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:
- Data analytics: Interprets and analyzes data to provide useful information
- Data mining: Identifies patterns and trends in large quantities of data, based on specific requirements
- Data analytics: Involves testing and hypotheses, building models
- Data mining: Involves identifying and extracting trends and patterns from a database
- Data analytics: Data sets can be any size and can be structured, non-structured or somewhere in between
- Data mining: Data sets are usually large and structured
- Data analytics: Provides insights or hypotheses in the form of graphs, diagrams and reports
- Data mining: Determines data patterns, classes of data and associations between the classes
- Data analytics: Uses analytical and business intelligence models
- Data mining: Uses mathematical and statistical models
- Data analytics: Computer science, mathematics, statistics and AI
- Data mining: Database knowledge, machine learning and statistics
- Data analytics: The goal is to make data-driven deductions and decisions
- Data mining: The main goal is to produce data that shows trends and patterns
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 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:
- Facilitate the accuracy of stock levels, saving costs relating to unnecessary overstocking
- Facilitate the procurement process based on sales and pricing information
- Provide the marketing department with customer details for conducting promotions and surveys, via email or social media, to glean insights regarding product quality and customer preferences
- Enable management to analyze products with a view to adding new product ranges or discontinuing non-profitable items
- Facilitate the financial processes of budgeting and forecasting
Data analytics can be broken down into the following types:
- Descriptive: Describes what has happened in the specified period of time. Did sales go up? How many online queries were converted to sales?
- Diagnostic: The focus here is on why something happened. Why did sales drop? Why did we lose customers?
- Predictive: What is likely to happen in a specified time frame, usually short term. Can we expect more sales? Should we re-stock?
- Prescriptive: Suggests a course of action. More people are exercising, so increase stocks of water bottles.
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:
- Classification analysis: The classification of data into separate classes for more efficient analysis later.
- Associated rule learning: The data miner will look for associations between different data variables. These associations are used to link data together for the required outcome.
- Outlier detection: This is data that has been excluded as it does not match the specified pattern.
- Clustering: The gathering of similar data objects within the same cluster.
- Regression analysis: Analysis and identification of a relationship between the different data variables. It is used to determine how a change in one variable is likely to affect another, dependent variable.
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.