Machine learning (ML) is a discipline of artificial intelligence (AI) that provides IT systems with the ability to independently learn from data and find solutions to problems, without explicit programming. ML uses algorithms to observe, uncover and extract critical patterns from data and then act upon it, using the knowledge and experience it gains over time from studying high volumes of data to teach, improve and optimize the performance of its computing models.

Advancements in processing power have made the development and running of complex mathematical machine learning models far more accessible to the average business user today. Several modern day analytical solutions have since combined self-service BI capabilities in conjunction with AI and machine learning to better enable users to instantly identify outliers, spot trends, automate analytical model building and enhance strategic decision-making.

What is machine learning used for?

Machine learning excels in its capacity to facilitate the extraction of valuable insights from data without human bias. Its use of automation and artificial intelligence enables analysts and regular business users to analyze vast volumes of data more efficiently and solve highly complex problems when traditional approaches, such as manual analysis and software engineering, fail.

Machine learning for software vendors and enterprises

Independent Software Vendors (ISVs) and enterprise organizations can leverage machine learning’s self-teaching algorithms to streamline the user’s analytical experience with their product significantly by automating various decision-making tasks. The instantaneous nature of ML allows business users to continuously enhance their self-service BI process, and frees up analysts and developers with more time to improve other core functions of the application.

Machine learning for business users

Machine learning enables business users to make the jump from data to insight much faster. They can use ML without having to seek assistance from their analyst team and without possessing advanced analytical skills to perform automated analysis, get answers to the questions they have of their data, and generate statistically relevant results in easy-to-understand and best practice visualizations and narratives. The actual implementation of machine learning in each business intelligence and analytics platform varies; in Yellowfin, for instance, Signals and Assisted Insights both leverage ML models to automate alerting and monitoring, and provide AI-assisted answers to user queries when exploring data, respectively.