Digitalization of data is a process involving several steps. Executives who want to achieve the maximum benefit from their data collection processes need to learn, embrace, and implement digitalization in all its stages. (Part 2 of 3--Data Collection)
"Data is like garbage.You had better know what you are going to do with it before you collect it."--Attributed to author Mark Twain
- Power - Taking AI to edge devices at scale is blocked by power constraints. Edge devices are battery-powered, but assets have limited power sources in the field. Battery-powered edge devices must be capable of detecting data even in low-power modes. Servers need to be contacted at periodic intervals, and edge devices must have sufficient power reserves.
- Connection - Connectivity to the external cloud is not welcome and, in some cases, it is not even allowed. This constraint necessitates an independent solution, i.e. data collection to edge devices. Instead of bringing field data to AI, it is now possible to apply AI in the field on edge devices.
- Limited computational resources - There are no pre-processing or decision-making capabilities on edge devices that collect data from smart sensors. AI training of data sets is limited.
- Security - Data collection must also be a cyber-secure process. Critical infrastructure is frequently hacked because bad actors know that service providers are willing to pay ransom.
Once these challenges have been addressed, the benefits of edge AI computing are accrued quickly. Edge-embedded AI is not like cloud-based AI. Edge devices enable immediate adoption of AI tools. They provide the benefits of AI without cloud connectivity. Real-time awareness and action become a reality as events are detected in real-time. This stands in contrast to traditional IoT that does not provide any additional value over the raw data; does not improve data quality and integrity; and real-time detection is not available.