The global data analytics market is growing fast, driven by advancements in IoT, cloud computing, and AI, according to new research from GlobalData.
With data volumes projected to exceed 175 zettabytes by 2025, the total data analytics market is set to grow at a compound annual growth rate (CAGR) of 11.1% between 2023 and 2028, hitting $190 billion in 2028.
While the data analytics space is a relatively mature market, it has seen significant innovation in recent years across the four layers of its value chain - hardware, data management, applications, and delivery.
Prescriptive analytics is the most advanced innovation, according to GlobalData, telling organizations what to do next rather than just describing what has happened and why.
Machine learning techniques can now provide data-driven recommendations by parsing large amounts of data and assessing 'what if' scenarios.
The traditional data analytics vendors such as SAS, IBM, Oracle, and SAP are being disrupted by AI-native vendors, such as Cognitive Scale and H2O.ai, which aim to help companies automate operational decision-making using ML.
Similarly, the emergence of generative AI tools has led data analytics vendors to embed solutions in their platforms. Microsoft's launch of Copilot, for example, embedding ChatGPT into analytics products such as Excel and PowerBI, represents a step change for enterprise capabilities.
"The traditional data analytics vendors are being disrupted by AI-native vendors that aim to help companies automate operational decision-making using machine learning," said Isabel Al-Dhahir, GlobalData principal analyst, strategic intelligence.
"Furthermore, the emergence of generative AI tools has led data analytics vendors to embed those solutions in their platforms, democratizing access to data science capabilities. For instance, Microsoft has launched Copilot, embedding ChatGPT into analytics products such as Excel and PowerBI."
Governance, security, and skills are key concerns
The report warned that the rapid growth of data volumes and the expectation of advanced analytics brings great pressure in terms of management, governance, and security.
"The ability of GenAI to create highly sophisticated models and simulations from vast datasets raises significant concerns about the potential misuse of personal information," said Al-Dhahir.
"The risk of exposing sensitive data increases as these AI systems become more adept at generating detailed, realistic outputs. This calls for stringent data governance frameworks."
Managing data analytics pipelines means a need for more skilled workers, including data scientists, data engineers, and other data specialists. Meanwhile, data analysis requires skills in visualization, reporting, and communication.
"Organizations must attract, train, retain, and upskill their current workforce to fill these roles. Companies may also attempt to plug the data-skills gap by encouraging less technical employees to act as citizen data scientists," said Al-Dhahir.
"Businesses must build up their in-house capabilities, but in some cases, strategic acquisitions or partnerships with small data analytics service companies may be preferable," Al-Dhahir added.
"As the market evolves, innovation across data management, AI integration, and governance will shape the future of data-driven strategies."
