What is data mining and why is it important?

How does data mining work?

Essentially, data mining is just a way to turn raw data and information into something useful. It can be used to improve user experience by analyzing what parts of a website are used more than others. Or by collecting and picking apart student data, a teacher could predict which students might fall behind early and devise a strategy to keep them afloat.

Data mining can employ the use of machine learning to automate many of the processes. By utilizing machine learning and artifical intelligence, a massive amount of data can be organized and collected into different categories and classifications with ease.

Once the data has been collected and a trend identified, it can finally be put to use. How the information is utilized depends entirely on the organization that mined the data. It could be used internally to provide better workplace efficiency, or it could be sold on to whoever would benefit most from the information — retailers, airlines or politicians, for example.

No matter what data mining is used for, it typically follows a similar process. Let’s break it down into a few steps:

1. An organization harvests data and stores it on physical or cloud servers. The data can be harvested by asking for it directly in the form of a questionnaire, or indirectly, like tracking user activity, for example.
2. Analysts or management will determine which patterns they want to look for in this large clump of raw data.
3. It’s passed on to the correct tech professionals, who make sure the data is processed accordingly to fit the end use.
4. The organized data is presented into an easy-to-digest format — usually a chart or graph.

Data mining techniques

Data can be mined in several ways and for a plethora of reasons. Here are five of the most common techniques that data miners will use to sort data:

Classification

Predefined classes will be determined by the organizer of the data. The raw data will be sorted into these classes based on their characteristics. A simple example is having a classification for people who are allergic to peanuts and another for those who aren’t. This example shows two predetermined classifications used to organize a set of data.

Clustering

Clustering is similar to, and often confused with, classification. Clustering is where groups are defined based on their similarities then sorted accordingly to those similarities. Whereas the classification technique will already have determined how the data is to be designated, clustering will create classes based on what the data collectively has in common.

Association

The association technique is most commonly used by retailers or those looking to sell a product to their users. It identifies data based on the relationship between an item purchase and what other items were purchased simultaneously. It’s a useful technique to determine the spending habits of a user base.

Sequential pattern

Sequential patterning is where patterns or behavioral traits are found in data over a specific amount of time. In other words, data is classified by the “sequence” of events that happened in the collection time window. By using the sequential pattern method, a shop can find out what products are often bought together during certain times of year.

Predictive

The predictive technique is most often used by organizations to justify new business actions. Predictive data mining will analyze previous data and find patterns that can be used to predict the future of a market.

What are the risks with data mining?

Many businesses have used social media data mining as an effective tool. Some platforms can collect an individual's data (search history, shares, likes, number of followers, etc.) and create a profile for each user. In that profile is all the data that has been mined over the user’s time on the platform. This information can be used for targeted ads throughout the user’s online session or even be sold on to third parties for another use.

A much more sinister purpose for mined social media data was revealed in 2018, where a data firm, Cambridge Analytica, harvested obscene amounts of data for the purpose of altering human behavior. The data was famously used to influence election results.

Is data mining bad?

Ultimately, it all depends on how sensitive the collected data is, who can access it and for what purposes it is actually used. Even if a company or an individual is cautious and mindful about the usage and collection of such information, nobody is safe from security breaches. If such data is leaked the consequences of this may be devastating to both individuals and companies.