The digital transformation market is growing rapidly and is set to reach a volume of one trillion dollars by 2025. Machine learning is also quickly gaining traction and is projected to grow to volumes amounting to trillions of dollars over the next couple of decades. Such market dynamics are resulting in an immense demand for appropriate solutions, and the development of a supply to match.

But the insufficiency of data is one of the main reasons why both machine learning and digitization projects fail. Data starvation puts multimillion dollar investments into digitization projects at risk. The availability of data is often being overestimated, leading to breakdowns in planned processes that were built on estimations and predictions, rather than preliminary analysis during project development. Such a state of affairs is considered unacceptable by both investors and the market that requires wholesome datasets to support operations.

Source: Everynet

Hardware is taking up a bigger role in software-related processes, as machine learning algorithms and digital transformation software need smart sensors to gain access to the real world. The availability of this vital link, and the overseers ensuring the authenticity of the data, is the key to providing constant data streams for improving machine learning efficiency.

The main challenge for data collection arises from the fact that computational efficiency has improved exponentially over the past decades, whereas wireless transmission continues to be energy-intensive and expensive. Historically, wireless communications are moving towards higher bandwidths for obvious reasons. Currently, 4G features an amazing 300 megabit per second download speed, which is already high enough to provide the necessary volumes of data transmission needed for next-gen AI systems to operate.

High throughput devices are not cheap

But not all devices require such an enormous amount of throughput. Billions of devices, such as water and energy meters, pallet and roll cage trackers, predictive maintenance sensors, and many other less complicated types of hardware, only need to report a very small amount of data several times a day. This makes the use of 4G and higher bandwidths an inefficient means of data transmission.

High throughput devices are not cheap, as it imposes certain requirements on the device’s battery consumption, antenna design, processing power and results in high costs for producing and maintaining the device.

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Continue reading: https://www.ept.ca/features/low-cost-sensor-data-acquisition-for-machine-learning-and-digitization-projects/

Source: www.ept.ca