We’ve recently discussed data collection and data-selling technology on our blog. But what happens to big data once you capture it? You have to process it somehow. And that analysis and extraction of information from big data is data mining.
But understanding data mining is also more complex than that. So if you want to know more about this topic, you’ll enjoy this article. Let’s begin.
What is data mining?
Data mining is the process of analyzing large volumes of raw data (data sets) to extract information from it.
Typically, this information includes patterns, irregularities, and connections within that data.
Based on the findings, individuals and organizations can extract value from big data.
In most cases, this means generating statistical forecasts that predict risks, opportunities, and outcomes within the context of that data.
In other words: data mining is the process of finding meaningful information in big data.
How to extract data patterns in statistics?
Technology is critical if you want to extract meaningful information from data sets. The reason for this is the volume, complexity, and structure of big data.
Typically, the data sets you capture can be:
Even with the simplest, structured model, manually analyzing large data sets requires a lot of time and resources.
So instead, researchers use software and innovative technology like artificial intelligence (AI) and machine learning.
These technologies can automatically process and analyze data sets to uncover patterns from them.
You can then use these statistical patterns in the data and apply them practically.
For example, when launching a new product, you’ll want to know what your target audience is, and whether they’ll welcome its arrival.
On the other hand, as huge as big data is, it’s never complete. It’s always provisional. So instead of applying it directly, you may first want to test it against more or other sample data.
In the product launch example, you could examine its effectiveness against existing products through a focus group.
What are some data mining techniques?
New technologies contributing to data mining are continuing to evolve. As they become more accessible, data miners can use them to adopt them and develop new techniques to extract information from big data.
And according to the International Journal of Computer Applications, there are 16 different data mining techniques in use today:
- Data cleaning and preparation
- Tracking patterns
- Outlier detection
- Sequential patterns
- Decision trees
- Statistical techniques
- Neural networks
- Data warehousing
- Long-term memory processing
- Machine learning and artificial intelligence
Who can use data mining?
While you’ll need the support of managed tech services, the importance of data mining can be felt across fields and industries.
A data mining example and its common use is science.
Researchers can collect data sets from across their field and use AI and machine learning to analyze and extract crucial results and findings for their research projects (regardless of their location).
But the addition of data mining techniques and algorithms isn’t limited to science alone. And there are many other uses for it in both the private and public sectors.
Here are a few types of data mining uses:
- People search
- Credit reporting
- Market testing
- Advertising effectiveness
- Researching political outcomes
- Risk evaluation
- And many others
Successful data mining steps you can take
Now, let’s take a look at how you can effectively apply data mining techniques.
Here’s a quick step-by-step guide on how you can make the best use of data mining:
#1 Choose the project carefully.
If you want to extract maximum value from big data, align your data mining goals with your top business goal.
When you know which information you need out of big data, it’s easier to collect, process, and analyze the right data to acquire it.
#2 Collect a lot of data from multiple sources
This is straightforward. The more data sets you use, the more varied the data is, and the greater the accuracy you’ll achieve for your forecasts using that information.
#3 Simplify your sampling strategy
Even when you use powerful data mining platforms to process large data sets, try analyzing smaller subsets of data instead.
Simplifying samples to make them clear and concise is the key to generating the best outcome from your efforts.
#4 Always use holdout samples
A holdout sample is a benchmark. It’s a reference point that you can use to evaluate the validity of your predictive models.
This ensures that your predictions aren’t based on other predictive patterns from a defined set of data. But, instead, on actual estimates from the real world.
#5 Refresh your models frequently
Once you generate a forecast or data prediction, start applying it to your research, business, or operations. But don’t hold onto it forever.
These models are only as good as the relevance of the patterns that you find. And as the data changes, it will affect the validity of your forecasts.
That’s why it’s essential to feed new data to the models every week, day, or even hour.