Top trends in big data for enterprises in 2026

1. Agentic AI transforms big data analytics

Over the past few years, AI capabilities have greatly transformed big data analytics. Traditional BI excels at visualizing business performance through charts, graphs and KPIs. Previously, AI was assistive, augmenting the human analyst's insights, typically over data warehouses or specially curated datasets.

Agentic AI brings real changes to this process. Systems can explore data, relate it to documented strategy and deliver insights autonomously, without explicit requests from human analysts. Major vendors in the BI space have embraced agentic AI.

The convergence of GenAI analytics and agentic workflows is changing roles across data and analytics teams. Rather than manually producing every insight or report, those teams collaborate with AI agents. Agentic systems promise to handle time-consuming BI work, freeing analysts to focus on strategic initiatives.

However, the challenges aren't trivial. Organizations have concerns about data privacy and hallucinations with agentic AI systems. Human oversight -- often described as human-in-the-loop -- along with audit trails and governance frameworks, is key to monitor agent behavior. As agents take on basic analytics work, humans become these digital teammates' supervisors and interpreters.

2. Privacy-preserving analytics matures beyond theory

The increased use of AI in autonomous decision-making, especially regarding customers or patients, has built demand for privacy-preserving analytics -- technologies and methods that enable data analysis without exposing sensitive or identifiable information. Two key techniques are in use, often in combination.

• Federated learning. A cornerstone of privacy-preserving AI techniques, federated learning trains models across decentralized data sources without moving raw data to a central server. Only aggregated updates, stripped of sensitive details, flow to a coordinating server.
• Differential privacy. Once specialized, it is now more common. This method adds controlled noise into data sets or query results to obscure individual-level data points while largely preserving the usefulness of statistical analysis.

Synthetic data generation is now widely adopted for training AI models. Numerous platforms can generate tabular data, text, JSON, events and more. In industries where real-world data collection is slow, costly and regulated, such as finance and healthcare, synthetic data fills gaps and helps teams plan for rare or extreme scenarios. However, synthetic datasets often lack historical context necessary for reliable trend modeling.

Expect to see increased integration of these techniques as standard options for commercial data and analytics platforms supporting big data applications.

3. Hybrid AI architecture becomes the default strategy

Public cloud services continue to grow, and with increased SaaS adoption, business data is frequently created and maintained in the cloud. But AI workloads differ substantially from traditional operational data. As such, teams must rethink where data lives and where operations run.

Cost management is a key driver. The public cloud pay-as-you-go model can produce unanticipated spending with increased usage. Training AI models or running them in production often drive high consumption and bigger bills.

Data sovereignty is another pressing concern. The principle is straightforward: data is subject to the laws of the jurisdiction where it's stored. Cloud computing complicates this paradigm, as it separates data from geography. For example, a team in the U.S. saving documents to a project folder in the cloud might not know precisely where that data is stored at any given moment. It could reside on a server in Europe and be subject to different rules.

AI adds further complexity to data sovereignty because legislation isn't just concerned with data's location, but also model training locations, the type of data used in training and how organizations use those outputs. A model trained on European patient records but hosted by American healthcare providers raises sovereignty questions, even if the original data never left Europe.

Currently, the EU AI Act enforces rules on high-risk systems and general-purpose models for businesses operating in Europe. In the U.S., legislation is still in development at both the state and federal levels.

These pressures explain why hybrid architecture is a default strategy for IT teams. In a hybrid system, sensitive data and model training can remain on-premises or in regional facilities subject to local laws. Less regulated operations can take advantage of cloud elasticity and global reach.

4. MLOps evolves into LLMOps

Machine learning operations (MLOps) -- the practices and tools for developing, deploying and managing predictive models -- emerged over the past decade. These practices standardized how IT teams move models from experiments into production systems. MLOps involves centralizing model management and automating the ML lifecycle to ensure reuse, efficiency, governance and compliance.

The rapid rise of large language models (LLMs), such as Gemini and ChatGPT, stretches established practices, especially for computing costs and governance. As part of this development, new disciplines have emerged, such as prompt engineering to craft the instructions for LLMs and retrieval-augmented generation, which connects AI models to corporate knowledge bases. Managing these systems requires LLMOps, an evolution of MLOps tailored to the demands of LLMs.

In 2025, reliability when moving AI projects from prototype to production was a common challenge. However, in 2026, teams are building dedicated operational infrastructure for their AI systems. For data leaders, the bottleneck is no longer building models but operating them responsibly and confidently at scale.

5. Small language models offer distinct advantages

Agentic AI enables more direct business automation and promises greater efficiency, but it also raises regulatory concerns and increases the need for cost control. As such, mainstream LLM adoption has seen a countermovement toward smaller, more efficient models.

Small language models (SLMs) often have fewer than 30 billion parameters. For context, LLMs can have trillions of parameters. SLMs are also typically open source and valued for reduced costs, ease of deployment and customization rather than raw power. Because deployment is often on-premises, organizations can process sensitive data entirely within their secure infrastructure. This addresses data sovereignty concerns and simplifies data protection compliance.

More specialization is coming with smaller models in 2026.

• Microsoft claims one variant of its Phi-4 model outperforms larger models on math-related tasks.
• Google's Gemini Nano is designed for deployment on devices.
• Meta's Llama 3.2 offers multilingual text generation.

Domain-specific models can be particularly effective. For example, Diabetica-7B, which is designed for diabetes-related inquiries, or PatentBERT for intellectual property research.

6. Data lakehouses are now standard

Data lakehouses aren't only a repository of enterprise data; they also serve as the long-term "memory" for AI models and agents. Lakehouse architecture's efficiency and scalability remain its core features, but there's greater emphasis on governance and metadata management.

Apache Iceberg remains the leading open table format. However, new interoperability layers, such as Apache XTable, are making specific format choices less consequential. Developers can now read and write across Iceberg, Delta Lake and Hudi interchangeably.

The "open" movement extends beyond storage formats to include catalog standards, such as Apache Polaris and emerging specifications for semantic layers, such as Open Semantic Interchange. These approaches reduce the risk of vendor lock-in to help IT departments avoid getting stuck on a proprietary and often expensive platform due to migration difficulties.

7. Quantum computing moves from theory to practice

The quantum computing industry is moving from theory to reality. There have been several important developments, including:

• Cloud service providers -- such as IBM Quantum Platform, Microsoft Azure Quantum, SpinQ Cloud and Amazon Braket -- offer subscription-based services, making it easier for businesses to experiment with quantum computing and develop applications.
• Fujitsu and RIKEN, who unveiled a 256-qubit superconducting machine in 2025, are targeting a 1,000-qubit system in 2026 scaled for commercial workloads.

McKinsey's 2025 Year of Quantum report suggests the quantum computing market could generate between $28 billion and $72 billion in global annual revenue by 2035. However, for data leaders dealing with large, complex systems, the question is how to prepare. Current encryption methods will become vulnerable once sufficiently powerful quantum computers exist. The most pressing concern for most companies is preparing for a post-quantum cryptographic world.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.