Intelligent Systems are ubiquitous in our daily lives, from facial recognition in cameras to product recommendations in e-commerce. These modern conveniences and others are enabled through machine learning. Machine learning is the process by which behavior of an application is controlled based on prior experience vs rules that are dictated by hand. Recent polls estimate that up to 50% of businesses are now using machine learning in some capacity, with an additional 22% now road mapping projects which incorporate machine learning. Fueling this growth is the massive amount of data being produced by today’s connected systems which provide valuable insights for decision making. In addition, companies such as Amazon, Google and Microsoft provide API’s that make machine learning algorithms more accessible.
Learning style
Machine learning algorithms can be categorized by learning style; supervised and unsupervised. Supervised learners will use training data associated with a label to train the machine and yield the desired output. A supervised learning algorithm is used when the output classes are already known, such as whether an email is spam or not. An unsupervised learner, on the other hand, will not have any provided labels and determine classes through finding structures or patterns in the data.
Machine learning in practice
Choosing the right algorithm is the task of a Data Scientist whose responsibility it is to find insights on the data and make decisions on which learner is appropriate. Service Objects takes the guess work out of finding the right algorithm in DOTS Name Validation. In its latest operation, NameInfoV2, classification of a name is performed to determine whether a provided name is a person, business or unknown. Using sophisticated learners coupled with a proprietary database of millions of names yields a highly accurate system for classifying and scoring a name. This helps clients improve efficiency in sales and marketing operations by knowing whether a provided name is in fact a person or some entity.
Adapting to future needs
Building intelligent applications that can adapt to the needs of our users is paramount to ensuring our clients are receiving the highest quality data. With more intelligent applications on the roadmap, Service Objects edges closer to realizing its mission of ensuring every contact data point is as accurate as possible.
