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.
Big data became a commonly used term over the past decade due to the convergence of everywhere access, cloud databases, IoT technology, and much more. But with all of these streams of data entering into an operation, it still needs to be processed for consumption. Machine learning has revolutionized this with automated augmenting, enhancing, healing, and enrichment of data. This saves on tasks such as standardizing formats, identifying outliers, masking sensitive data, and much more. With machine learning, major repetitive steps can become automated for faster, more accurate results, enabling data scientists to focus their time and energy elsewhere.
No matter how well they're trained or how much experience they have, data scientists and analysts can only move at human speeds. Machine-learning models have the capability to handle simpler analysis and data-set processing at speeds impossible by data science teams. Because of this larger scope and faster speed, machine learning can identify patterns that human teams may overlook. On the same level, machine learning can examine relationships and build suggestions for further analysis that may not have been possible on a manual level.
Machine learning powers search capabilities to higher levels, both with the actual search function and output. Under machine learning, algorithms can be trained to factor in specific parameters while running forecasting, trending, clustering, and correlation analysis. The result improves both power and flexibility, from improving the accuracy (and thus, engagement) of recommendation engines to offering greater customization options to deriving new types of forecasting or exceptions.
The range of machine learning functions includes natural language processing (NLP), which creates an evolving model to understand human language. This is the engine that drives voice recognition, which itself has many applications across business, accessibility, and day-to-day life. The more an NLP algorithm learns, the greater its accuracy, creating enabling interactions simply through speech. This is also related to natural-language generation (NLG), which can be used to auto-generate descriptions and reports based on insights from data.
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.