Data mining has a long history. Originally known as knowledge discovery in databases, the term “data mining was coined until the 1990s. Data mining is the process of digging through large sets of data to identify patterns to predict future trends. Data mining is used at the intersection of machine learning, statistics, and database systems.

Database information is doubling every two years developing chaotic and repetitive noise in data. More information does not necessarily mean more knowledge. Unstructured data makes up 90 percent of the digital universe. This is why data mining is important, it allows you to understand what is relevant and how you can use that information to assess likely outcomes.

Data mining consists of four main steps. These steps include setting objectives, collecting and analyzing data, applying data mining algorithms, and evaluating results.

Data is stored in data warehouses, either in-house servers or the cloud. Information technology professionals, business analysts, and management teams then access the data and decide how they want to organize it using application software. The data is then presented in easy-to-understand and share formats, such as a graph or table.

The most common algorithms and techniques used to turn data into useful information include association rules, neural networks, decision trees, and K-nearest neighbor

In sales and marketing companies collect large amounts of data about their customers and prospects. Companies can optimize their marketing efforts by observing their consumer demographics and online user behaviors to increase profits. In educational institutions, data is collected to understand student population and environmental performance for optimal success.

Lately, data mining has gone under criticism due to users being unaware of data mining happening with their personal information. This data is being collected to influence consumer behavior and change their preferences. One way to protect yourself from data mining is to use a secure VPN, remove personal information from social networking sites, and always look at the privacy policy for any website and social media platform.


Virtual reality (VR) and augmented reality (AR) have made waves in the gaming, marketing, e-commerce, education, and technology industries. According to a research study from Tulane University, by 2022, the VR and AR market is projected to grow to $209.2 billion. Both technologies bring together the virtual world and real world by utilizing elaborate 3-D visuals. Based on the technology used and the experience it provides, there are some significant differences.

Augmented reality (AR) adds digital elements to a live environment by using the camera on a smartphone. AR users can control their presence in the real world while VR users are controlled by the system. Some examples of AR include Snapchat lenses and the game Pokemon Go. 

Virtual reality (VR) shuts out the physical world and submerges the user in a completely virtual experience with a headset. VR devices such as HTC Vive, Oculus Rift, or Google Cardboard allow users to transport into real-world or imagined environments like traveling underwater in a submarine or allowing medical students to practice procedures on virtual patients. 

The headset used for a virtual reality experience is constructed of optics, displays, sensors, and haptics. These components require a seamless connection to create an immersive experience for the user. As technology advances, the waveguides within the optics will grow to be a significant part of augmented reality displays. 

Augmented reality and virtual reality in the information technology field can help developers, researchers, and data scientists by providing educational simulations that enhance their learning experience. It also allows more humans to be involved in monitoring machine learning models to ensure machine’s decisions are ethical, fair, and reasonable.