Drone Technology: What We’ve Learned In The Lab So Far

David Cruickshank

Part 4 in the 4-part Co-Innovation Series.

Having reached Part 4 of this series, I’m looking at the source material and realize I could have written ten chapters, but I’m no Ken Burns. My goal is to share examples of active co-innovation projects – initiatives that might involve some of your largest suppliers and technology partners. I hope to provide a source of inspiration, and perhaps even spark an opportunity to help your own IT teams gain insights from machine data with like-minded collaborators.

I posed this question in closing Part 3, where I looked at the case for drones: Does it make sense for your business to collect data using drone technology? Does it make sense for your firm to own and manage its own drones? Is that so different from managing other assets outputting sensor data?

Consider that adoption of unproven technology (unproven as integrated into your business) needs to weigh many different factors, from the investment (time, money, effort, resources) to the timing (when to do what), to the potential deployment (where and how). Here, I’ll describe what challenges and opportunities we hear about as we pursue project work featuring the use of drones and drone data in our own lab.

The challenges inherent in going it alone

Companies making a go of it in-house should begin by expecting to encounter the unexpected – and that’s not always weather-related or due to electromechanical failure. For example, we’ve heard from colleagues at mines in Australia describing how local hawk populations attack drones at work. How do you solve a problem like that? Budget for a loss rate of drone assets each year? Fly extra drones for in flight security? Budget for the expense of more drones or drone parts?

Drones can execute a variety of sensor payloads. Options abound for companies that must monitor conditions of large material assets used in construction projects like sand and gravel. Consistent inspection of these materials for the purposes of insurance-claim management is a single example of an existing business process that can be enhanced in ways to help save money (avoid lost or denied claims) using a cost-effective, Web-delivered service comprised of drone-sourced data. Through our own investigation of developing IoT-based solutions that draw from drone-sourced data, the use cases in both construction and mining have both well-understood and emerging business needs like:

  • Asset inspection (roofs, bridges, land, materials, etc.)
  • Site mapping (GIS) for large landscapes often for data comparison over time
  • Surveys that require levels and volumes

You can have target applications in mind for using drone data that satisfy specific end-user or business-unit needs. Yet your approach will differ if you are looking to integrate those data feeds into an enterprise-based back end or with applications run only from the cloud. We find such use cases now being described from the field and not just the lab, which encourages adoption. The capacity to absorb new capability and sufficiently process, analyze, and then visualize this data to the benefit of the company is crucial. Collecting this data represents only a portion of a complete solution. Similarly, what data you collect to support specific actions, and how often you collect data are additional questions that surface.

Our observations suggest that a combination of automation and managed services, all delivered through the cloud, has resulted in firms having bought into the technology for maybe a single yearly audit. They are now more often adding missions and deriving additional value from it. There can be similar uptake among firms that run drone operations internally as they continue to master skills and optimize data collection and ingest process; yet measuring operational costs and efficiency will differ.

Avoiding risk through collaboration

IoT has clearly emerged as imperative to competitiveness. It is therefore becoming essential to accept the risks inherent to adoption of still-nascent technologies, like using commercial drones. Much of the underlying technology, be it flight automation or data streaming into the cloud, are now well-proven. Yet as you begin to understand how all this newly harnessed capability will integrate with your business processes, you might feel uncomfortable with your chances to succeed.

With the speed at which technology advances, gaining the knowledge required to apply these technologies to solve problems can become overwhelming to a single team of innovators. Yet to pursue complex problem-solving through collaboration with others who may be further along the path takes full advantage of prior learning, as well as sharing in the risks when we innovate – like trying to collect data without provoking birds of prey.

Strength in numbers

When the skills and resource constraints within your team present a challenge to successfully execute a digital transformation project, your approach likely includes collaborating with other business units and teams. Working with a larger community of stakeholders and subject matter experts across the organization is often a good way to approach important new initiatives. You can possibly extend your knowledge further. As you consider the larger physical systems and processes where you seek to draw useful information from connected assets and devices, consider the compute, storage, and network resources that your solution will tie into. Suppliers and providers of these systems are often also involved in project work with others from the ecosystems in which they engage. You may want to explore working with those vendors or other companies that can richly contribute to your own efforts, reduce or share risk, and achieve goals sooner.

The use of not-at-rest sensors is a thing for me. I like the topic, and I plan to return to it in future posts, drawing from ongoing project work done in our lab exploring software and services at the edge of the enterprise systems and the cloud.

For more from this series, click here.


David Cruickshank

About David Cruickshank

David Cruickshank is senior director for strategy and operations for the SAP Co-Innovation Lab. He leads the lab's efforts in Silicon Valley to enable ecosystem-driven co-innovation between SAP, its partners, and customers. Additionally, he manages all operational aspects necessary to run a multimillion-dollar data center to provision private cloud infrastructures to deliver productive SAP landscapes consumed by co-innovation projects seeking a faster track to market for commercially successful innovations.