The exponential growth of data, driven by IoT, cloud computing, and artificial intelligence (AI) advancements, is transforming how businesses operate and make decisions. Against this backdrop, the total data analytics market is forecasted to be worth $190 billion in 2028, implying an 11.1% compound annual growth rate (CAGR) between 2023 and 2028. With data volumes projected to exceed 175 zettabytes by 2025, organizations must leverage advanced analytics tools to extract actionable insights, says GlobalData, a leading data and analytics company.
GlobalData’s latest Strategic Intelligence report, “Data Analytics,” reveals that data analytics is a relatively mature market, yet significant innovation has occurred in recent years across the four layers of its value chain: hardware, data management, applications, and delivery.
Isabel Al-Dhahir, Principal Analyst, Strategic Intelligence at GlobalData, comments: “The traditional data analytics vendors are being disrupted by AI-native vendors that aim to help companies automate operational decision-making using machine learning. Furthermore, the emergence of generative AI (GenAI) 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.”
The rapid growth of data volumes and the expectation of advanced analytics require exemplary management, governance, and security. Data governance is all about data quality and reliability; good, trustworthy data is essential to data analytics. While data governance is augmented by technology, it is fundamentally about individuals and companies implementing rigorous protective policies and procedures.
Al-Dhahir continues: “The ability of GenAI to create highly sophisticated models and simulations from vast datasets raises significant concerns about the potential misuse of personal information. 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.”
Chief information officers, chief data officers, and chief analytics officers are critical to successful data analytics strategies within companies. Managing data analytics pipelines requires skilled workers, including data scientists, data engineers, and other data specialists. Similarly, top-notch data analysis is nothing without visualization, reporting, and communication. As the volumes of data continue to grow and data analytics projects are rolled out in organizations, the need for employees with diverse skills will only grow.
Al-Dhahir concludes: “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. 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. As the market evolves, innovation across data management, AI integration, and governance will shape the future of data-driven strategies.”
