The Seven Key Differences Between
Data Analytics and Data Mining

It’s easy to confuse the terms “data mining” and “data analytics” as these two functions sometimes overlap, depending on the circumstances in which they are being used. Let’s begin with the definitions of these two terms.

Data analytics is the analysis of data based on a hypothesis, producing useful information in the form of graphs, diagrams and reports. These are used to measure past performance and utilize the information for future operations.

According to IBM, data mining is: “the process of uncovering patterns and other valuable information from large data sets.” The data miner looks for trends and patterns in the data, identifies data sets, finds associations between various data sets and excludes data that does not match the requirements of the exercise. This process assists organizations by transforming large amounts of data into smaller, useful subsets.

Here Are the Seven Key Differences:

Process:

Purpose:

Data Set: 

Output:

Models Required: 

Skills Required:

Goal: 

You can acquaint yourself with these disciplines with the help of an online masters in data science at Baylor University. This degree will help you develop the skills and knowledge that will ensure an interesting and fulfilling career path in the field of data science.

An In-Depth Look at the Methods Utilized by the Two Disciplines

Data Analytics

Data analysts help businesses optimize their processes to perform more efficiently, reduce losses, maximize profits and set strategic goals. It has become an essential part of business; if performance or quality is not properly measured, improved performance is unlikely. Efficient data analytics procedures enable management to keep track of the business and make quick, informed decisions.

Data analysts use business tools to analyze, interpret and forecast data obtained from the database.

Typical data for a sales environment would be customer name, address and type of business. The business would keep track of the type of products each customer buys and the quantity bought, per product. Based on this information, and assuming there is enough historical data, the analysis of data could assist with the following:

Data analytics can be broken down into the following types:

Data Mining

Data mining is the identification of patterns and trends in a large database to provide data that is manageable and useful. Companies use this data to learn about their customers: everything we do online is stored, and companies mine this information to find out what we searched for, what we bought and when. They use this information to market new or similar products and special offers.

With this in mind, and the enormity of the database that has to be mined for this information, the data miner needs to have a good understanding of the business requirements. Before the data can be extracted, the data miner needs to know the company’s storage capacity and any limitations in the amount of data that can be extracted, as well as the security requirements.

Once the data has been extracted, it can be “cleaned” to eliminate superfluous items that may slow the computational speed down. The data is then assessed to determine patterns and trends, relationships and associations between data records, and classified, if possible, to make it easier to work with. The use of algorithms and models in this process enhances the efficiency of the data analysis.

Here are some methods that data miners use when categorizing data:

Takeaway

Both data analytics and data mining play a vital role in the success of modern organizations, and technology is changing at an ever-increasing rate. If you’re considering a career in data management, be assured that there will always be something new and interesting to learn along the way.