In 2030, the global data analytics industry is forecast to reach $402.7 billion, up from $82.23 billion in 2025. A large part of this growth comes from investments in AI-powered analytics, edge computing, and tools that make data accessible to non-technical users.
In this report, we’ll cover these and other current data analytics trends. You’ll find market statistics and growth projections, along with industry-specific adoption rates. We’ll also explore emerging approaches like data mesh, data fabric, data as a service, and synthetic data generation. So, let’s get started!
Data analytics is the process of analyzing data sets to find useful information for solving problems. It combines computer programming, statistics, and mathematics to provide precise data analysis.
The potential of data analytics trends is evident in the remarkable market growth. The global data analytics market size was valued at $ $64.99 billion in 2024 and hit $82.23 billion in 2025. By 2030, experts expect it to reach $402.7 billion, with a compound annual growth rate (CAGR) of 25.5%.
North America dominated the global market, accounting for a substantial 33% share in 2024. Concentrated technology infrastructure and major providers, including IBM, Microsoft, Google, and AWS, support the region’s growth.
However, the Asia Pacific is expected to grow at the fastest rate through 2030, driven by rapid AI adoption across India, China, and Japan.
By industry vertical, IT and telecom currently lead in market share. Companies in this sector use data analytics trends to improve network services, security, and customer experience. Supply chain management dominates application-wise. Manufacturers are using analytics to optimize capacity planning and demand forecasting.
Predictive analytics held the largest market share by type in 2024. Real-time analytics, in turn, is projected to grow fastest through 2032. Organizations are investing heavily in complex event processing to analyze large volumes of data in motion. In fact, a global survey found that 52% of companies are already allocating resources to this technology.
Customer analytics trends also drive adoption. According to a Qualtrics and ServiceNow study, around 83% of customers are willing to switch brands for better digital engagement. Additionally, 70% are more likely to trust brands offering seamless services. By analyzing data, organizations can personalize and enhance customer touchpoints.
Having said that, it is high time to review the latest data analytics trends сhanging the industry in 2025 and beyond.
Augmented analytics, driven by AI and ML, is one of the fastest-growing data analytics trends. This approach uses machine learning and natural language processing to facilitate analysis,
The global advanced analytics market has witnessed significant growth, valued at $15.26 billion in 2025, a 70% increase from $8.95 billion in 2023. By 2032, analysts expect it to reach $87.03 billion. The market will exhibit an impressive compound annual growth rate (CAGR) of 28.2% during the forecast period of 2025-2032.
Now, let’s look at what’s driving this growth. Investment activity in AI and analytics continues to surge. In the first half of 2025, venture capital funding for AI companies exceeded $205 billion globally, up 32% from H1 2024. Nearly one-third of all venture funding was directed to 11 AI-related startups.
For example, Databricks, a platform that combines AI with enterprise-scale data management, raised $10 billion in December 2024 at a $62 billion valuation. Then, in August 2025, it announced a $1 billion Series K round that pushed its valuation above $100 billion.
Now, let’s move to advanced analytics trends by industry use. In 2024, the BFSI (Banking, Financial Services, and Insurance) segment led the US augmented analytics market, with a 20% share by end user. Banking and financial institutions invested significantly in advanced solutions to enhance decision-making.
However, analysts expect the situation to change. They project the retail industry will gain the highest CAGR over the forecast period. Increased online shopping and the growth of e-commerce supply chains will drive this gain.
Apart from retail, other sectors like healthcare and manufacturing are also driving increased demand. In healthcare, for example, augmented analytics helps enhance patient care quality and reduce operational complexities.
In 2024, North America led the way in augmented analytics, with a market share of $3.86 billion. This was driven by various factors, such as:
Analysts expect the United States to capture the largest share of the North American market. The dominant presence of leading augmented analytics market players based in the country drives this expectation.
Next, let’s move on to one of the well-established trends in data analytics: edge analytics.
Edge analytics processes and analyzes data right where it’s created, typically near IoT devices and sensors. This approach has gained popularity due to the massive volume of data generated every day by billions of connected devices.
Back in 2021, Gartner predicted that 75% of enterprise-generated data would be created and processed at the edge by 2025, up from just 10% at the time. The need for real-time data analysis and the limitations of conventional cloud computing drove this prediction.
In 2025, the global device base generates 328.77 million terabytes of data every day. Also, IDC projects that 41.6 billion connected IoT devices will generate 79.4 zettabytes of data by the end of 2025.
Organizations are responding to these data analytics trends with heavy investment in edge infrastructure. According to IDC, global spending on edge computing grew from $228 billion in 2024 to $261 billion in 2025. Retail, manufacturing, healthcare, and financial services lead adoption. These industries are deploying edge solutions across more than 1,000 enterprise use cases, ranging from video analytics to real-time supply chain optimization.
The edge analytics market specifically is valued at $17.30 billion in 2025. Analysts expect it to reach $52.04 billion by 2030, growing at a CAGR of 24.6%.
As of 2025, several factors drive this transition toward edge analytics:
Moreover, edge analytics is crucial for industries that require immediate data analysis. For example, data analytics trends in healthcare show the highest industry CAGR at 32%. Hospitals deploy real-time patient monitoring and imaging analytics at the bedside.
