The possibilities created by data are almost unlimited. Yet increased choice does not necessarily make things easier for enterprises — often the opposite. It becomes harder to determine what is actually relevant and to identify the insights that truly matter.
At the same time, expectations are rising. In the context of data, organisations are expected to understand what is what, prioritise correctly, anticipate change and identify the right ecosystem to help capture value. This is happening in a time when AI is often presented as a universal answer to complexity and decision-making, while at the same time placing high demands on data quality, structure and governance.
Against this backdrop, three trends stand out as particularly important when IoT data is part of the equation:
For many organisations, the analytics journey begins with analytics applications and visualisations. Dashboards and reports provide visibility, enable pattern recognition and establish a shared language around data.
What has changed are the underlying conditions. Technological advances in data management, data security and data governance, important elements within the broader concept of Data Architecture, have significantly reduced many of the earlier constraints related to data sharing and exposure.
As a result, analytics has matured. Insights are increasingly tailored to roles and responsibilities, with the ambition of delivering the right data to the right person, at the right time.
In the past, analytics applications largely served classic BI needs, helping organisations track KPIs over time to assess business health and trends. Over the years, this has evolved as new expectations and technical capabilities have reshaped what analytics can deliver.
Today, analytics tools can adapt to the individual by providing contextual and role-aware insights that not only enhance situational understanding but also support richer strategic and tactical conversations across the organisation.
Connectivity providers observe how IoT subscriptions behave over time — including traffic patterns, availability, anomalies and usage changes, without direct visibility into the specific products, services or use cases behind each device.
IoT customers, in turn, know what each device represents: the product or service it belongs to, how it is used, and what is business-critical in their operations. When these perspectives are brought together across organisational boundaries, it becomes possible to shape analytics around the customer’s specific context — enabling insights that reflect real-world use cases, priorities and impact.
Through subscription-level identifiers, reference data on products, services and customer categories can be securely linked and shared between connectivity providers and customers. This creates a foundation for insights that are more contextual, actionable and easier to work with across multiple functions.
At the same time, questions around the right partnerships are becoming more prominent as solution complexity grows. Deep domain expertise in IoT and connectivity becomes increasingly important, and involving connectivity partners early, or sharing relevant reference data directly with IoT providers, can significantly increase the impact compared to treating analytics purely as a system integration exercise.
Ultimately, the key question remains: why are we doing this?
As IoT incorporates more devices, technologies and use cases, analytics requirements become increasingly dynamic. The focus shifts from retrospective analysis to continuous detection of deviations, understanding of behaviour and prioritisation of actions, often with a high degree of automation.
This drives a growing need to share IoT data securely across organisational boundaries. Environments such as “clean rooms” are being set up, allowing multiple parties to work with the same data without full access and without risking data leakage, in line with GDPR and data ownership regulations.
In practice, customers retain visibility and control over outcomes, while other parties perform analytics on anonymised or cryptographically protected versions of the data without access to the underlying raw information.
This becomes even more important as AI enters the conversation. The move towards customer-tailored Analytics-as-a-Service follows the same logic as combining connectivity data with customer context: analytics should not remain a generic, connectivity-only capability, but evolve into something shaped by the full understanding of how devices are used in real operations.
For AI agents to meaningfully support or automate business processes, they must operate with rich context drawn from both connectivity behaviour and the customer’s product and service domains. As data-sharing models mature, emerging interaction frameworks such as Model Context Protocols (MCP) and AI agents further amplify the need for high-quality data, structure and governance across both parties.
The difference between simple AI assistants and truly autonomous agents is substantial, and the depth and completeness of the shared contextual data remains the decisive factor.
These shifts point to a clear direction. IoT analytics creates the greatest value when connectivity behaviour and product context come together, and when analytics evolves from isolated tools into a scalable capability built on structure, trust and collaboration.
Please don’t hesitate to reach out if you’re interested in our solutions or would like to have an overall dialogue about analytics or the future of analytics.
Boston Consulting Group (BCG). (2023). A New Architecture to Manage Data Costs and Complexity.
Berg Insights. (2025). The Global M2M/IoT communications Market (10th edision). Berg Insights.
Mustafa Bayat, M. A. (2025). Enhancing secure IoT data sharing through dynamic Q-learning and blockchain at the edge. Nature.
Data Insights Market. (2025). Data Clean Room Software: Disruptive Technologies Driving Market Growth 2026-2034.
Roy, K. (2025). Analytics as a Service: A Modern Approach to Data Engineering.
(2026). Model Context Protocol. Wikipedia.
MIT Technology Review. (2026). The era of agentic chaos and how data will save us.
Ready to simplify your IoT deployment? Talk to our connectivity experts!
