Data analytics is one of the most dynamic and fastest-growing segments of the technology landscape. Driven by the need to process and interpret huge volumes of varied data, companies are increasingly investing in business intelligence, data visualization and AI-powered analytics tools. 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 

Agentic AI has gained significant momentum in recent years and continues to expand rapidly across diverse business and technology sectors. 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 such as 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 them 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: First, an agent collects data from input sources or environments where it is located.  

• Reason: Next, an agent interprets the obtained information to generate insights, assesses their potential practical implications and decides what to do next, typically by combining rule-based logic and AI reasoning. 

• Act: Once an agent makes a decision, it executes an action within the permissible range, such as updating a database, initiating a transaction or drafting a personalized email. 

• Learn: Finally, an agent assesses the outcomes of executed actions to refine its performance and improve decision-making, and then the entire cycle repeats.  

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 maintaining the statistical characteristics of real-world datasets.  

Although synthetic data can have many potential applications in analytics, its primary use case today is AI and ML 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 continuously evolving, which is 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.