The Differences between Data Mining and Predictive Analytics

Data has become an important part of businesses and it gives businesses an advantage over the competition when used appropriately. Data Mining and Predictive Analytics have gotten wider recognition.

Data Mining and Predictive Analytics use data to discover information and provide the best solutions. Both are commonly linked to explain how data is processed, however, there are substantial differences between them. The difference between Data Mining and Predictive Analytics is that the latter studies the data and the former answers. In essence, they are two different analytical methods with their exclusive benefits.

In this blog, we will examine the differences, as well as the benefits of Data Mining and Predictive Analytics.

Let’s dive in!

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Definitions

Data Mining is the technical process of extracting usable data from a bigger set of any raw data to identify patterns and establish relationships. With data mining, you can identify, investigate, sort, and organize consistent patterns. This is a strategic practice that is essential for successful businesses. Data sources can include databases, data warehouses, and the web.

Predictive Analytics is a valuable tool for forecasting. Predictive Analytics describes a range of analytical and statistical techniques used for determining patterns that may be used to predict future trends, behaviours or outcomes. In other words, Predictive Analytics aims to forecast future events.

Techniques and Tools

There are several innovative and intuitive Data Mining techniques, which include classification, sequential patterns, clustering, regression, outer detection, and association rule discovery. Data cleansing, clustering, and filtering are features a Data Mining tool should have. Two frequently used programming languages in data mining are R and Python.

With the advancement in technology, business users can use a more user-friendly tool to forecast business outcome or market trends. Software technologies such as Machine Learning and Artificial Intelligence are Predictive Analytics tools to examine the available data and predict the outcomes.

The Objective

The two main objectives of Data Mining is to offer businesses information by retrieving interesting patterns in the data and to provide businesses with predictive power to assess future values or outcomes.

The objective of Predictive Analytics is to go beyond identifying and understanding what has happened, to offer the best estimation of what will happen in the future. And it helps businesses to get to know their consumers and understand the trends they follow. Although, it will not give an exact picture of what will happen in the future, Predictive Analytics can help businesses mitigate future risks. Businesses will be able to take necessary action at the right time.

Functionality

Data Mining has three phases which are:

a) Exploration — this phase includes business understanding, data understanding, and data preparation. Here, the appropriate data is collected, cleaned, and integrated from multiple sources.

b) Model Building or Pattern Identification — this phase involves modelling and evaluating the data. Here, the same dataset is applied to different models, and the most fitting with the business requirement needs will be chosen and evaluated.

c) Deployment — in this phase an implementation plan is made, strategies to support and monitor the results for its effectiveness are formed, review to check for repetition and finally, the selected data model is applied to predict results.

Predictive Analytics utilizes various models to analyze and predict a customer’s behaviour. Models can be prepared to analyze the most recent dataset and examine their behaviour.

Talent

Engineers with a strong mathematical background, machine learning experts, and statisticians’ experts commonly use Data Mining techniques.

Predictive Analytics is usually employed by business analysts and other field whizzes who are skilled in evaluating and decoding patterns located by machines.

Benefits

Data Mining helps businesses to get the information required to make better decisions. As a result, you can add value to your business by better understanding customer segments, purchase patterns, and behaviour analytics.

Predictive Analytics helps a business to improve efficiency, as well as gain an advantage over the competition. It allows you to tap into valuable information that already exists to provide more insights. Additionally, Predictive Analytics helps to considerably reduce risk and meet consumers’/customers’ expectations.

So, what is the future of Data Mining and Predictive Analytics?

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The world of business moves briskly, and technology moves even faster. We are in an era of constant growth, where businesses are using available data for investigating patterns, forecasting outcomes, and executing decisions that will influence their business.

Data Mining and Predictive Analytics are some of the tools that can help businesses to make informed decisions by cutting costs, discovering fraud, saving resources, intensifying productivity, and producing effective results. The future of Data Mining and Predictive Analytics seems bright.

You need the right knowledge or expertise to help you make the best out of Data Mining and Predictive Analysis. Get in touch with us to learn more!

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