Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. Business 2 Community brought this topic to our attention in their article, “5 Mistakes When Building Predictive Analytics and How to Overcome Them.”
Making decisions based on data and predictive modeling is more effective than making decisions by adapting aging reports to the modern reality. As the market and demand for analytics continues to grow, the learning curve gets less steep.
Everyone wants to understand not only why things happened in the past but what the future will bring. Most modern analytics is retrospective; it’s based on historical data to explain past events, reactions, successes, and failures. Building a business intelligence system which automates each stage of data collection, storage, and processing will prevent human errors.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.