Machine learning has been a paradigm shift for companies and organizations, providing opportunities to answer their business questions at previously unachievable speeds. With incoming heavy volumes of data from a growing number of data sources—ranging from social media feeds to Internet of Things (IoT) devices—machine learning provides a cost-effective means of both deriving insights from data and simply processing it in a resource efficient manner.
While organizations internally handle the application of machine learning within their framework, its impact resonates into the everyday world. Examples of machine learning include Facebook ads ranking, Amazon purchase recommendations, suspicious activity detection from closed-circuit televisions, and even the technology behind Apple's Siri voice assistant's natural language processing.
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Machine learning’s capabilities and accuracy are based on the amount of data it processes. The more data, the more opportunities to refine its decision-making. In a previous post, the analogy of learning to read was used:
Think about how you learned to read. You didn’t sit down and learn spelling and grammar before picking up your first book. You read simple books, graduating to more complex ones over time. You actually learned the rules (and exceptions) of spelling and grammar from your reading. Put another way, you processed a lot of data and learned from it.
As Bernard Marr, author and Advanced Performance Institute CEO, points out, the output of machine learning is almost more important than the process itself. Different algorithms are produced that can handle classifications, estimations, clustering, and regressions, to name a few.
"Machine learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations," says Marr. "They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood."
So what can you do with machine learning? The all-encompassing answer is “identify patterns in massive volumes of data.” Of course, that’s like saying you can travel at high elevations with an airplane. The trick is how you use that ability. Building algorithms and refining models, as well as increasing reliable data sources, are all part of the process of optimizing machine learning. In general, this breaks down to increasing efficiency and obtaining data-driven insights.
Here are four examples of elements every organization should include in their big data strategy:
Intelligent search: With machine learning, seamless searches with natural language queries offer the ability to search all aggregated data sources. When used with an augmented analytics platform, these results can be further output into relevant visualizations based on the search. This enables insights such as forecasting, clustering, trending, and building correlation analysis so that when you run data models, you can see the results and then train machine learning models using specific parameters. For example, you could improve recommendation engines ("This is the best ad to show this user now."), or provide personalization ("Would you like to see this report on Monday mornings?"), as well as mention forecasting ("I expect March sales to be $143 million based on previous estimates."), and exceptions ("I expected March sales to be lower based on estimates.").
Smart data discovery: Machine learning models can automatically analyze and generate explanations of data, including key drivers of the results. Exploratory machine learning sifts through the data, then analyzes patterns within it. If it’s connected to a graph analytics platform, this opens the door to new insights based on relationships. In addition to producing those results, machine learning can create further suggestions based on user history of interactions to speed up discovery and insights.
Smart data preparation: At the heart of successful big data usage lies data preparation, and machine learning can significantly optimize this: augment, enhance, heal, and create richer data, all leading to improved business insights and sharper understanding. From a task perspective, data preparation involves a wide range of things and differs depending on a dataset’s specific needs. Possible tasks include removing outliers, standardizing data formats, filling in missing values, and masking sensitive data. Machine learning can automate this process with first-cut suggestions, and the more it repeats the task, the faster and more accurately it will be able to operate.
Natural language: Natural-language processing (NLP) is a subset of machine learning that focuses on the system being able to understand and communicate via human language rather than preprogrammed interfaces. For example, NLP can be used to power queries via voice recognition or provide sentiment analysis of tracked social media posts. For the former, such a tool creates a truly self-service experience, where users can search in written text or via speech using NLP. Another element of this is natural-language generation (NLG), which converts those insights into annotations and textual descriptions.
Machine learning has profound potential within companies and across every function: from finance, to marketing, to service, to sales, to manufacturing. Let’s take a look at three different industries getting the most out of machine learning:
Financial services: Banks and financial services can use machine learning for many different applications. However, this industry may see its biggest application in detecting fraudulent transactions. By being able to sift through data quickly, identifying anomalies and ties to known fraudsters, these types of organizations can react and execute faster to minimize fraudulent transactions.
Customer service: Regardless of industry, machine learning can transform the way organizations handle customer service and retention. With machine learning analyzing a user’s account history, automated interactions and outreach can generate offers, provide check-ins, and incentivize deals, all at a customized level designed to maximize conversion.
Logistics and supply chain: Now more than ever, real-time updates of supply chain are critical to keeping our world moving. Machine learning can take in real-time tracking data from different pieces of the supply chain and automate changes to keep various parties informed, while taking smart opportunities to evolve workflows and minimize lost time or risk.
These three examples are just the tip of the iceberg when it comes to the possibilities of machine learning. Oracle Analytics provides a complete analytics platform powered by machine learning. Featuring all of the elements mentioned above and more, Oracle Analytics delivers cutting-edge augmented analytics, business intelligence, and data visualizations in an easy-to-use package. Learn more about Oracle Analytics.
If you want to begin your business journey and get started with machine learning, we are providing a free eBook published by O'Reilly that you will want to check out: Getting Started with Machine Learning in the Cloud and and don't forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox. Also, follow us on Twitter @OracleBigData.