Machine Learning in the Oracle Analytics Cloud

Data Science Capabilities at Your Fingertips

Most analytics tools are only capable of reporting on past events and showing trends. While helpful, it’s not actionable. Historically, predictive analytics required the specialized skills of data scientists and advanced developers to discover patterns, trends, and correlations in raw data. Then came Machine Learning (ML). Once considered a “nice to have” due to its expense and lack of clear added value, ML is now available as an integral component of the Oracle Analytics Cloud (OAC). It’s user and data friendly, as well as visually impactful—and you don’t have to be a data scientist to use it! As the barriers to realizing the benefits of ML have fallen, it’s time to revisit how ML can help you.

What is Machine Learning?

ML is an artificial intelligence that provides systems the ability to automatically “learn” and improve from real-world interactions and experiences without a pre-determined model. ML algorithms access and analyze data to find natural patterns, gain insights, and predict future outcomes. The results of ML can be used to improve organizational performance through proactive decision-making. All of us experience ML every day through applications such as Siri, weather predictors, and those clever browser ads.

Why Use Oracle Analytics Cloud’s Machine Learning Capabilities?
As a business executive, manager, or analyst you might be wondering, “Machine Learning is very interesting, but what does it have to do with me?” OAC’s ML component evaluates quantifiable data and transactions associated with any business process and delivers predictive outcomes, providing an authoritative basis for business users to create actionable plans and make proactive management decisions. OAC also includes a data visualization tool that conveys results graphically, thereby illustrating patterns, trends, and correlations that would be more difficult to see in traditional reporting outputs.

ML involves real people, in real scenarios. Let’s consider one common scenario involving employee attrition:
“I work in HR and manage talent acquisition. Recently, there has been an increase in employee turnover that is putting more pressure on our recruiting team and costing the organization money in lost productivity. I need to gain more insight into this situation, including what types of employees are leaving the company and why. I would also like to better understand which current employees are at risk of leaving and target them with effective retention strategies before they leave.”

How does it work?

Many questions regarding attrition can be answered using ML: How do we find the best people? What attributes should we be screening for in our recruits in order to reduce turnover? What are the drivers of retention? To answer these questions and develop winning strategies, programs, and processes, ML creates a model that “learns” from your HR’s historical data. The historical data set is used to teach the model about the factors that drive attrition within your organization. Then, based on those drivers, ML can predict future attrition for the organization and identify employees that are at risk of leaving by applying a risk score. Similarly, for recruiting new talent, ML can identify a set of attributes with which to pursue candidates that are indicators of retention.
The business challenges that ML can tackle are endless. Data science can be applied to improve performance within any organization. And now with the OAC’s ML capabilities, it is easier than ever to transform your business. What challenges are you facing? Contact us today to see how Sierra-Cedar can help.