Co-owner, Member of the Supervisory Board, Intecracy Group
IT investment professional educated in law and finance; advisor on investment and M&A.
In the AZIOT knowledge base, the expert provides strategic commentary on published articles: investment, governance, security, and architecture angles for infrastructure decisions.
Electromagnetic interference (EMI) can distort sensor data in industrial systems, leading to false triggers and inaccurate insights. This article explores architectural and operational strategies to minimize the impact of EMI on the reliability of IIoT solutions.
Storing large volumes of IoT telemetry presents a significant budgetary challenge. This article proposes architectural and operational solutions for cost optimization through multi-tiered data storage and aggregation, balancing cost and analytical needs.
Selecting a standardized data model for building energy management is a critical architectural decision impacting semantic interoperability and analytics. We compare Brick Schema and Project Haystack, examining their approaches, tools, and ecosystems.
Sensor drift can lead to critical errors in industrial operations. This article explores architectural approaches for its detection, balancing reliability, cost, and implementation complexity.
Selecting the architectural approach for implementing buffering and store-and-forward mechanisms on edge gateways is crucial for ensuring continuous transmission of critical data in unstable network conditions.
Rapid IoT system deployment often leads to an accumulation of raw data that lacks business value without proper transformation. Underestimating data normalization and enrichment results in hidden costs and a lack of ROI.
Selecting the right LPWAN protocol is critical for the long-term efficiency and scalability of IoT solutions in urban infrastructure and utility management, especially with the phasing out of 2G/3G networks. This article provides a direct comparison of LoRaWAN, NB-IoT, and LTE-M, helping leaders make informed architectural decisions.
Unreliable IoT sensor data can lead to significant operational failures and financial losses. This article provides practical guidance on implementing robust data quality monitoring, validation, and maintenance strategies to ensure trustworthy operational insights and automation.
Facility managers and technical leaders face a strategic choice: transition from closed Building Management Systems (BMS) to flexible IoT platforms to unlock data and optimize operations. This decision requires balancing initial investment with long-term benefits.
Identifying anomalies in industrial IoT systems is just the beginning. Effective predictive maintenance requires integrating signals with diagnostics, work order management, and measurable feedback.
Digital twin projects often fall short of expected ROI due to poor data quality. Insufficient attention to semantic models, timestamp accuracy, quality flags, and data ownership turns the twin into a source of flawed decisions.
Objectively assessing the return on investment for HVAC system monitoring requires strict adherence to methodologies for baseline establishment, data normalization, and clear measurement boundaries. This guide offers a practical approach for professionals seeking reliable savings data.
