With projections of billions of connected devices by the beginning of the next decade, expectations for the IoT are decidedly high. Yet with mere weeks remaining in the current decade, those predictions are largely unrealized.
They’ll likely remain so until the IoT has a killer app to drive its adoption mainstream. Semantic graph model’s data integration will support the IoT’s impending ubiquity by providing the killer enabler to automate the data preparation necessary for widespread adoption.
Implementing the IoT at global scale requires semantic interoperability for inter-machine communication, which is achievable with the W3C’s semantic standards. These standards integrate machine data with enterprise data to overcome the point-to-point architectures limiting large IoT operations. They enable the IoT’s collaborative community of data points to be aggregated alongside and linked to typical enterprise sources that can provide significant synergistic value and often crucial context to the streamed data.
The result is increasingly sophisticated use cases like Digital Twins, Digital Threads, and information on demand via Enterprise Information-as-a-Service. Digital Twins are 3-D models transmitting data about the state and behaviors of the real world; Digital Threads illustrate the value chains of assets throughout their lifecycles. Semantic consistency for these and other IoT applications supports the scale and interoperability necessary to actualize its projected adoption rates.
IoT data integration presents several data management difficulties. Most IoT datasets feature proprietary formats and semi-structured or even unstructured data, which can be cumbersome for managing with typical relational techniques. These datasets are also characterized by complicated, high-dimensional data models, multiple data formats, and extreme low latency for fleeting business opportunities. The integration challenges inherent to these data streams often result in silos and manually intensive data preparation activities circumscribing enterprise deployments and adoption rates.
Still, the predictive and prescriptive analytics for equipment asset monitoring in the Industrial Internet, for example, yield considerable benefits at scale. Predictive maintenance on wind-powered energy assets like connected windmills reduces downtime, extends equipment life, and decreases the costs of inoperability and new equipment expenses. However, integrating these sources with other, perhaps more historical enterprise data delivers even greater business value. Organizations can ascertain in what conditions they perform best to inform future product development, placement, and optimization based on their actual performances.
The semantic graph model redresses IoT integration troubles to facilitate these use cases and more. It solves the data modeling challenges with standardized models to which all data conform. These ontologies naturally expand to accommodate new data sources or requirements. When used with uniform vocabularies and taxonomies for machine-understandable data, this approach enables organizations to complete their data preparation work upfront for real-time integrations.
With this machine intelligence, IoT data becomes just another source to quickly integrate with enterprise data for sophisticated use cases encouraging adoption. In manufacturing, organizations can integrate real-time customer usage data of connected vehicles or appliances to impact product development, decrease research and development cycle time, and offer new services—all based on integrating the same IoT data.
Integration possibilities could include Enterprise Information-as-a-Service, in which users can ask any question of the enterprise via the cloud and get holistic answers from the aggregation and integration of all data sources available. This process could revolutionize clinical trials in the pharmaceutical industry. Envision the revenue generated from selling this information, or it's capacity for enhancing employee efficiency across the organization in retail and other industries.
Timely data integrations are constraining the IoT’s adoption rates. However, semantic graph models rapidly blend IoT data with enterprise data to overcome this albatross. They support utilitarian use cases that expand IoT adoption rates across verticals. With this approach, organizations can break free of the rigors of managing IoT data to realize a new future in which these sources are increasingly influential.