Digital technology makes it easier than ever to gather data about people and their behaviors. When people enroll in customer loyalty programs at grocery stores, for example, they benefit by saving money. The stores also benefit, however: Every time customers make a purchase and swipe their loyalty cards, the stores digitally record the products they buy. The stores can also see what products customers are interested in by tracking the links they click in loyalty program emails. The stores can then target future marketing accordingly. If a customer always buys a certain laundry detergent, for example, the store may send an email alert when that product is on sale. If successful, the targeted campaign will lure the customer into the store. Once there, the customer is likely to make additional purchases, increasing the store’s profit.
While it may sound straightforward, this process relies on massive amounts of data and complicated algorithms to succeed. Huge volumes of information must be collected from hundreds of thousands of customers, securely stored, and subsequently analyzed for noteworthy patterns. A great deal of work goes into determining that one customer tends to buy a specific detergent brand. How this information is processed requires an understanding of data mining vs big data – the two phrases are intertwined, but aren’t the same thing. This article explains exactly what these two terms mean and examines how they’re increasingly influencing the modern world.
Data in the Digital Age
Big data is reshaping many areas of modern life; shopping is just one area where it comes into play. It’s also useful in healthcare, for instance. As the Wired magazine article “AI Could Reinvent Medicine — Or Become a Patient’s Nightmare” explains, 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) system. The clinic intends to use artificial intelligence (AI) technology to study this data and possibly predict — and prevent — diseases based on patient behavior.
Big data is also changing the face of the education system. Entrepreneur describes how internet learning is shaped by big data in “3 Ways Big Data Is Changing Education Forever.” For example, course designers can track details such as how long it takes students to answer a test question or how many times learners go back to review a certain educational text or video. If they see that students have to return to a certain text or video tutorial many times, they can tweak this material to make it easier to understand.
It’s clear that the digital age offers society many advantages. From commerce to medicine to education, data has enhanced many aspects of modern life. Given the significant value that data provides, companies will even pay vast sums to acquire it. For example, information about internet users is highly coveted — including details like the websites they visit and their search histories.
Defining Big Data
Before discussing data mining, it’s necessary to answer the question of just what the term “big data” refers to. In short, big data is characterized by its size — it consists of datasets so large that they require the assistance of computer technology to be analyzed. According to Data Science Central, the term “big data” first emerged in 1997 and was used to refer to data collections that were too large to be “captured within an acceptable scope.” In the decade that followed, the term was redefined several times. The concept as we understand it today was introduced to the wider public in 2007, according to the World Economic Forum. To qualify as big data as it’s now commonly understood and accepted, the following criteria must be met, known as the five V’s:
- Volume. A very large amount of information is required — usually at least 1 terabyte of data.
- Variety. Big data is further characterized by the fact that it comes from a wide variety of sources, such as social media, web servers, photos, and audio recordings.
- Velocity. Big data is also set apart by fast growth; it must be increasing at a rapid, ideally exponential, rate.
- Veracity. Veracity refers to how accurate or trustworthy the data is.
- Value. Big data must have value. Data scientists should be able to use techniques like data mining to discern this value and yield a benefit for the companies they work for.
Defining Data Mining
Without big data, data mining wouldn’t exist. Data mining describes the process by which companies study information to gain insights into consumer behavior. Every modern industry relies on data mining in some way — and usually uses this information to improve consumers’ lives. Data mining refers specifically to the process of finding meaning in expansive volumes of data. Data scientists collect large amounts of data and study it, looking for patterns and discrepancies to solve problems. Take the example of the grocery store in the introduction: Data can be automatically collected as customers swipe their loyalty cards, with their purchases noted, what day of the week they purchased items, and even what time of day they made their purchases.
Preprogrammed algorithms sort purchases into an ordered Microsoft Excel table. The data scientist needs to examine this raw data, but it’s not humanly possible to read through such a volume of information. The data scientist thus relies on algorithms to pinpoint patterns, picking out key points, like the products that see a sales spike on Friday nights. The data scientist can then communicate the results of this analysis to the store’s marketing team. The team may decide to use this information and offer a combo promotion on ice cream and beer on Fridays, for example, hoping to boost sales even further. Companies use data mining to spot trends in customer behavior. This enables them to better define their target demographic, tailor their marketing, and even predict customer behavior.
Most companies simply use data mining methods to learn more about their target audience and its needs. The traffic management startup Waycare is a compelling example, according to VentureBeat. The company uses data mining to study traffic patterns in different cities, and this information enables city managers to better design urban infrastructure, thereby easing traffic congestion. Through data mining, an industry can learn much more about the people who use its products and services, and it can work to improve them and anticipate consumers’ needs.
Using a Bachelor’s in Data Science for Data Mining and Big Data Analysis
Data mining vs. big data — although they may refer to different aspects, both are major elements of data science. Companies across all industries employ data scientists to use data mining and big data to learn more about consumers and their behaviors. From actuaries to marketing analysts, many professions benefit from a knowledge of data science. Professionals with the skills needed to work in this field are in high demand and can expect lucrative salaries: According to September 2019 data from PayScale, the average annual salary of a data scientist is $96,000.
Individuals interested in gaining a competitive advantage in the workforce can thus benefit from a bachelor’s in data science. The Maryville University online Bachelor of Science in Data Science is an option. This program gives students the foundation they need to succeed in the world of big data by teaching them how to manage data, analyze it to spot trends and predict behavioral patterns, and effectively explain data trends to lay audiences. Coursework covers everything from Foundations of Data Science to Predictive Modeling. To find out more about the curriculum and get the details on how to enroll, visit the Maryville University website.