The rapid pace of modern business demands an agile approach to enterprise intelligence. Whether developing AI-powered knowledge management solutions or improving automation with intelligent decision making and orchestration, there are more options than ever when considering how best to uncover important insights from data.
Traditional data analysis is “descriptive” and useful in reporting, explaining data, and generating new models for current or historical events. Machine Learning is “predictive” and can learn from data to provide valuable insights and recommendations to help optimize processes, reduce costs, and open up new operating models. Which technology approach is right for your organization? That’s largely dependent on the target use case, data complexity, and the need for longer term expandability and scalability.
This infographic highlights the key differences between traditional data analytics and machine learning, focusing on the core benefits, protocols, data, and models.
You can read more about the 18 important skills required to bring AI solutions to life at your enterprise and Entefy’s quick video introduction to the emerging area of multimodal AI.
