Proving the value of analytics on the edge

Proving the value of analytics on the edge

By pushing more analytic capabilities where data is collected, organisations are achieving greater responsiveness and efficiency. Here are three edge analytics success stories.

Credit: Gorodenkoff / Shutterstock

To help bolster its data analytics operations overall and at the edge, the city government is developing a data analytics group as an offshoot of the IT department. The Office of Data and Analytics will drive how data is governed and used within the organisation, Sherwood says. “We see lots of opportunities with many new technologies coming onto the market,” he says. “Our main goal has focused on building our team and working on the governance and the curation of data sources.”

The benefits of the early stages of the deployment include enabling traffic intersections to adjust signaling timing based on actual traffic flows. This “is a positive indication of how these new technologies can really help create solutions which benefit everyone in the community,” Sherwood says.

With AI now playing a role in some of the city’s newest smart systems, the need for edge computing and analytics at the edge will only grow, Sherwood says.

Most of the challenges of edge computing involve deciding on what processing to do at the edge, and how the data is stored and for what time period, Sherwood says. “We are still working through this process, and the more systems and pilot projects we undertake, the more we learn about the art of the possible and reality,” he says.

Earth observation

Satellogic, a company that provides commercial and government customers with high-frequency, high-resolution geospatial

imagery and analytics, is taking the concept of edge computing to the extreme.

The company, which manufactures its own satellites, is working with several partners, including big data analytics software provider Palantir Technologies, to move its data analytics to the edge of its network — on board its satellites.

Satellogic is building and operating a constellation of satellites that collect multispectral and hyperspectral images as well as full-motion video, says Gerardo Richarte, CTO and co-founder of the company.

“When designing and building our first satellites — over 10 years ago — we knew that we needed to make decisions at the edge,” Richarte says. “Our first satellites flew with hardware and software onboard to take advantage of edge computing, and being vertically integrated meant we could be highly agile in developing and testing new technologies in orbit.”

Initially the satellite-based computing work was internal and experimental, Richarte says. “As our customer base expanded, we started working with customers to stream their image-processing algorithms in orbit,” he says.

Edge computing unlocks three major enhancements to Satellogic customers’ experience, Richarte says. “First, edge computing will allow us to provide customers with real-time alerts,” he says. “The closer we are to the source of information, the sooner we can generate and dispatch the alerts each of our customers requires.”

Second, the company can take actions at the edge, including retasking. “When a particular object of interest is flagged by an algorithm, we can instantly retask a satellite to lock onto, track that object, or enable a different product like a full-motion video capture,” Richarte says.

An algorithm might trigger a satellite to instantly turn on a particular payload to capture data that would have otherwise been missed. Full-motion video (FMV), for example, “is an excellent application for edge AI, as it can prove critical for certain kinds of decision-making,” Richarte says. “But [it’s] far too data-intensive to run continuously.” Edge AI algorithms programmed according to precise customer needs can define the parameters for leveraging Satellogic’s FMV along with other data and cost-intensive payloads, he says.

Finally, edge computing can be leveraged to prioritise data transport. “Remote connections from orbit to ground have limited bandwidth, and data download may take longer than required by certain applications,” Richarte says. “By running satellite data through algorithms at the edge, we can orchestrate data transport according to each individual customer’s priorities and goals.”

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