The Role of Data Mining in Big Data

Writer : Angle Marque EG

The Role of Data Mining in Big, Data Data collection about people and their behaviors is now easier than ever thanks to digital technology. Customers who sign up for customer loyalty programs at grocery stores, for example, save money. However, the stores benefit as well: Customers' purchases are digitally recorded every time they make a purchase and swipe their loyalty cards. Customers' interests in products can also be determined by tracking the links they click in loyalty program emails. The stores can then tailor their marketing efforts in the future. If a customer buys a particular laundry detergent on a regular basis, the store may send an email alert when that product goes on sale. The targeted campaign, if successful, will entice the customer into the store. The customer is likely to make additional purchases once they arrive, increasing the store's profit.

While it may appear simple, the success of this process is dependent on large amounts of data and complex algorithms. Hundreds of thousands of customers must provide massive amounts of data, which must then be securely stored and analyzed for noteworthy patterns. It takes a lot of effort to figure out why one customer prefers a particular detergent brand. Understanding how this data is processed necessitates a knowledge of data mining vs big data – the two terms are often used interchangeably but are not interchangeable. This article explains the meaning of these two terms and looks at how they're influencing the modern world.

In the Digital Age, Data

Big data is transforming many aspects of modern life, including shopping. As a healthcare professional, for example, you can use it. The Mayo Clinic has partnered with Google to store massive amounts of hospital patients' health data in Google's cloud, in a single electronic health record (EHR). By analyzing this data, the clinic hopes that it can predict and prevent diseases based on patient behavior using artificial intelligence (AI) techniques.

Big data is also reshaping the educational landscape. In "3 Ways Big Data Is Changing Education Forever," Entrepreneur describes how internet learning is shaped by big data. Students can be tracked for things like how long it takes them to answer a test question or how many times they return to a particular educational text or video. Text or video tutorials that students frequently revisit can be reworked in order to make them more understandable.

It is undeniable that the digital era has many advantages for society. Data has improved many facets of modern life, including commerce, medicine, and education. In order to reap the benefits of data, companies are willing to pay large sums of money. Users' online activities such as the websites they visit and the search terms they enter are among the most highly prized pieces of information.

Defining Big Data

Prior to delving into the topic of data mining, it is necessary to clarify what is meant by the term "big data." According to this definition, big data is defined as data sets that are so large that they necessitate the use of computer technology to analyze. "Big data" was first used in 1997 to describe data sets that were "too large to capture within an acceptable scope," according to Data Science Central. Since then, the term has been redefined numerous times. According to the World Economic Forum, the concept as we know it today was first introduced to the general public in 2007. The five V's of big data must be met in order for it to be considered such in today's context:

  • It's not uncommon to need at least 1 terabyte of data for a project of this scope.
  • Variety. There are many different types of data that make up big data, including social media posts, web server logs and even photos and audio recordings.
  • Velocity. Fast data growth is another distinguishing characteristic of big data; the rate of data growth should be rapid and, ideally, exponential.
  • Veracity. The degree of accuracy and reliability of data is measured by its veracity.
  • Value. There must be a use for all this data. It is the job of a data scientist to use techniques such as data mining to discover this value and bring it to the benefit of their employers.

Defining Data Mining

Data mining would not exist if it weren't for big data. Data mining is a term used to describe the process of analyzing data in order to learn more about consumer habits. For the most part, data mining is used to improve the lives of consumers in some way in every modern industry. The term "data mining" refers to the process of sifting through large amounts of data to discover patterns and trends. Data scientists sift through massive amounts of data to find patterns and discrepancies that can be used to solve issues. As an illustration, recall the grocery store from the beginning: Loyalty cards can be used to automatically collect information about customers' purchases, such as what they bought, when they bought it, and what day of the week it was.

Microsoft Excel tables are sorted by preprogrammed algorithms. In order for a data scientist to analyze this raw data, they must first sift through all of it. The data scientist, on the other hand, relies on algorithms to identify patterns, such as the products that see a significant increase in sales on Fridays. The results of this analysis can be communicated to the store's marketing team by the data scientist. On Fridays, the team may decide to offer a special promotion that includes both ice cream and beer in an effort to increase sales even further. In order to identify patterns in customer behavior, businesses turn to data mining. As a result, they're able to pinpoint their ideal customers, personalize their advertising, and even anticipate their buying habits.

Most companies rely on data mining techniques to learn more about their target audience and what they want and need. According to VentureBeat, the traffic management startup Waycare is a compelling example. As a result of this data mining, the company is able to provide city planners with better urban infrastructure designs, reducing traffic congestion. An industry can learn a great deal about its customers and their habits through data mining, and it can use this information to improve its products and services and anticipate the needs of its customers.

Benefits of a Bachelor's Degree in Data Science for the Study of Big Data

Even though data mining and big data have different meanings, they are both important components of data science. The use of data mining and big data by companies in all industries helps them learn more about their customers and their habits. Data science is useful in a wide range of professions, from actuaries to marketing analysts. It's a great time to be an expert in this field, as the demand is high and the pay is high; A data scientist can expect to earn an annual salary of $96,000, according to PayScale data from September 2019.

Because of this, data science bachelor's degree holders can gain a competitive edge in the workplace. It's possible to earn a bachelor's degree in data science online from Maryville University. With the help of this program, students will learn how to manage, analyze, and explain data trends to non-technical audiences, laying a solid foundation for future success in big data. Everything from Data Science Foundations to Predictive Modeling will be covered in the course. Visit the Maryville University website to learn more about the program and how to apply.

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