Machine learning is everywhere. At home, it helps power personalized shopping apps, suggests personalized entertainment experiences, manages and monitors self-driving cars, supports virtual assistants, and improves navigation.
At the office, it helps businesses develop the next best offer, recruit top-notch candidates, detect fraud, automate supply chains, and boost data center efficiency. Yet, corporate finance leaders are asking deeper questions which require more advanced analytics systems.
"Why can you ask your mobile phone for directions to find the nearest restaurant but you can’t ask your system how revenues are trending in Italy?" Oracle Vice President of Product Strategy for Big Data Analytics, Rich Clayton said during a recent Financial Executives International (FEI) webcast. "Why is that your systems don't understand your processes? Why do you spend so much time explaining simple variances when much can be automated?"
Clayton discussed how machine learning is transforming businesses and specifically the finance departments. Clayton collected tried-and-true use cases for machine learning in Finance over the year and knows from his experience how best to prepare your organization for big changes ahead in a way that seems very simple on the surface.
Clayton says machine learning helps financial service leaders tackle three areas at once:
"Rather than hiring expensive programmers to write ETL or data loading programs, machines will recommend how to combine data," Clayton said.
So how does this work in the real world? Clayton suggested a couple examples:
Because machine learning techniques are designed to learn as they go, Clayton suggests that business analytics designers look at Oracle's suite of analytics products to help with autonomous data discovery to help guide users toward areas of interest they may have passed over. Self-learning contextual insights anticipate questions and infuse data-based insights into daily activities.
And what machine learning discussion would be completed without mentioning voice-activated analytics. If words can be expressed as values, interpreting a semantic layer of data through natural language processing can enable on-the-fly queries and auto-complete expressions on any device with a microphone.