Data and Technology Trends That will Define 2026

2025 settled the debate around the importance of data and AI. Most companies have already pulled these tools into their core operations in one way or another. As we kick off the new year, the conversation changes. The real test is whether those early efforts can mature, hold up under pressure, and deliver value that lasts beyond the first wave of excitement.

“Last year, we highlighted themes like data literacy, responsible AI governance, automation, and real-time analytics as key trends for 2025,” says Andreas Bartsch, Head of Innovation and Services at PBT Group. “What we have seen since then is not a reversal, but an acceleration. The same fundamentals still apply, but the stakes are higher, and the gaps are more visible.”

Looking back to look ahead

In 2025, many organisations moved from talking about data literacy to actually building it into training and leadership agendas. Teams that could interpret and question data in context were better positioned to use analytics and AI responsibly. At the same time, boards and regulators pushed harder on governance and ethics, especially as AI moved deeper into financial services, telecoms, public services, and marketing. GenAI also shifted from novelty to utility.

“In many clients, we see GenAI moving from isolated pilots into everyday tools. It is now writing first drafts, assisting with code, summarising documents, and helping analysts explore data faster. That is a significant mindset shift from experimentation to operational use,” says Bartsch.

Real-time analytics grew as infrastructure and local data centre investments improved, particularly for use cases such as fraud detection, real-time customer insights, and more responsive supply chains. Knowledge work, from content creation to analysis, began to feel different as GenAI became part of the standard toolkit rather than something used only by a few pioneers.

“Those developments set the stage for 2026. If you work with data, run analytics teams, or plan technology investments, the year ahead will be about building on these gains in a more structured, scalable way,” Bartsch emphasises.

Augmented analytics moves into the mainstream

PBT Group highlight augmented analytics as the first major trend for 2026. This refers to the use of AI and machine learning to automate pieces of the analytics workflow, from data preparation and cleansing through to analysis and insight generation.

“Augmented analytics lowers the technical barrier for business users. You will see more analysts, managers, and specialists using tools that quietly automate the heavy lifting behind the scenes. The goal is not to replace skills, but to shorten the path from data to decision,” says Bartsch.

For organisations, this means carefully considering how these tools are embedded, governed, and supported. Automated insight is only helpful if the underlying data is well managed and if users understand what the system is doing.

Real-time, streaming, and edge analytics

The second trend is the continued rise of real-time, streaming, and edge-based analytics. As more devices, applications, and services generate live data, the demand for immediate feedback grows.

“Use cases that once tolerated batch processing now demand near real-time responses. Whether it is IoT sensors in manufacturing, behavioural data from digital channels, or operational monitoring, the expectation is that systems will respond as events happen, not hours later,” says Bartsch. “This moves processing closer to the source, often at the edge, and introduces new complexity around latency, privacy, and security. It also increases the need for clear data lineage and strong engineering practices. Real-time insight without robust design simply speeds up bad decisions.”

Modern data architectures, not just warehouses

The third trend is architectural. Many organisations are moving beyond a single, centralised data warehouse towards more flexible approaches such as data mesh and data fabric.

“These approaches are attractive because they acknowledge what is happening. Data now lives across hybrid and multi-cloud environments, on-premises systems, and external platforms. Trying to force everything into one place creates bottlenecks,” Bartsch says.

Data mesh, data fabric, and related patterns promote decentralised ownership and domain-level accountability for data products, while still providing common standards and governance. For 2026, Bartsch expects more organisations to adopt elements of these architectures rather than attempting full, overnight change.

“Modern architectures are not a silver bullet. However, they do give you a more scalable way to align data with business domains as your environment grows.”

Governance, provenance, and compliance as non-negotiables

As data volumes and regulatory pressure continue to increase, governance will remain central. Provenance, in particular, knowing where data comes from, how it has been transformed, and how it is being used, will become more visible.

“Trust in analytics and AI depends on traceability,” says Bartsch. “Customers, regulators, and internal stakeholders will expect you to show how you arrived at an outcome. That requires more than a written policy. It demands automated controls, good metadata, and clear accountability across the lifecycle.”

AI-assisted governance tools will play an increasingly important role here, helping organisations maintain transparency, auditability, and compliance without bringing projects to a standstill.

Data-centric AI and democratised tools

The fifth trend PBT Group points to is a shift towards data-centric AI. Instead of only tuning models, attention is moving to the quality, structure, and suitability of the data that feeds them.

“Better datasets generally beat minor model tweaks. In 2026, we expect more investment in how datasets are designed, labelled, and maintained, along with stronger pipelines that keep them current and reliable,” says Bartsch.

Alongside this, there is growth in low-code and no-code analytics platforms and self-service tools. These options expand analytics to a broader user base, but they also increase the importance of strong governance and quality control. Democratisation without discipline simply moves risk closer to the front line.

Why these trends matter for 2026

Across all these themes, the common thread is strategic intent. Modern data architectures lay the foundation for scaling analytics and AI. Augmented analytics and democratised tools reduce bottlenecks and free scarce specialists to focus on higher-value work. Real-time and edge analytics unlock new use cases, but also require careful design and security. Ethical, explainable, and compliant analytics build trust and reduce legal and reputational risk.

“In our view, the technology trends of 2026 will reward professionals and organisations that think strategically and act responsibly. The most future-ready teams will not only understand the tools, but will know how to apply them to real business problems in a way that is explainable, governed, and sustainable,” concludes Bartsch.