Collecting large sets of public web data is a must for making well-informed business decisions, which would generate desired profits. Yet, there’s no point in gathering data if it’s not used in the right way later on. So, how to make that way right?
Data mining is the answer we’re looking for. Bear with us, and we’ll explain what exactly data mining is and how you can take advantage of it while optimizing your business operations, cutting costs, and improving relationships with your customers.
What is data mining?
Data mining is an advanced analysis of collected datasets. Basically, it’s the next step you take after the data collection process, such as web scraping, is done.
Data mining definition
Data mining is the process of exploring data through cleaning raw data, identifying patterns, and building models. This requires statistics, machine learning, and database systems.
Let’s take this data mining example: say you have an extensive list of product pricing data gathered from e-commerce websites, and you want to use this data to adjust your pricing strategy. For this, you’ll need to analyze and understand it first or, in other words, perform the data mining process.
Data mining process: how it works?
The data mining process involves all stages from data gathering to visualization of valuable insights. Its primary goal is to describe data through observations, associations, and correlations.
Data mining often involves four key steps: defining goals, planning collected data, applying algorithms, and evaluating outcomes.
Setting business goals
Well-defined business objectives are crucial for successful data mining outcomes. Data team (analysts, scientists, and engineers) must cooperate with other business stakeholders in describing business problems, which lead to informed data questions and frameworks. At times, analysts need to place extra input into fully understanding the context.
Having a clear business problem in mind, data specialists can quickly identify which information can answer relevant questions. After the data is gathered, they’ll clean the data by deleting duplicates and finding missing values.
Some datasets might require minimizing the number of dimensions to avoid any delays in computation later on. It’s up to data scientists to keep the essential features to ensure the accuracy of a model.
Based on the type of data analysis chosen, data scientists examine relations such as sequence, associations, or correlations. High-frequency patterns might have broader applicability, yet particular deviations in a dataset can showcase even areas of potential fraud.
During pattern mining, you can use deep learning data mining algorithms to classify or cluster datasets. If the data input is labeled (supervised learning), either a classification model is applied to group data or regression to predict how likely a specific assignment occurs.
If the dataset isn’t labeled (unsupervised learning), separate data points are compared to explore similarities and categorize them based on these features.
Finally, when the data is grouped, it’s time to assess and interpret the results. For findings to aid in achieving a company’s goals, the following criteria must be met during their evaluation: validity, novelty, usefulness, and comprehensibility.
Data mining techniques
There’s a range of methods that you can apply to your data mining process. The most common data mining use cases are pattern or anomaly identification, which several techniques enable.
Let’s briefly go through the most popular data mining methods.
This is an if-then rule-based technique for finding relationships between elements in a dataset. Association rules include two criterias: support and confidence. Support evaluates the frequency of a particular component in a dataset, while confidence shows how many times the if-then statement is correct.
This method intends to train data by mimicking interactions between the human brain through layers of nodes. Nodes include inputs, weights, bias, and outputs. If the output value surpasses a set threshold, then the information is passed to the next layer.
In this way, together with supervision, neural networks learn this mapping function and adjust it according to the loss function. When the loss function is close to zero, we can trust the model is accurate.
This technique groups elements into different categories designed during the data mining process. Some examples of classification include decision trees, k-nearest neighbor (KNN) algorithms, and logistic regression.
This data mining technique puts components sharing identical qualities into clusters based on data mining applications. Instances of this technique are hierarchical clustering, k-means clustering, and Gaussian mixture.
This is an additional method to identify relationships between data. It includes the prediction of data values on the basis of specific variables. As examples, we’d take linear regression, multivariate regression, or decision trees.
In some data mining cases, analysts would look for patterns that lead one set of events or values to the following ones.
Benefits of data mining
Generally speaking, the benefits data mining brings to businesses revolve around exploring hidden materials, trends, relations, and abnormalities in datasets. All these combined enhance the decision-making process and strategic planning.
Here are some specific advantages data mining can offer:
- Efficiency in marketing and sales. Both marketers and salespeople can benefit from data mining in better understanding customer behavior and preferences. This aids in developing targeted marketing campaigns, boosting lead conversion rates, and selling products or services to existing customers.
- Supply chain improvements. Having market trends in mind, companies can easily forecast product demand and handle all the supplies. On top of that, you can use data to optimize warehouse, distribution, and other logistics operations.
- Quality customer support. Businesses can quickly identify customer issues and use this information in calls and online chats with their customers.
- Powerful risk management. Risk managers and business executives can effectively assess and manage financial, legal, cybersecurity, and other risks associated with a corporation.
- Reduced costs. Data mining can save a company’s resources, as it ensures operational efficiency in processes and minimizes unnecessary spending.
Overall, if you implement the process into your business operations, it’s likely data mining results in higher revenue and profits while developing a competitive advantage over other companies in the field.
Web scraping vs data mining
From what we’ve already discussed, you might have a view of how web scraping differs from data mining. Web scraping is all you do to extract data from the internet and put it in an easy-to-analyze format.
Data mining, on the other hand, no longer involves any collection of data. Instead, it’s everything you do with your data after it’s in place and in a convenient format: preparing it, searching for patterns, and evaluating what you’ve found.
Data mining is no doubt a step-must-to-take after you gather data from the web. It can serve significant benefits to the teams all over the company, including marketing, customer service, sales, risk, and more.
All of this combined helps you leverage data mining to make well-informed business decisions, leading to profit and revenue.
If you’d like to learn more about the data analysis process, head over to our article on a significant component of the whole data mining process – data normalization.