3 Important Steps in Data Mining To Grow Your Business

Have you ever heard of the term Data Mining? In Data Science Data mining is a process of dredging or gathering important information from Big Data. Often this process uses statistical methods, mathematics, to use artificial intelligence (AI) technology. Data mining itself has several names, namely Knowledge discovery in databases (KDD), data pattern analyst, data archeology, data dredging, information harvesting, business intelligence, and others.

Data Mining itself has many functions. There are two functions, namely descriptive function and predictive function. The function of description in data mining is to understand more about the observed data and then the data and to know the characteristics of the data in question. The Predictive function of data mining is to find certain patterns from a data, patterns that can be used to predict other variables whose value or type is unknown?

There are several steps for data mining retrieval, including:

1.Data retrieval process

Data Mining has several processes in finding new data, these stages start from raw data to information that has been processed and is ready to be used. The process consists of:

• Data Cleansing
  This is the earliest stage, where data that is incomplete and still has many errors and inconsistent data is removed from the data collection.
• Data Integration
  The process where if there is repeated data it will be combined into one data at this stage.
• Selection
  At this stage, the data that has been cleaned and has also been combined will be sorted into data that is relevant to what the company needs.
• Data Transformation
  After passing the selection stage, it will be sent to the mining procedure stage through data aggression.
• Data Mining
  This process is a crucial process, because at this stage various techniques will be used to extract various potential patterns to obtain useful data.
• Pattern Evolution
  At this stage is a process where the potential patterns that have been found will be carried out in the identification stage based on the standards that have been given
• Knowledge Presentation
  In this final stage, the data that has been collected will be given a visualization which aims to help the client understand the results of this data mining.

2. Data Mining Techniques

Data mining itself has several techniques for mining data, the following techniques can be used to mine data:

• Predictive Modeling, this technique explains the nature of future events to certain events based on what has happened in the past, such as predicting the winner of the ‘Super Bowl’ or predicting the temperature on a particular day.
• Database Segmentation, this technique groups contacts with each other according to their behavior, gender, age, demographics, interests, and so on.
• Link Analysis, this technique makes a relationship between individual data or a set of records from the database.
• Deviation Detection, a technique for identifying outliers that express a deviation from a previously known expectation.
• Nearest Neighbor, this technique is a classification method for a collection of data based on data learning that has been previously classified. This technique is the oldest technique of data mining.
• Clustering is a technique for classifying data based on the criteria for each data.
• Decision Tree, is a next generation technique, where this technique is a predictive model that can be described as a tree. Each node in the tree structure represents a question that is used to classify data.

3. Dynamic Pricing

This method is useful for companies that use data to divide customers into groups according to their needs. So that groups that are not a priority for the company get products at the right price. This price change is based on information that has been obtained through Data Science and various other supporting factors. This method is also good for new people to try the services or products offered.

An example of dynamic pricing itself is generally used in e-commerce applications. They distinguish new users from old users. So that new users get discounts that make new users feel comfortable using company applications.

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