Data has taken a new position in the spotlight as the most important part of using AI. If the organization is using corrupt data, insights will vary wildly, and misinformation can damage the company’s reputation. Poor data quality costs organizations at least $12.9 million per year on average, according to Gartner research from 2020.
Data quality is crucial to an organization because it ensures accurate, reliable, and timely information, which supports informed decision making and enhances operational efficiency.
“Nearly everything today—from the way we work to how we make decisions—is directly or indirectly influenced by AI. But it doesn’t deliver value on its own—AI needs to be tightly aligned with data, analytics, and governance to enable intelligent, adaptive decisions and actions across the organization,” said Carlie Idoine, VP analyst at Gartner.
According to Research and Markets, the big data and analytics services market size is expected to see exponential growth in the next few years. It will grow to $365.42 billion in 2029 at a compound annual growth rate (CAGR) of 21.3%.
The growth in the forecast period can be attributed to advanced analytics and AI, data privacy and security, cloud-based analytics, and real-time analytics. Research and Markets predicts trends during this time will include AI and machine learning integration, cloud-based big data services, edge analytics, data privacy, and compliance.
A recent Unisphere Research survey, “2025 Market Study: Modern Data Architecture in the AI Era,” explored how the evolving landscape of enterprise data architecture has shifted in the past 2 years with the arrival of AI capabilities, fundamentally changing how organizations evaluate, justify, and implement their data strategies.
The survey shows that modern data architecture strategic positioning has undergone a dramatic shake up, with real-time analytics declining from the 2023 leadership position of 50.0% to 32.0%, while cloud data warehousing maintains relative stability, decreasing from 44.0% to 36.7%.
Strategic commitment patterns now clearly separate AI-enabling architectures from standalone technologies, with data lakehouse at 33.6% and data fabric at 28.2%, holding stronger future value positions than data streaming platforms at 22.0% or data mesh at 23.2%.
Multi-cloud adoption has only intensified, with only 6.2% of organizations planning on-prem-only operations, indicating that cloud platform decisions have evolved into complex portfolio choices rather than single-vendor commitments, according to the survey.
Generative AI (GenAI) demonstrates strong market acceptance, with 39.0% of organizations actively involved and embracing clear business value recognition. The survey shows GenAI is establishing itself as the organizing principle that influences how organizations evaluate and prioritize other architectural investments.
Other trends such as streaming data platforms and data lakehouses show healthy progression from conceptual understanding to detailed implementation expertise, with organizations moving beyond basic infrastructure concerns toward sophisticated architectural integration challenges.
Emerging approaches such as the data fabric are experiencing market recalibration as initial enthusiasm encounters implementation complexity, while semantic layer trends remain in active evaluation phases despite clear potential for enabling AI initiatives.
Security and compliance are as important as ever, and organizations are taking it seriously, as a recent IBM security report shows companies utilizing AI in security initiatives are saving $1.9M compared to organizations that aren’t using these solutions.
According to the “IBM 2025 Cost of a Data Breach Report,” the global average cost of a data breach in 2025 amounted to $4.4 million, a 9% decrease from last year which was driven by faster identification and containment. IBM recommends investing in integrated security and governance solutions that allow organizations to gain visibility into all AI deployments (including shadow AI), mitigate vulnerabilities, protect prompts and data, and use observability tools to improve compliance and detect anomalies.
To support organizations in navigating through new challenges and a rapidly evolving big data ecosystem, Big Data Quarterly presents 2025’s “Big Data 75,” a list of companies driving innovation and expanding what is possible in terms of collecting, storing, and extracting value from data.
The list is broad, including some companies that are longtime industry leaders which continue to innovate, as well as others that are newer arrivals on the data management and analytics scene.