Andreas Bartsch | Head | Innovation & Services | PBT Group | mail me |
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.
Now that we are in 2026, the conversation has shifted. 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. These data and technology trends will define how organisations scale analytics and AI capabilities in 2026.
Last year, we highlighted themes like data literacy, responsible AI governance, automation and real-time analytics as key trends for 2025. What we have seen since then is not a reversal, but an acceleration. The same fundamentals still apply. However, the stakes are higher and the gaps are more visible. These data and technology trends are now becoming core strategic priorities.
Looking back to look ahead
In 2025, many organisations moved from talking about data literacy to 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. This pressure increased 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.
Real-time analytics grew as infrastructure and local data centre investments improved. This growth supported 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, this year will be about building on these gains in a more structured, scalable way.
Augmented analytics moves into the mainstream
We 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. These pieces range from data preparation and cleansing through to analysis and insight generation. Augmented analytics is one of the most impactful data and technology trends reshaping business intelligence.
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.
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. 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.
Data mesh, data fabric and related patterns promote decentralised ownership and domain-level accountability for data products. At the same time, they provide common standards and governance. For 2026, we expect 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. 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. They will help organisations maintain transparency, auditability and compliance without bringing projects to a standstill.
Data-centric AI and democratised tools
The fifth trend 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.
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. However, 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 they also require careful design and security.
Ethical, explainable and compliant analytics build trust and reduce legal and reputational risk. Taken together, these data and technology trends will determine which organisations lead and which lag in 2026.
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.
