Predictive Analytics vs Data Mining

This article will cover two primary categories of analytics software: predictive analytics and data mining. Predictive analytics refers to the use of both new and historical data, statistical algorithms, and machine learning techniques to forecast future activity, patterns, and trends. The primary objective is to go beyond knowing what has happened to assess better what will happen in the future. On the other hand, data mining refers to the computational technique of discovering patterns in huge data sets involving methods at the intersection of AI. The main objective of data mining is to extract useful information from the data warehouse and transform it into a piece of useful information. Before starting the differentiation between data mining and data profiling, let's understand the two terms separately.

What are Predictive Analytics?

As the name suggests, 'Predictive' means to predict something, so predictive analytics is the analysis done to predict the future event using the previous data. It is the process of extracting information from existing sets of data to find useful information, trends and forecast future events. Predictive analytics does not tell the exact thing that will happen in the future. It predicts what might happen in the future.

Let's understand the concept of predictive analytics with the help of an example; consider a hotel chain that wants to predict how many customers will stay in the specific location this weekday to ensure they have enough hotel staff and resources to manage the demand of the customers.

Advantages of Predictive Analytics in business

These are the given most significant business benefits of Predictive Analytics.

1. Predictive Analytics Increase production efficiency.
2. It minimizes business risks.
3. It helps in decision-making purposes in any business organization.
4. It drives a competitive environment.

Strategies of Predictive Analytics

Based on the information gathered from predictive analytics, many companies have increased their turnover, achieved goals, and increased revenues by applying these strategies.

1. Synchronizing supply with demand.
2. Fraud prevention.
3. Creating lasting inventories.
4. Customer satisfaction.
5. Setting prices appropriately in order to increase profit.

What is Data Mining?

Data mining refers to a process of analyzing data from different contexts and summarizing it into useful information. The information gathered from data mining could include customer patterns, purchase patterns, transaction times, customer demand, and the relationship between the sold items. It is a powerful technology with great potential to assist companies in targeting the most significant information in the data set they have gathered about the customer behaviours and potential of the customers.

These are the given steps involved in the process of data mining

1. Business Understandings
2. Data Selection
3. Data Preparation
4. Modelling
5. Evaluation
6. Deployment

Application of data mining

1. Financial Analysis
2. Biological Data Analysis
3. Market Analysis
4. Retail Industry
5. Manufacturing Engineering
6. Criminal investigation

Difference between Predictive Analytics and Data Mining