• By having a data warehouse, snapshots of data can be taken over time. This creates a historical record of data, which allows for an analysis of trends. • A data warehouse provides tools to combine data, which can provide new information and analysis.
Data Mining and Machine Learning
Data mining is the process of analyzing data to find previously unknown and interesting trends, patterns, and associations in order to make decisions. Generally, data mining is accomplished through automated means against extremely large data sets, such as a data warehouse. Some examples of data mining include: • An analysis of sales from a large grocery chain might determine that milk is purchased more frequently the day after it rains in cities with a population of less than 50,000. • A bank may find that loan applicants whose bank accounts show particular deposit and withdrawal patterns are not good credit risks. • A baseball team may find that collegiate baseball players with specific statistics in hitting, pitching, and fielding make for more successful major league players. One data mining method that an organization can use to do these analyses is called machine learning . Machine learning is used to analyze data and build models without being explicitly programmed to do so. Two primary branches of machine learning exist: supervised learning and unsupervised learning. Supervised learning occurs when an organization has data about past activity that has occurred and wants to replicate it. For example, if they want to create a new marketing campaign for a particular product line, they may look at data from past marketing campaigns to see which of their consumers responded most favorably. Once the analysis is done, a machine learning model is created that can be used to identify these new Information Systems for Business and Beyond (2019) pg. 83
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