We live in the age of big data. It is no surprise, that companies have come to the discovery that data now lies at the heart of almost every business' most prized strategies.
Of course, developing these data-driven strategies is no easy task. However, analysts use data mining- the examination of large sets of data to extract patterns and knowledge that would otherwise be unknown- to identify the best way to personalize strategies for businesses.
Since companies accumulate so much data in their lifetime, making sense of it all can throw even the most competent analysts for an occasional loop. But despite its difficulties, analysts can always turn to some trusted data mining techniques. Techniques that allow them to efficiently organize data, establish connections between different data elements and sets, and ultimately identify patterns that would otherwise be undiscovered.
So, what are these techniques, and why do you need to know them? Join us for a quick tutorial of data mining techniques to learn how data mining can transform your business decisions.
1. Identifying Anomalies
Not every piece of data falls into a set. Analysts run into data elements that just don't seem to fit anywhere on occasion.
The fact that this data doesn't fit anywhere, though, isn't indicative of its overall usefulness. True, sometimes an anomaly is a dead end. Other times, however, an anomaly can be revelatory in nature.
Let's say, for example, that a company's target demographic is young women. Imagine that a large majority of its purchasers are women. Then suddenly, the number of male buyers sharply increases, and then returns to normal.
That sharp increase and dip could well have represented some new trend. Needless to say, the business should keep an eye out for this unearthed trend and find ways to use it to its advantage.
2. Tracking Patterns
Tracking patterns is intuitive for many people. Unlike anomalies, patterns are generally reliable, though they're by no means infallible.
As you saw above, keeping up with patterns is essential for any business. Businesses that store and analyze data in order to build buyer personas and remain competitive have a clear advantage over those that don't.
Not only that, but identifying patterns is arguably the only thing that makes identifying anomalies a possibility. If a business hasn't noticed a pattern, how can it notice an anomaly? This means that companies which don't recognize patterns also miss out on using anomalies to their advantage.
Classification is just what it sounds like. It entails grouping data elements into data sets based on some unifying feature.
This is easy to understand when given examples. Dog bowls, dog food, and leashes, for instance, might each constitute their own categories. Those classifications can also make up one large category called "dog items" or something along those lines.
All in all, classification is useful for establishing connections between elements in data sets. It usually involves mathematical functions which help businesses make predictions and create more classifications. Do note, though, that establishing those connections isn't necessarily as easy as classifying items that canines need.
Technically speaking, classifying data is one way of making associations. For example, there is a clear connection between dog bowls and dog food.
Of course, life is seldom this easy. Sometimes we have to make connections between items which we can't necessarily group together in a single category.
Think about how you spent your Fourth of July. What types of foods did you eat? Ribs? Macaroni? Hot dogs? Chicken?
On most days of the year, there is no clear connection between these foods. Few people buy ribs and then pick up hot dogs while in the grocery store. These foods are only associated with each other around certain parts of the year for perhaps a couple of weeks at a time.
It's easy to see how businesses can use these simple associations to craft marketing plans and specials for their customers.
Predictions, when strategically made, are one of the most powerful data mining techniques. Of course, a good prediction should rely primarily on the data that a company has access to.
For instance, if a company observes some patterns and anomalies which might indicate a significant change in the near feature, that company will use these predictions to make the proper adjustments at the most reasonable speed. Effectively using predictions to outclass competitors in this way proactively sets a business apart from its competitors.
Companies that fail to learn how to use data to make predictions and implement change can be hit hard by market shifts. That said, don't make their mistake. Use your data to attempt to predict the future.
That's why it's there.
6. Clustering Data
Clustering data is similar to classifying data, so much so that some people might even confuse the two. It's an admittedly and honest mistake, but it's one that must be nipped in the bud no less.
Clustering data can be thought of as much less general than classifying data. Classifying data sorts items into specific classes, while clustering data organizes data by similarities.
These similarities don't have to be extremely significant. Imagine that you were going shopping for pasta and made your way to the pasta aisle in the store. Rigatoni is clearly not fettuccine, yet you'll find both in the same aisle.
Finding both items in the same aisle makes shopping for pasta much simpler than it would be otherwise.
What came first? The egg or the chicken? Well, if you're using regression, the egg certainly came first, even if it didn't actually come first.
Think about it this way: What if you didn't see the chicken lay the egg? Your mission would be to find out what the egg's existence is owed to.
Now think about in terms of business. What does that slight increase in sales owe its existence to? You have to go backward (regress) to figure it out.
It's All In the Data Mining Techniques
Sifting through big data is no doubt a headache, even with all of these data mining techniques. But you can't deny the fact that properly interpreting your data to develop growth strategies makes enduring that splitting headache worth it in the end.
Much like identifying the best strategies for your business with the best data mining techniques, it is equally as important to identify the right people to conduct your data mining; someone who understands the process as well as your company's purpose and goals.
So, if you ever find yourself getting stuck, we're always in your corner. Just give us a shout and we'll come to your aid.
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