’s no question that this year, we’re seeing dramatic upheavals in the data and analytics space. AI, of course, is the subject du jour, but any discussion of AI and its potential must, by necessity, involve the data teams that provide the information that makes AI successful. Here are what industry leaders and experts see as the most important technologies shaping the growth of data-driven enterprises.
Generative AI—as seen with models such as ChatGPT and GPT—opens up new worlds for data teams and their enterprises. This democratized AI “is creating numerous opportunities for enterprises to derive insights from unstructured, distributed, or isolated data,” said Harshul Asnani, global head of the technology business at Tech Mahindra. “Generative AI has an ability to analyze unstructured data by using a combination of natural language processing, computer vision, and deep learning, along with distributed computing capabilities. It has the potential to unlock the value of this untapped data by providing powerful tools for analyzing and deriving insights from unstructured data.”
Progress: Generative AI is still in the early stages of adoption by enterprises. At this point, both technology providers and enterprises are in the process of building use cases, said Asnani.
Potential roadblocks: Generative AI requires “specialized skills and expertise in machine learning, data science, and related fields,” Asnani cautioned. “Additionally, there are concerns around the ethical use of AI and the need for responsible AI governance frameworks to ensure that the technology is used in a way that is fair, transparent, and accountable.”
In addition, data managers need to significantly step up capacity. AI software, tools, and platforms “require a massive amount of data storage capabilities, which will need to be continuously scaled to meet the demand for integrating and leveraging these tools,” said Jon Pratt, CIO at 11:11 Systems. “This will have a big impact on how data is stored in the cloud, requiring cloud infrastructures to expand capacity and capabilities to accommodate the continuing AI boom.”
Business benefits: Generative AI potentially will deliver “transformative enhancements in creativity and innovation, improved efficiency and productivity, personalized products and services, improved decision making, and enhanced customer experiences,” said Asnani. In time, it promises to “deliver and empower decision makers with insights on demand, allowing them to make faster and more informed business decisions, drive meaningful innovation, increase competitiveness, and ultimately contribute to make the world a better place.”
A shift away from the centralized model of data center or cloud-based data infrastructure and to the edges has profound implications for data management. “The world, believe it or not, exists at the edge and not in a central cloud in northern Virginia,” said Joe Reele, VP of solution architects at Schneider Electric. “We live in homes and apartments; we work in hospitals, factories, schools, buildings in cities and towns; we shop with retailers, grocers, and so on. All of these places are packed full of networks, data, and now, applications. Most end devices—sensors, circuit breakers, smart patient walls in hospitals, industrial robots—across industries are smart and connectable.”
As a result of the proliferation of computing and data at the edge, Reele continued, “there are huge changes happening in data prioritization, segmentation, and traffic flow across the industry.” There’s pressure to “determine what data is important and what is not. And then where the data is needed enters the picture as well. Some data is critical locally but wouldn’t be as useful for big data analytics. Organizations must manage what data can be used locally and what needs to be sent out to the cloud. Telco providers, data center owners, and hyperscalers are all seeing a need to go further out to the edge.”
Progress: “The global edge computing market is facing massive growth, growing at 12.5% annually to an estimated $250.6 billion in 2024,” Reele shared. “Continued upgrades to the fiber and network infrastructure, the birth of smart cities, and the evolution of AI and augmented reality will lead to the next killer applications.”
Potential roadblocks: The growth of digital infrastructure “is being outpaced by technology innovation and demands for use and scalability,” said Reele. “A recent example is the incredible demand for AI applications like ChatGPT and the power and cooling needs that are required to support them. The time and resources needed to meet today’s demand are a significant roadblock to the implementation of edge-computing technology.”
Business benefits: The ongoing move to the edge means “lower latency, improved data privacy, and reduced bandwidth demand on the larger network,” said Reele. “These changes make it possible to process larger volumes of data.”