News and Views: Drive Smart Decisions with Cloud Analytics, Machine Learning and More

Take Business Analytics to the Next Level with Machine Learning

Michael Singer
Director, Product Marketing, Oracle Analytics

The convergence of big data, analytics, data science, and cloud is creating a need by business managers to optimize their investments with a comprehensive way to derive value from data.

Successful companies know how to strategy that includes applying machine learning to their processes, so they can automate and speed their time to decision.

For example, companies like yours use machine learning in the following ways:

  • A large bank used machine learning to analyze its collection activities and learned it could eliminate more than 40 percent of customer calls with better outcomes.
  • A global retailer used advanced machine learning to forecast customer demand cutting forecast error in half.
  • A telecom company found that its machine learning yielded a 75x reduction in "false alarms" for churn and instead focused its resources on those truly at risk of leaving.

To better illustrate the business applications of machine learning and how it affects you, Oracle sponsored a webcast entitled: "Where Will Machine Learning Take You? See Your Future with Oracle Analytics."

Rich Clayton, VP Product Strategy, Oracle Analytics and Mike Lehmann, VP of Product Marketing for Oracle Big Data and Machine Learning lead you through the process, where you can hear how Oracle Analytics uses machine learning to help you understand more, faster and understand how to get started on that path immediately.

Every industry has been transformed by applying machine learning to its data analytics strategy, including automotive, healthcare, media, energy, communications, and government. Likewise, ML can be applied to all business divisions such as HR, finance, sales, marketing, and IT. Whether it's investigating customer churn, text sentiment analysis, forecasting and modeling, data discovery and auditing, or transactional data extraction and transformation, Clayton notes that machine learning enables better business data visibility.

"Traditionally what we see is that people not being able to work together," Clayton says. "What adding machine learning to Oracle Analytics Cloud does is ultimately help them organize their work, build, train and deploy these data models. It's a collaboration tool whose value is that it accelerates the process and allows different parts of the business to collaborate, giving you better quality and models for you to deploy."

One of the barriers to has been the multiple layers that data must pass through before it is processed, and its value derived. There is an extraction layer, data blending, modeling, aggregating and publishing long before there is a discovery mode.

Clayton argues that by automating and embedding machine learning allows for faster time to decision.

"There is a foundational opportunity by taking some of these components and embedding it into a value chain from an analytics perspective," Clayton says.

A typical finance department is routinely burdened by repeating a variance analysis process—a comparison between what is actual and what was forecast. It's a low-cognitive application that screams for assistance of machine learning, Clayton notes.

"By embedding machine learning, finance can work faster and smarter and pick up only where the machine left off," Clayton says.

To hear the entire conversation, register for the webcast and see where your future will go with machine learning as part of your data analytics strategy. For more information about Oracle Analytics Cloud, visit our website.

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