At Textron Systems, digital transformation is core to our strategy, not only from the perspective of how we execute our business, but also how our products and services are differentiated. A key enabler to digital transformation is leveraging an ecosystem of ’as-a-service’ offerings that bring the necessary capabilities and operational performance to our solutions. Beyond the elasticity that these architectures provide, they also enable access to a marketplace of services like machine learning, advanced analytics and proven algorithms. Machine learning represents an opportunity for commercial, civil and government organizations alike to drive powerful economies such as cost reduction, productivity optimization and product differentiation. To achieve these benefits, organizations must implement machine learning with an eye toward avoiding its most common failure modes.
First and foremost, data is key when it comes to machine learning. The more data you have for the advanced algorithms to learn from, the higher the probability of driving desired and meaningful insights. There are inherent challenges to perfecting machine learning insights, starting with the cleanliness, or quality, and availability of good data sets to mine, or “learn” from. There are several applied examples where biases in data have skewed the results and accuracy of data insights through machine learning concepts. Training an algorithm is like training a body; the healthier the inputs, the better the chances of improved performance.