Data management key to AI growth says IoT Analytics report
New research from IoT Analytics underscores the paramount importance of robust data management for the development of Artificial Intelligence (AI) models. The insights derived from the comprehensive “Data Management and Analytics Market Report 2024-2030” highlight seven critical components that are essential for building effective AI systems. As AI propels substantial growth in the data management market, which is anticipated to reach USD $513.3 billion by the year 2030, the role of these components becomes increasingly crucial.
The report reveals that AI relies on seven key elements of data management. These elements include data sources, ingestion, storage, transformation, analytics, governance and security, and orchestration. Each of these components plays a significant role in ensuring the seamless operation and efficiency of AI models. According to the research, hyperscalers such as AWS, Microsoft, and Google currently command over 50% of the market share. Despite their dominance, several emergent data management vendors are gaining recognition for their superior offerings and are attracting a robust clientele.
Knud Lasse Lueth, CEO at IoT Analytics, commented on the findings, stating: “Hyperscalers like AWS, Microsoft, and Google dominate the data management market with highly integrated portfolios across all major market segments. There are also some quickly growing data management scale-ups that are regarded as having a best-in-class offering and are subsequently enjoying strong market traction. It will be interesting to see whether companies will opt for the convenience of having everything from one vendor or settle on three to five main data management solutions on top of their cloud architecture.”
Oktay Demir, COO at IoT Analytics, also weighed in, underscoring the critical yet often underestimated role of data management in AI development. “C-level executives often overlook the critical importance of data management for AI. Strong data management is the foundation for successful AI implementation,” Demir noted. He further emphasised that while AI’s transformative power brings significant prestige to corporate leadership, the underlying data management strategy, which is fundamental to AI’s success, is frequently neglected.
Additionally, Mohammad Hasan, an analyst at IoT Analytics, shared his perspective on the evolving data management landscape. He remarked, “In my opinion, data fabric is still not very popular in terms of adoption as it can come with a heavy price tag due to unfit data architectures. However, given the increase in data complexity because of the exponential growth in big data, propelled by hybrid cloud, AI, IoT, and edge computing, there seems to be a good opportunity for vendors in this scenario.”
