Data Mining is the computational process of extracting valuable information from large amounts of data. Finding gold, in this type of mining, is detecting hidden patterns.
Data Mining is successfully applied in many areas: marketing, insurance, finance, customer service, etc. By using the right Data Mining solutions, it is possible to discover new business opportunities, to focus an advertising campaign to the right target markets or to forecast the demand for a new product.
Custom Data Mining
At 3.14, we are committed to developing tailor-made solutions. Depending on the needs of each client, our Data Mining systems can combine statistical methods, database systems, Artificial Intelligence (AI) and Machine Learning. The overall goal is to find unknown, but potentially useful patterns. Among these patterns, it is worth mentioning:
- Groups of data records: Cluster analysis groups objects into clusters as homogeneous as possible.
- Anomalies: The process of outlier detection identifies unusual records.
- Dependencies: Relationships between data can be discovered through association rule mining.
In general, Data Mining tasks can be classified into two categories:
- Descriptive Data Mining: This type of Data Mining uses unsupervised learning methods. It focuses on finding hidden relationships among data.
- Predictive Data Mining: This type of Mining, also called supervised learning, trains a model with a set of example data. Its goal is to forecast outcomes.
Among the most common techniques used in Data Mining are the following:
- Decision Trees: This predictive model represents every possible outcome of a decision.
- Rules inductionn: Its goal is to extract a set of if - then rules.
- Clustering: This technique consists of dividing data into groups.
- Neural Networks: This paradigm, inspired by the way biological nervous system works, is normally used to model complex relationships between inputs and outputs.
- Genetic Algorithms: This process is inspired by genetic evolution and natural selection. It is commonly used to solve optimization problems.
Benefits of Data Mining
There are many business benefits from effective Data Mining, including:
- Improvement of decision-making process: Data Mining discovers relevant information for strategic decision making.
- Discovery of new opportunities: The identification of hidden patterns opens the door to new business avenues.
- Risk limitation: Predictive models help companies reduce uncertainty and manage risk.
- Better customer experience: Data Mining can be used to better understand customers. In this way, companies can improve customer satisfaction. It can also be applied to better understand other stakeholders, such as suppliers, employees or investors.
The benefits may be as diverse as the goals of each project. However, any organization that manages a large volume of information can take advantage of Data Mining.