Much of what you do produces data. Did you use a loyalty card last time you went grocery shopping? You can bet the grocery store was eager to collect all the information it could about this specific trip and your buying habits. Your credit card company got in on the game, too. Then, after you put the groceries away and sat down to watch your new favorite sci-fi show on Netflix, the media giant was learning about you through data points.
What happens to all of this data? How do your grocery store, your credit card company, and Netflix use it to give you more personalised service? How do they use it to encourage you to buy more?
Data mining plays a key role in this process.
Investopedia has an excellent definition of data mining: It’s “a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies.”
In other words, data alone is pretty useless, even if you have massive amounts of it. To make any sense of the data, you need a system of organising it, and then searching for patterns and insights. That’s exactly what data mining does, and it’s important to understand some data mining techniques and how they work.