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.

Sources

The Internet of Things (IoT) refers to the transformation of physical objects around the world that are now connected to the internet. These devices are all collecting and sharing data that help businesses make business decisions as well as automate human lives.

The idea first stemmed throughout the 1980s and 1990s when technologists thought of the idea to add sensors and intelligence to basic objects. They started investing in devices like televisions, cars, and smartphones. Now, these devices have matured to the point where an internet connection is part of the base offering. 

SOME EXAMPLES OF IoT DEVICES INCLUDE, 
  1. A lightbulb that can be switched on by using a smartphone app 
  2. A smart refrigerator that tracks what type of foods you eat
  3. A smart thermostat that analyzes patterns in your household to set the right temperature without human interference 
  4. Self-driving cars
  5. Fitness watch
WHAT ARE THE BENEFITS OF THE INTERNET OF THINGS FOR BUSINESSES?

Businesses use the Internet of Things to learn more about their customers so they can react faster and serve them in advanced ways that add new value and increase revenue. 

For example, manufacturers are adding sensors to parts of their products to record data and track how they are doing. This can help companies see when a certain part is likely to fail and switch it out before it causes any damage. This helps make their supply chain more efficient because they have data that keeps them one step ahead.

WHAT ARE THE BENEFITS OF THE INTERNET OF THINGS FOR CONSUMERS?

The Internet of Things makes our homes, offices, and vehicles smarter and measurable. Smart speakers allow users to play music, set timers, or get on-demand information just by speaking to it. Home security systems allow users to monitor their homes inside and out with little effort and smart thermostats help people heat or cool their homes before they arrive.

However, these connected devices can raise concerns about personal privacy since they contain sensitive data and can be open to hackers.

WHAT NEXT FOR THE IOT?

Tech analyst company IDC predicts that in total there will be 41.6 billion connected IoT devices by 2025. Consumer IoT spending was predicted to hit $108 billion, making it the second-largest industry segment: smart home, personal wellness, and connected vehicle infotainment will see much of the spending. Within the integration of artificial intelligence, the future of IoT is limitless. 

The field of data science struggles with the presence and successes of women and people of color. According to Ms. Magazine, research suggests that only 15 percent of data scientists are women and fewer than 3 percent are women of color. Due to the education system failing to attract young girls and women to computer science, math, and other related fields, the number of girls and women leaning toward careers in data science is disproportionate. The leaky pipeline metaphor describes the gender gap in STEM-related careers.

A few solutions to this faulty pipeline include STEM education for women and people of color early in life, providing mentorship programs for women in data science, and developing gender-balanced policies.

One of the issues the lack of diversity in data science brings is racial and gender bias in algorithms. Women and people of color become overlooked depending on who is developing these algorithms. Machine learning is the act of training the computer to make judgments or predictions about the information it processes based on patterns it sees. In an article written by Rebecca Heilweil for Vox Recode, Amazon tried to use artificial intelligence to develop a resume screening tool. Its objective was to make screening resumes easier. The issue with that was that the data collected mainly came from men. In the end, this taught the computer to discriminate against women. Amazon decided not to use this tool for several reasons.

We can start accounting for everyone by hiring people of color and women to take on leadership roles. In addition, companies can start using rich and diverse data when training computers to process data.

Sources

https://msmagazine.com/2021/07/26/data-science-diversity-gender-women-stem/

https://generalassemb.ly/blog/data-science-gender-race-disparity/

https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency