Difference Between Data Mining and Predictive Analytics

The main difference between data mining and predictive analytics is that the data mining is the process of identifying the hidden patterns of data using algorithms and mining tools while the predictive analytics is the process of applying business knowledge to the discovered patterns to make predictions.

Data Mining is the process of discovering the patterns in a large dataset. It extracts new patterns and relationships between data entities. The output of data mining is a pattern that forms a timeline varying distribution. On the other hand, predictive analytics is the process of applying business knowledge to discovered patterns in a data set in order to predict trends and behaviors. These patterns are discovered by data mining or using some other technique. Business analysts and domain experts analyse and interpret them to make meaningful business insights. 

Key Areas Covered

1. What is Data Mining
     – Definition, Usage
2. What is Predictive Analytics
     – Definition, Usage
3. Difference Between Data Mining and Predictive Analytics
      – Comparison of Key Differences

Key Terms

Data Mining, Predictive Analytics

What is Data Mining

Data mining refers to the process of discovering patterns in a large data set. It involves extracting information from a data set and converting the information into a comprehensible structure for further use. It is used in many fields such as mathematics, cybernetics, marketing, etc.

Figure 1: Data Set

Data mining is associated with several tasks such as data integration, data transformation, pattern evaluation, and visualization. Data comes from multiple sources. All data is integrated and stored in a single location called data warehouse. Secondly, the data is preprocessed to make it suitable to perform data mining. Then, the patterns are recognized using algorithms such as clustering, regression, etc. Finally, these patterns are evaluated and visualized using graphs.

Furthermore, there is a type of data mining called web mining. This is the process of gathering information via traditional data mining methods and techniques through the web. It helps to understand factors like the effectiveness of a website and customer behavior. Overall, data mining provides the ability to uncover hidden patterns in data so that they can be used to make predictions and take business decisions.

What is Predictive Analytics

Predictive analytics analyzes the current and historical facts to make predictions about future or unknown events. It uses various statistical techniques such as data mining, predictive modelling, and machine learning.

Figure 2: Predictive Analytics Process

The predictive analytics process involves the following activities.

1. Defining project – Define project outcomes, scope, business objectives and identify the data set to be used.
2. Data Collection –  Gather data from multiple sources.
3. Data Analysis – Process of inspecting, modelling data to discover useful information.
4. Statistical Analysis – Validate assumptions, hypothesis and test them using statistical models.
5. Modelling – Create accurate predictive models for decision making.
6. Deployment – Deploy the analytical results for daily decision-making process to get results, reports, and outputs.
7. Model Monitoring – Managing and monitoring the model performance to ensure that the model is providing the expected results.

Predictive Analytics is used in many fields. It helps business organizations to analyze patterns found in historical and transactional data to identify risks and opportunities. For example, assume credit scoring. The customer’s credit history, loan application, and customer data are analyzed and processed to make decisions on whether that customer will pay the credit payment on time. Moreover, predictive analytics is used in fields such as marketing, finance, insurance, retail, telecommunication, healthcare, social networking and so on.

Difference Between Data Mining and Predictive Analytics

Definition

Data mining is the process of discovering patterns in large data set using methods of machine learning, statistics and database systems. Predictive analytics is the field of statistics that deals with extracting information from data and using them to predict trends and behavior patterns. This explains the basic difference between data mining and predictive analytics. 

Functionality

Data mining applies algorithms such as regression and classification on collected data to discover hidden patterns.  Predictive analytics, however, applies business knowledge to discovered patterns to get business valid predictions.

Usage

There is another difference between data mining and predictive analytics based on their usage. While data mining helps to understand the collected data better, predictive analytics helps to make predictions about future or unknown events.

Involved Professions

Although data mining is performed by statisticians and engineers, predictive analytics is performed by business analysts and other domain experts.

Conclusion

The difference between data mining and predictive analytics is that the data mining is the process of identifying the hidden patterns of data using algorithms and mining tools while the predictive analytics is the process applying business knowledge to the discovered patterns to make predictions.

Reference:

1. “What Is Data Mining? – Definition from WhatIs.com.” SearchSQLServer, Available here.
2. “Predictive Analytics.” Wikipedia, Wikimedia Foundation, 26 Aug. 2018, Available here.