The amount of sensors has grown so much that 20 years ago, computer scientists had to design a new internet protocol foreseeing that the previous would exhaust the amount of connected devices. This is the Internet of Things or IoT world - with sensors connected within the enterprise and publicly creating a wave of noisy, loud, fast, and voluminous data requiring new architectures and patterns in edge computing, cleaning algorithms, storage, notification triggers, and integration solutions. This is IoT & Sensor Data.
Beyond Big Data, sensor data – remote or IoT, big and/or fast, requires edge and centralized new data flow patterns to trigger the real-time monitoring to support operation and decision making at the data-driven organization.
Our clients in transportation and science are leading the way in sensor integration and knowledge products.
- Agile Applicatoin Integration with IoT/Sensor Data and Enhancement (Development)
- Real-Time Data Hub Architecture and Development (Design and Dev)
- Remote Sensing Large Portfolio Rationalization Tools and Consulting (Strategy)
- Real-Time Data Lifecycle Governance Development and Architecture Advisory (Strategy)
Top 5 Challenges IoT Data Face
- IoT feeds require both high performance computing and storage solutions to avoid the data lakes turning into data marshes.
- Organizations have increasingly started looking at storing big data collected using IoT devices rather than rely on vendors.
- IoT devices may provide unstructured data formats, may be dirty, and require edge filtering, adaptive transformation, and other deferred load solution as area matures.
- Data hubs are needed to handle IoT, remote sensing, and other massive fast or big feeds.
- No current toolsets are available to easily assess the effectiveness of IoT sensor project installation projects.