New research from IoT Analytics highlights the critical importance of robust data management for the development of artificial intelligence (AI) models. Insights derived from the “Comprehensive Data Management and Analytics Market Report 2024-2030” highlight seven critical components that are essential for creating effective AI systems. As AI propels substantial growth in the data management market, which is expected to reach $513.3 billion by 2030, the role of these components is becoming 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 an important role in ensuring that AI models run smoothly and efficiently. According to the study, hyperscalers such as AWS, Microsoft and Google currently hold more than 50% market share. Despite their dominance, several emerging data management vendors are gaining recognition for their superior offerings and attracting a strong customer base.
Knud Lasse Lueth, CEO of IoT Analytics, commented on the findings, saying: “Hyperscalers like AWS, Microsoft and Google dominate the data management market with highly integrated portfolios across all major market segments. ups which are considered to offer the best deal and subsequently enjoy strong traction in the market. It will be interesting to see whether companies will opt for the convenience of having everything from one vendor or choose three to five core data management solutions. on top of their cloud architecture.”
Oktay Demir, COO at IoT Analytics, also spoke, highlighting the critical but often underestimated role of data management in AI development. “Executives often overlook the critical importance of data management for AI. Strong data management is the basis for successful AI implementation,” Demir noted. He further highlighted that while the transformative power of AI brings considerable prestige to business leaders, the underlying data management strategy, which is fundamental to AI’s success, is often overlooked.
Additionally, Mohammad Hasan, Analyst at IoT Analytics, shared his views on the evolving data management landscape. He remarked: “In my opinion, Data Fabric is still not very popular in terms of adoption because it can be expensive due to unsuitable data architectures. However, considering the increase in data complexity due to the exponential growth of Big Data. , powered by hybrid cloud, AI, IoT and edge computing, there appears to be a good opportunity for providers in this scenario.
