Data analytics is one of the fastest-growing and most dynamic areas of technology, driven by the urgent need to process, integrate, and interpret massive volumes of diverse data. Organizations are increasingly investing in business intelligence, advanced data visualization, and AI-powered analytics platforms to unlock actionable insights, improve decision-making, automate workflows, and gain a competitive advantage across operations, marketing, finance, and product development.
According to Mordor Intelligence, the data analytics market reached $82.33 billion in 2025 and is expected to surpass $345.30 billion by 2030. In addition to its steady growth, the data analytics domain is being shaped by emerging technologies, innovative applications, and evolving industry dynamics. Keeping up with emerging data analytics trends is especially critical for any company that aims to navigate this complex and ever-evolving field and implement a reliable data analytics solution.
In this article, experts from Itransition, a company with 15+ years of experience in data analytics services, highlight three trends currently dominating the data analytics domain and are expected to remain impactful in the coming years.
Agentic AI
In recent years, agentic AI has gained significant traction, and today, its rapid expansion across diverse business and technology areas continues. Its integration with data analytics is deepening, with many organizations beginning to view AI agents as dependable tools for analytical tasks. In the 2025 PwC AI Agent Survey, 38% of respondents indicated that they trust AI agents most for data analysis. In practice, instead of executing time-consuming preliminary data analysis activities like data collection and preparation themselves, business users can delegate these tasks to AI agents.
For instance, a user can simply provide an instruction in natural language (e.g., “Find financial records for Q4 2025 in the central repository, extract and merge information about our profits, and present it to me via a dashboard”), and an AI agent will execute it. Similarly, users can analyze prepared data without writing SQL queries or using Python, simply prompting the agent with questions such as “Why was our customer churn rate so high in Q4?” or “What is the optimal price for our product?”
Beyond merely responding to prompts, agentic AI solutions can also handle the data analytics process end-to-end and make decisions based on generated insights without direct human involvement, enabling a shift from traditional human-in-the-loop validation to more autonomous decision-making. This autonomy is enabled by the cognitive framework underlying AI agents, which is defined by KPMG experts as the PRAL loop:
- Perceive: An agent begins by gathering relevant data from a variety of input sources or environments where it operates — databases, sensors, APIs, logs, user inputs, or third-party services. It filters and organizes incoming signals, detects anomalies and contextual cues, and prepares a structured dataset for subsequent interpretation and processing.
- Reason: The agent analyzes the organized information to generate insights, combining statistical methods, domain-specific rules, and AI-driven inference. It evaluates possible explanations, estimates uncertainty and implications, prioritizes hypotheses, and formulates the next steps or recommendations that best align with objectives, constraints, and risk tolerances.
- Act: After deciding on a course of action, the agent carries out authorized tasks within its operational boundaries — updating records, triggering workflows, sending alerts, executing transactions, or producing reports and visualizations. Actions include safeguards, logging, and rollback options to ensure traceability, compliance, and minimal unintended impact.
- Learn: Finally, the agent reviews outcomes and feedback from its actions to measure effectiveness and identify errors or biases. It updates models, refines rules, adjusts decision thresholds, and incorporates new examples into training data so future cycles are more accurate, efficient, and better aligned with evolving environments and objectives.
Synthetic Data
The creation and use of artificially generated data that mirrors real-world data is another influential trend reshaping industries and analytics practices. In 2025, Research Nester valued the synthetic data generation market at $447.16 million, and the experts forecast it to grow up to $8.79 billion by 2035. Synthetic data is gaining popularity because it is easier, safer, and more cost-efficient to use for many tasks while preserving the statistical characteristics of real datasets.
Although synthetic data can have many potential applications in analytics, its primary use case today is AI and machine learning model training. To be more specific, data professionals leverage synthetic data to supplement or even fully replace real-world data, which is often difficult to collect, cleanse, and secure.
Synthetic data is also valuable for modeling scenarios that have not occurred in the real world, such as the emergence of new markets or unprecedented economic events. Synthetic data can additionally be used to add noise to training data. As stated in the 2025 whitepaper Synthetic Data: The New Data Frontier from the World Economic Forum, “For some use cases, adding synthetic noise during training can improve model robustness, prevent overfitting, or help assess the impact of existing noise on model performance.”
Data Fabric
The growing adoption of data fabrics is another notable tendency in the modern business and technology landscape, which also impacts the data analytics domain. Researchers from Coherent Market Insights state that the global data fabric market surpassed $3.55 billion in 2025 and is expected to reach $17.02 billion by 2032.
In short, the concept of a data fabric involves establishing an integrated, enterprise-scale data management system, typically using specialized tools such as Microsoft Fabric, AWS Industrial Data Fabric, or Dataplex. Using a metadata-driven approach, this system indexes all data owned by a company, regardless of where it resides (data warehouses, data lakes, etc.), and provides a unified view of it. Beyond acting as a catalog, a data fabric orchestrates data integration, transformation, governance, and delivery processes.
For analysts, this means fast, self-service access to high-quality, trusted data. As a result, they can avoid manual data discovery, integration, and preparation, as well as delays caused by dependencies on IT teams. This allows them to focus more on generating insights and delivering value.
Final Thoughts
The data analytics sector is in continuous evolution, reflected not only in rapid growth but also in the emergence of new technological trends. Agentic AI, synthetic data, and data fabric represent three major trends redefining how organizations access, manage, and analyze their data. An experienced provider of data analytics services can help you navigate and capitalize on these and other trends by providing strategic guidance throughout your software implementation project or by delivering a robust analytic solution.
Suggested articles:
- Data-Driven Project Management: Web Analytics Integration
- Bridging Data, Strategy, and Execution in Modern Businesses
- How to Implement Data Strategies for Business Success
Daniel Raymond, a project manager with over 20 years of experience, is the former CEO of a successful software company called Websystems. With a strong background in managing complex projects, he applied his expertise to develop AceProject.com and Bridge24.com, innovative project management tools designed to streamline processes and improve productivity. Throughout his career, Daniel has consistently demonstrated a commitment to excellence and a passion for empowering teams to achieve their goals.