Hybrid architectures are also becoming standard. Here, sensitive workloads run inference locally while batch analytics processes in centralized clouds overnight.
Now, let’s take a closer look at how data analytics trends are making insights accessible to everyone. The democratization of data remains the top priority trend in data analytics for 2026. Self-service analytics empowers both experts and non-technical users to analyze data without relying on specialized data skills or knowledge.
Traditionally, complex legacy BI tools confined data analysis to IT specialists. Business users either had to forgo insights or depend on technical teams to interpret data. Today, self-service BI platforms replace those barriers with:
One of the latest trends in data analytics democratization is the rise of no-code and low-code solutions. These solutions allow the creation of applications through drag-and-drop interfaces without extensive coding. In March 2023, Pyramid Analytics introduced AI-driven augmented efficiency in collaboration with the OpenAI platform. This move aimed to drive the widespread adoption of this data analytics trend with its no-code and AI-assisted capabilities.
Workflow automation platforms also play an expanding role in augmented analytics. Tools like Zapier, Make, n8n, and Microsoft Power Automate let organizations connect analytics applications to their broader tech stack without writing code. Moreover, automation integrates AI-powered natural language capabilities to further drive non-technical adoption.
As a result, European organizations, long challenged by multilingual user bases, now see 50% more non-technical engagement after rolling out LLM-powered interfaces.
By democratizing data, enterprises empower numerous employees to become “citizen data scientists.” These individuals can analyze data, albeit not as their primary role. In fact, productivity for analytical tasks has risen 74% as citizen data scientists pair domain knowledge with simplified tooling.
As a result, regular business users, dubbed “augmented consumers,” can now:
However, for democratization to succeed, organizations must invest in training. According to the World Economic Forum, 85% of employers plan to prioritize upskilling their workforce to work with AI and data tools in 2026 and beyond.
Poor data quality remains one of the most expensive problems in enterprise data management. According to Gartner research, every year, poor data quality costs organizations an average $12.9 million.
This figure has significant implications for AI initiatives in particular. Forecasts indicate that, through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. Moreover, 63% of data management leaders either do not have, or are unsure whether they have, the right data practices for AI.
Prioritizing high-quality data is now one of the key data analytics trends for data leaders. These challenges have accelerated interest in two emerging approaches: data mesh and data contracts.
Data mesh takes a decentralized, domain-oriented approach, treating data as a product. Instead of maintaining monolithic data lakes, individual departments take ownership of their data while adhering to global interoperability standards. Data contracts complement this approach by defining:
Investors see potential in data mesh and data contracts approaches. In Fall 2023, NextData, led by Zhamak Dehghani, the author of “Data Mesh”, raised $12 million. Additionally, Gable.ai, a data contracts platform headed by CEO Chad Sanderson, raised $7 million.
Data as a Service (DaaS) also represents one of the most prominent data analytics trends. The DaaS market is surging, fueled by businesses’ growing need for data-driven insights and competitive advantages. The analysis indicates the DaaS market reached $24.89 billion in 2025. Experts project it to soar to $61.93 billion by 2030, exhibiting a remarkable 20% CAGR.
Several factors drive DaaS adoption:
Businesses with unified data platforms see information retrieval speed improve by three to five times. They also report 50-70% gains in response accuracy. Executives increasingly view data as a strategic business asset rather than a technical overhead.
The BFSI sector leads DaaS adoption by industry with 28.7% of total market revenue in 2024. Regulatory compliance requirements and sophisticated fraud-prevention systems drive data analytics trends in banking. Banks and insurers report positive ROI from anti-fraud programs but recognize they still lack advanced AI capabilities. This gap pushes for continued investment in data infrastructure.
Large banks prioritize DaaS for analytics and reporting services. Smaller financial institutions, however, are slower to adopt. DaaS helps these organizations simplify outputs, create consistent datasets, spot patterns, and cut processing time.
Within the DaaS market, healthcare posted a 22.5% CAGR, the highest growth rate among all verticals. Healthcare organizations invested an average of $38 million in cloud services in 2024. And 72% reported satisfaction with their migration outcomes.
Company size influences DaaS adoption patterns. Large enterprises generated 67.3% of DaaS revenue in 2024, thanks to larger budgets and in-house analytics teams. Yet SMEs show the fastest DaaS uptake at 23.9% CAGR through 2030. Pay-as-you-go pricing eliminates the need for big upfront investments.
The AI connection drives a significant DaaS opportunity. 85% of enterprises now leverage data lakehouses to support generative AI projects. Financial data providers expect AI-related DaaS revenue to add $1.9 billion by 2028 as banks subscribe to continuously refreshed market feeds.
Analysts project that industry-focused AI models will make up more than half of all generative AI deployments by 2027. This will further boost demand for niche DaaS offerings.
As the DaaS market grows, it not only caters to advanced users but also supports the learning needs of beginners and professionals alike. Many platforms offer beginner-friendly online data analysis courses to help individuals build essential skills from scratch. These courses are designed to be self-paced and interactive, ensuring that learners can easily understand and apply data analytics concepts.
