In our previous article, we gave way to one of the most talked-about topics in recent years. It is data mining and its impact on decision making in many companies.
But to understand much more about what this important topic is all about, it is necessary to know its key concepts:
Getting the best results from data mining requires a variety of tools and techniques. Some of the most commonly used functions include:
- Data cleaning and preparation: here data is transformed into a suitable format for further analysis and processing (such as identification and elimination of errors and missing data).
- Artificial intelligence (AI): these systems perform analytical activities associated with human intelligence (such as planning, learning, reasoning and problem solving).
- Association rule learning: these tools are also known as market basket analysis and are responsible for finding relationships between variables in a data set, such as determining which products are typically purchased together.
- Clustering: is the process of partitioning a data set into a set of meaningful subclasses (called clusters), the purpose of which is to help users understand the natural structure or grouping of the data.
- Classification: This technique assigns elements in a dataset to target categories or classes with the objective of accurately predicting the target class for each case in the data.
- Data analysis: this process evaluates digital information into useful business intelligence.
- Data warehousing: is a large collection of business data that is used to help an organization make decisions. It is the fundamental component of most large-scale data mining efforts.
- Machine learning: This computer programming technique uses statistical probabilities to give computers the ability to "learn" without being explicitly programmed.
- Regression: A technique used to predict a range of numerical values, such as sales, temperatures, or stock prices, based on a particular set of data.
How does data mining work?
A typical data mining project starts with asking the right business question, collecting the right data to answer, and preparing the data for analysis. Success in the latter phases depends on what happens in the former.
Data mining professionals generally achieve reliable and timely results by following a structured process involving these six steps:
- Business understanding: develop a thorough understanding of the project parameters, including the current business situation, the main business objective of the project and the criteria for success.
- Data understanding: determine the data that will be needed to solve the problem and collect it from all available sources.
- Data preparation: preparing the data in the appropriate format to answer the business question, addressing any data quality issues such as missing or duplicate data.
- Modeling: use of algorithms to identify patterns within the data.
- Evaluation: determining whether and to what extent the results delivered by a given model will help achieve the business objective.
- Implementation: making the results of the project available to decision makers.
Why is data mining so important to us? Thanks to this technology at LISA Insurtech we are able to empower the insurance industry by detecting fraud, segmenting policyholder preference, among many other things!
If you want to know how do we work, click here