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Data Mining Techniques and Process

Jun 22, 2020 5 min read

Quite frankly, the world of technology and its advancement has thrown everyone out of the age of manually analyzing big data. It is no longer a surprise that data has formed an essential part of every company and business. Surprisingly, data has grown to become one of the top priorities of companies and their strategies.

Realistically, developing a data-driven business strategy in the present world is nothing but a challenging task. However, the use of data mining techniques has been adopted by numerous business analysts to examine a large set of data. Apart from that, they’ve grown even further to the extent of extracting unknown knowledge and patterns to identify effective ways to personalize business strategies.

In the business world of today, companies and businesses solely rely on the accumulation of data. In reality, making sense of the accumulated data could throw the most experienced analysts on an occasional loop. However, data mining techniques remain a trusted procedure that any analyst can rely on despite the perceived challenges and difficulties. What exactly is data mining? What are the data mining techniques? Where are these techniques used? These and more are some of the areas we’ll be covering in this piece.

What is Data Mining?

Simply put, data mining is an analytical procedure to convert raw data into meaningful and useful pieces of information. It is the application of specific software to mine through a wide array of data in a search for specific patterns that communicate both knowledge and insights into the business owner.

The models derived or extracted cannot be discovered without the help of the mining software. This is due to the complexity of the data for an individual to sift through.

What help does data mining offer to business owners? Data mining provides businesses with the golden opportunity to have a deeper understanding of their customers. Apart from that, it helps businesses craft more personalized marketing strategies, which will ultimately bring about a drastic increase in sales and revenue while decreasing costs or expenditures.

Besides, the information extracted or derived through data mining can function to help businesses improve their customer relationships, reduce risks, and many more.

Data Mining Techniques

With the right and accurate techniques in place, data mining is, no doubt, a highly productive process. However, the challenge lies in the ability to opt for the best techniques for your specific situations. This is because there are numerous data mining techniques to choose from. Here are the major data mining techniques:

Classification

The classification analysis is a form of analysis that is primarily used to group different data based on different classes. Usually, this isn’t an ambiguous term to understand. The word “classification” itself simply means grouping data elements into data sets based on specific criteria.

For instance, dog bowls, dog food, and leashes might constitute a specific category. This classification can also make up a broader category titled “dog materials or items.” It can also be grouped as something else along that line.

All in all, the primary purpose of classification is to establish a connection between elements in a specific set of data. Usually, it involves mathematical or statistical functions which help to make accurate predictions and classifications for businesses. However, you should keep in mind that it is easy to classify items related to canines rather than establish a connection between them.

Clustering

Clustering is similar to data classification. Even, some people get confused while differentiating between these two data mining techniques. Admittedly, it usually amounts to a mistake, but it has to be nipped in the bud, whatever the situation.

Clustering data is similar to classifying data in numerous ways, but there is a clear difference. In classification, data items are sorted into specific classes. Clustering, on the other hand, involves organizing similar data sets. These criteria for clustering data doesn’t need to be highly significant.

For instance, different groups of customers are clustered together to discover the similarities and dissimilarities between the strands of information provided by them.

Regression

Regression is a data mining technique that is designed to identify and pinpoint the existing communication or interaction between different variables. It is used to determine the probability of a variable from other existing variables. This method is also referred to as predictive power.

For instance, between an egg and chicken, what came first? Is it the egg? Or probably the chicken? With regression, the egg indeed came first, even if it didn’t come first in reality. Take a look at this scenario: What if you do not know that a chicken is responsible for laying an egg? You’ll be left with the mission to find what the existence of the egg is tied to.

Now, relate this to your business. What strategy is specifically associated with a slight increase in your sales? You have to discover that with regression. The ultimate goal of regression is to establish the link or interaction between two separate information in one set.

Association

This data mining technique is used to find an association between two or more properties or events. It boils down to help you discover an underlying model in the database systems. Think about it this way: when you visit a store to purchase a laptop. You’ll immediately be offered a laptop bag to go with. Association as a data mining technique helps businesses to craft marketing plans and robust specials for their customers.

Prediction

When strategically made, predictions are one of the potent data mining techniques. Of course, an excellent prediction is solely based on the data that a company has access to.

For instance, if a business discovers some anomalies or patterns which indicate a significant change in the foreseeable future, it immediately devises strategies to utilize these predictions to proper adjustments. These predictions can also be used to outsmart their competitors, thus, proactively positioning their business to outlive the competitors.

If your company lacks the understanding behind making use of predictions to implement strategic changes, then that business will be affected in a hard way by market shifts. That said, you shouldn’t make such mistakes. Use the available data to predict the future of your business.

Outlier Analysis (Outer detection)

This data mining process helps to identify specific anomalies in a data set. You should be able to understand the reasons why these outliers are there amidst the all-encompassing patterns. For instance, among all your female audience of buyers, you suddenly started recording female buyers at the peak. Outer detection helps to identify the data items in the dataset that doesn’t align with the expected behavior or patterns. Outer detection is also known as outlier mining or outlier analysis.

Sequential Pattern

The primary function of the sequential pattern in data mining techniques is to discover similar models, regularities, or transaction tendencies in the available data set over a specific period. In sales, companies or businesses can discover the influx of purchase of some items specifically in a season of the year. Based on this data, they can offer better deals to those consistent clients with relevant purchasing history.

Data Warehousing

It is practically impossible to have data mining without warehousing. Data warehouses simply refer to the databases that host, process, and prepares data for mining. The basic task of a data warehouse is to sort data, classify it, and eliminate unusable data. Hence, setting up metadata.

Manual Data Mining is Challenging: Use Whatagraph Data Mining/Reporting Tool

Sifting a vast amount of data is undoubtedly a headache. With all the earlier mentioned techniques, it doesn’t stop it from being challenging and trying to achieve it manually. That is why you need a data mining and reporting tool like Whatagraph.

Proper interpretation of these data can go a long way in developing your business growth strategies.

While you need to identify the best strategies for your business growth with the best data mining techniques, it is equally vital that you identify the right reporting tool to help with your data mining. You need to partner with a company that understands the nitty-gritty of data mining and reporting as well as your company’s purpose and goals. Look no further than Whatagraph Data Mining/Reporting Tool.

Wendy Gooseberry
Written by Wendy Gooseberry

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