What are examples of machine learning on an enterprise scale? The last decade's growth of machine learning has been a significant leap forward for companies and organizations, accelerating data-driven insights and powering artificial intelligence for smarter decisions. Data now arrives in heavy volumes from countless sources: Internet of Things devices, social media feeds, and much more. Such heavy volumes of data are untenable to manually analyze, but machine learning turns this deluge into something manageable and actionable for easy integration into organizational processes.
Enterprises of all sizes use machine learning to improve their functionality. When a search engine returns personalized results based on a user profile, that's machine learning. When a shopping site loads recommendations based on a customer’s product purchases and views, that's machine learning. When your phone auto-corrects a typo in your text messages, that's machine learning.
From natural language processing to finding anomalies in massive data sets, machine learning algorithms learn like the human brain, but with the technical accuracy of a computer. Rather than a set of if/then rules or process guidelines, machine learning identifies patterns and anomalies while learning the context around those–the more volume, the more to learn from.
Machine-learning algorithms and models are the engines that drive this process—but what can enterprises exactly do with them? It's easy to consider recommendations from an ecommerce website or a streaming service, but what about on the level of a B2B company or internal operations? Let's consider four machine-learning examples that demonstrate the breadth of machine learning's capabilities.
Now that we've established four general use cases for machine learning, let's put this into a real-world example. Consider the customer service department of any company. Machine learning can analyze every transaction within the database and create a customer profile based on user history to create a specialized outreach program with individual preferences. Machine learning can identify the different paths through here based on crunching heavy volumes of data and analyzing the patterns involved.
For example, the machine learning algorithm may notice that people who make purchases early in the morning are also more prone to a specific type of product. With this, targeted groups of customers can be sent special offers when this product category is on sale or when inventory is low. Many different types of pattern correlation can be determined with machine learning and further applied to further engage customers, create incentives, and maximize retention.
To learn more about what machine learning can do–and how Oracle makes it easy–discover Oracle Machine Learning is used to solve complex data-driven problems.