Complexity Economics Engineering

Data-driven Business Models for Military Vehicles

To outsource or not to outsource data collection, management and analysis for military platforms? This is the question we will attempt to answer in this short blog.

Data-driven business models hinge on products that bring manufacturers and users together in mutually beneficial ways. Independently of market sector, almost all new products contain plenty of software and generate large amounts of data collected by a myriad of sensors. This data reflects the functioning of a product as well as its interaction with the environment. Who understands the data understands the product. The more complex the product the more strategic is the data as it helps understand the product better, improve its design and identify its vulnerabilities.

This last point is crucial. High complexity induces fragility and therefore exposes software-heavy products and systems to cyber-attacks. For this reason, it is paramount that manufacturers of complex military equipment and systems do not surrender this data and its management to third parties or providers. While this may be a decision based exclusively on economic considerations, it can turn out to be a grave mistake. The data generated by combat platforms is reflection of the most intimate know-how involved in their engineering development, their functioning and intricacies. Handing over data collection, management and analysis is not a sound business strategy. If, however, such a path should be pursued, equipment manufacturers will relegate themselves to a mere function of production and assembly of hardware components, leaving the “intelligence” component in the hands of others. Consequently, this kind of know how must remain within the equipment manufacturer.

 Data-driven business models are “multi-dimensional” in nature and may, in the future, develop along paths that today may not be visible or obvious. However, there is a simpler reason to hang on to data generated by your product. If an equipment manufacturer collects, manages and produces data intelligence for his products, his customers will not be able to resort to other data management companies. Data management as a service is a business of the future and will one day become a commodity. This will mean that those who produce the data but hand it over to others for intelligence extraction will be less motivated to understand it, loosing progressively the control of their own products. He who analyzes his own data understands it and the system that generated it. Who lets others analyze data originating from his products, is, in effect, handing over control of these products to third parties. Such companies are destined to disappear. Hardware is easy to replace, intelligence and know how are not.

Future mission scenarios will not only involve vehicle-terrain interaction. As the “digital landscape” of a mission becomes more complex, vehicles will be exposed to and will collect huge amounts of data reflecting this landscape. The interaction between the vehicle and its environment will, inevitably, become more complex. The application of QCM in an edge-computing (on-board) context can help manage a mission better and identify threats and vulnerabilities from a combined “vehicle+environment” perspective. As more sophisticated navigation and weapon systems are incorporated into future military platforms, this integrated approach to data collection and analysis is mandatory, it is not a matter of business strategy.

www.ontonix.com

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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