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.
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.
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