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 customers. It is called “supervised” learning because we are directing (supervising) the analysis towards a result (in our example: consumers who respond favorably). Supervised learning techniques include analyses such as decision trees, neural networks, classifiers, and logistic regression.
Unsupervised learning occurs when an organization has data and wants to understand the relationship(s) between different data points. For example, if a retailer wants to understand purchasing patterns of its customers, an unsupervised learning model can be developed to find out which products are most often purchased together or how to group their customers by purchase history. Is it called “unsupervised” learning because no specific outcome is expected. Unsupervised learning techniques include clustering and association rules.
The increasing power of data mining has caused concerns for many, especially in the area of privacy. In today’s digital world, it is becoming easier than ever to take data from disparate sources and combine them to do new forms of analysis. In fact, a whole industry has sprung up around this technology: data brokers. These firms combine publicly accessible data with information obtained from the government and other sources to create vast warehouses of data about people and companies that they can then sell. This subject will be covered in much more detail in chapter 12 – the chapter on the ethical concerns of information systems.
Sidebar: What is data science? What is data analytics?
The term “data science” is a popular term meant to describe the analysis of large data sets to find new knowledge. For the past several years, it has been considered one of the best career fields to get into due to its explosive growth and high salaries. While a data scientist does many different things, their focus is generally on analyzing large data sets using various programming methods and software tools to create new knowledge for their organization. Data scientists are skilled in machine learning and data visualization techniques. The field of data science is constantly changing, and data scientists are on the cutting edge of work in areas such as artificial intelligence and neural networks.
We end the chapter with a discussion on the concept of knowledge management (KM). All companies accumulate knowledge over the course of their existence. Some of this knowledge is written down or saved, but not in an organized fashion. Much of this knowledge is not written down; instead, it is stored inside the heads of its employees. Knowledge management is the process of creating, formalizing the capture, indexing, storing, and sharing of the company’s knowledge in order to benefit from the experiences and insights that the company has captured during its existence.