IoT—as helpful as the technology has been in the arena of collecting, aggregating, analyzing, and using large-scale data—has introduced a myriad of challenges to enterprises. As businesses struggle to provide the scalability and speed required to process and analyze all the data streaming in from IoT sensors and devices, the benefit of IoT is rapidly losing its footing. The proliferation of AI and new types of databases, data platforms, tools, and services are further amplifying the obstacles that IoT data is inducing.
Experts joined DBTA’s roundtable webinar, Bringing the Power of AI to IoT: Data Strategies and Enabling Technologies, to offer IT leaders and data teams a variety of technologies and best practices to truly set up enterprise IoT strategies for success.
Shiva Nathan, CEO and founder of Onymos, began by explaining what Onymos offers: pre-built software commodities, or “features,” that can be combined to build custom solutions for web, mobile, and IoT.
Additionally, Onymos licenses the entire source code out, eliminating vendor lock-in and driving greater verification and customization. Operating with a “no-data” architecture, Onymos eliminates vendor compromise and delivers direct connectivity to the customer’s own cloud or on-prem infrastructure.
Nathan then jumped into a medical use case that necessitated Onymos’ capabilities, where analytics, research, and scalability converged to resolve this enterprise’s IoT challenge.
David Brader, senior practice lead at Domo, offered a provocative statement, “Data, in it of itself, is useless until the point that we turn it into action.”
He further added that IoT is just another source of data, ultimately applying to his statement of actionability. The process of operationalizing IoT data is exactly the same as any other source, meeting the data where it’s at and bringing it together, joining it, automating those processes, and deploying it into analysis.
Brader then added that there are several critical things to keep in mind, including:
- The existence of multiple concurrent cycles (immediate/short, mid, long) going on
- Individual data points lose value quickly over time
- Take advantage of IoT both as a point of data and as it is combined with other parts of the business
- Easily build reliable feeds
- Efficiently store scale and volume
- Combine individual and critical associated data rapidly
- Run models and analyses
- Provide visualizations on both real-time data and the fully aggregated data
Mark Gamble, product and solutions marketing director at Couchbase, explored how Couchbase’s architecture fundamentally suits IoT use cases. Couchbase runs on the cloud, at the edge, and on the device, automatically synching data between each of these locations.
Couchbase is extensively used for Cloud-to-Edge IoT, according to Gamble, ranging in application from robotic vacuums and electric scooters to interconnected surgical tools and wearable IoT devices.
Gamble introduced the following Couchbase technologies that support IoT use cases:
- Couchbase Capella Columnar Services for aggregation of data across large data sets
- Couchbase Lite Predictive Query API, an embedded database for mobile and IoT devices that runs ML models within mobile apps
For an in-depth review of solutions for solving IoT challenges, featuring use cases, examples, and more, you can view an archived version of the webinar here.