One of the key data analytics trends is the growing use of metadata-driven data fabric solutions. As companies become more complex, they need flexible integrated systems for managing data.
A data fabric is an architecture that allows access to data engineering, analytics, and other data services. It ensures consistent data practices whether data is in the Cloud, on-premise, or at the edge. The data fabric connects different endpoints and provides integration, metadata discovery, governance, and processing.
Analysts evaluated the global data fabric market size at $2.81 billion in 2024. They project it to grow to $11.9 billion by 2034 at a CAGR of 15.54%.
AI readiness has become the primary driver for data fabric adoption. According to IBM‘s 2025 research, 50% of CEOs report their organizations have disconnected technology due to the pace of recent investments. While 67% of CFOs say their C-suite has data to capitalize on new technologies, only 29% of tech leaders strongly agree their data has the necessary quality, accessibility, and security to scale generative AI.
Additionally, Gartner analysts predict that by 2028, data management markets will converge into a single market around data ecosystems enabled by data fabric and GenAI. The firm recommends investing in metadata management and data fabric to support multimodal inputs and outputs for GenAI experiences.
In terms of business applications, the fraud detection sector held the highest revenue share at 27%. A resilient data fabric improves business process effectiveness while enhancing security measures. Big data platforms built on this architecture evaluate transactions in real-time and spot unusual user behavior across large-scale patterns.
When it comes to industry insights, the BFSI sector had the highest income share of over 23%. Many state regulations encourage the exchange of consumer data with agreements. Data fabric makes it simpler for banking organizations to handle data in a consistent and secure manner.
On the other hand, pilot projects in the energy and electricity sectors have been the extent of digitalization efforts; no large-scale initiatives have been put into action. It is primarily caused by aging IT networks that rely on traditional methods.
Geographically, North America had the largest income share of over 47%. The North American area controls a lion’s portion of the data fabric market. The area is also known for being among the first to embrace cutting-edge solutions. The main driving forces behind the U.S. market are the presence of the majority of data centers.
Finally, the last data analytics trend we’ll explore today is synthetic data. Synthetic data refers to information generated by artificial means, such as statistical algorithms or AI.
Industry reports indicate that the synthetic data generation market was valued at $0.51 billion in 2025. Moreover, it is projected to grow at a 39.4% during the forecast period, reaching $2.67 billion by 2030. Notably, North America dominated the global market with a 38.7% share.
A key driver behind the adoption of synthetic data is the rise in the deployment of Large Language Models (LLMs). These learning algorithms, such as Generative Pre-trained Transformer (GPT), can translate, generate, and predict text and other content types based on large datasets.
Furthermore, privacy concerns and compliance risks associated with real-world data have contributed to the growth of synthetic data generation. Regulations like GDPR, CCPA, and HIPAA have made it challenging to access real-world datasets. Consequently, synthetic data provides an alternative with similar statistical properties while ensuring privacy.
Here are several application areas that stand out in the current synthetic data analytics trends:
However, synthetic data introduces governance and ethical risks that require careful management. According to the World Economic Forum, if not properly generated and controlled, synthetic data can perpetuate biases from original datasets, mislead decision-makers, leak sensitive information, or enable malicious uses like deepfakes.
All in all, the future of data analytics appears to have several key trends driving its innovation. A key part of these trends is making advanced analytical capabilities more accessible. Solutions like augmented analytics that use AI and ML are democratizing data analysis by delivering real-time insights. The rise of self-service analytics platforms allows business users across organizations to analyze data independently.
At the same time, trends like edge analytics address the need to process the huge amounts of data. By analyzing data at the source, organizations can enable real-time decision-making.
Underlying these data analytics trends is a bigger focus on data quality and integrated data management. Approaches like data mesh and data contracts aim to improve reliability and get the full value from data assets.
Additionally, the growth of Data as a Service (DaaS) models streamlines access to data. It gives organizations on-demand analytics capabilities.
Together, these data analytics industry trends provide organizations with tools to drive innovation. As businesses continue adopting them, they position themselves to succeed in the data-driven digital transformation.
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Request CVs2025 has focused the main data analytics trends on augmented insights and agentic AI. Organizations are moving toward democratized real-time analytics, using automated systems that analyze data and generate findings via natural language interfaces, without the need for deep technical knowledge.
The future of data analytics points to fully autonomous AI systems. Organizations expect them to handle the entire decision-making cycle, from data analysis to independent action. Smaller language models, fine-tuned for specific business domains, are expected to become more common. They will replace generic LLMs to deliver more accurate and context-aware results.
However, as technology and data analytics advance toward AI, regulation will follow. Businesses will need transparency in how AI reaches conclusions, especially in finance and healthcare.
The biggest data challenge in 2025 is poor data quality, which costs organizations an average of $12.9 million annually. This trend directly impacts AI initiatives. Analysts forecast that 60% of AI projects will be abandoned through 2026 due to a lack of AI-ready data. Additionally, 63% of data leaders either lack or are unsure if they have the right data practices for AI.
Another growing concern is synthetic data governance. If not properly managed, synthetic data can perpetuate biases, mislead decision-makers, or leak sensitive information.
