Rapid growth in available data has increased the amount of time and sophistication necessary to make sense of enormous data. Making this data actionable has become its own industry. Demand for artificial intelligence (AI) is burgeoning quickly. According to Statista market research, the market for AI will increase from $9.5 billion in 2018 to $118.6 billion by 2025 in the US alone. How long ago did point of care first responders only dream of using AI-driven hand-held ultrasound to assess injury; AI-driven pattern detection systems now diagnose and inform emergency personnel when abnormalities are detected and suggest next course of action. Similarly, Salesforce Einstein Analytics Platform is the leading innovation in business intelligence. While Salesforce CRM is the leading software in increasing business data visibility, Einstein Analytics was developed specifically to expand business user-centric access to analytic apps leveraging algorithms to identify trends too complicated to view or detect otherwise, diagnose shortfalls, and suggest next actions.
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Francis is an 8X certified Salesforce Einstein Analytics and Discovery Senior Consultant at Apps Associates. He is a Ph.D. scientist who has authored academic scientific publications in the fields of lipid membrane biophysics and molecular and cell biology in conjunction with Indiana University, Purdue University, University of Illinois, Duke University School of Medicine, and Washington University School of Medicine in St. Louis. He has reported on data-driven clinical research serving as regulatory scientist, director, and medical writing consultant for the biological device, pharmaceutical, and wellness industries. His work in science has shifted from generating and reporting data to finding better ways to help biotechnology companies assess, visualize, and make predictions using AI and machine learning to process data.
Artificial Intelligence (AI) and Machine Learning (ML) have
become popular mainstream topics. You no doubt have read about them or seen programs about them. Typically, they are presented as very complex topics that require specialized computer processing and large teams of highly experienced data scientists. This was true for many years but it is beginning to change and Oracle is at the forefront of this change. Oracle has built machine learning capabilities directly into its Cloud BI platform, Oracle Analytics Cloud (OAC), thereby making functionality like ML and predictive analytics available to regular business analysts (BAs). BAs who are willing to explore this new functionality will find a broad array of ML capabilities that don’t require an extensive background in ML or data science. They will be able to interact with and analyze data in ways they previously have not done and they will be able to add a whole new level of analytical value to their organization – enabling the data-driven decision making so many organizations are pursuing. This functionality is part of the Data Visualization component of OAC and is natively available without any special licensing. In this post, I will show you how to use the machine learning capabilities in Oracle Analytics Cloud to predict HR attrition. It is easy to use and requires no writing of computer code but here are some quick points to know before we begin:
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Myles has more than 20 years of global experience in the design and deployment of large scale ERP and data warehousing/BI solutions. He spent the first 10 years of his career at a Big 4 consulting firm leading international ERP and BI deployments including Oracle and SAP. He then moved into industry where he held senior management positions in Business Intelligence and Corporate Finance Technology. He has a unique blend of deep experience in both the ERP and BI arenas. Myles has lived and worked in Tokyo, London and Hong Kong and has a demonstrated track record of delivery in complex environments. He has leveraged a blended onshore/offshore model for the last 10 years.