Big Data and Data Mining_ Defining the Differences

Big Data and Data Mining_ Defining the Differences

Big Data and Data Mining: The Role Data Mining Plays in Big Data

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 college essay help 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 AgeBig 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 DataBefore 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.