With the evolution of technology and big data over the last decades, Data Mining has become a powerful tool for transforming information into powerful and valuable knowledge.
In this article, we are going to learn what Data Mining is, how it works, and some of its applications.
- 1 What is Data Mining?
- 2 Benefits of Data Mining
- 3 How does Data Mining work?
- 4 Data mining resources: software, tools, and companies
- 5 Data Mining applications
What is Data Mining?
It is a core discipline in Data Science and a part of what is known as KDD (Knowledge Discovery in Databases), which is a methodology for gathering, analyzing, and processing data.
It is the analysis of large blocks of data to find patterns, information, and different relationships that can be valuable knowledge for problem-solving and business management.
This mining process is normally used for two major purposes:
- Making more informed decisions
- Predicting future trends
Especially during the last few decades, and due to the growth of big data within many organizations, it has become more and more popular. This process can be very useful for planning different aspects of business management and strategies such as marketing actions, sales, or customer support.
Benefits of Data Mining
Broadly, Data Mining is beneficial for finding patterns, correlations, trends, or anomalies in any set of data. When the data is relevant for an organization, having it mined allows the company to improve its business planning and decision-making since they are doing it in a much more informed way.
Some specific benefits are:
- Improvement of customer services by identifying potential issues.
- Improvement of the supply chain by spotting trends in the market and forecasting the demand.
- Enrichment of marketing and sales strategies by allowing a better understanding of customer preferences and behaviour.
- Increase production time by predicting and identifying potential problems before they occur and optimizing the maintenance of the process.
How does Data Mining work?
Before starting the data analysis there is one previous step: setting the objectives.
If we want to obtain the best outcome from this process, we need to ask the right questions and define correctly all the parameters. Normally data scientists along with the business stakeholders have to define the problem they want to solve and understand the context.
Once the objective of the mining is clear, it usually follows these four steps:
- Gathering the data
- Preparing the data
- Mining the data
- Interpreting the results
Gathering the data
With the objective in mind, the data analyst must identify all the relevant data, which can be located in various sources and as structured or unstructured data. In this first step, the data is identified, assembled, and normally moved into a data lake, where the rest of the steps will be implemented.
Preparing the data
Once the data lake is ready the analyst must prepare it for mining by exploring, profiling, and pre-processing it. Also, it must be cleared of outliers, noise, duplicates, or missing values.
In some cases, this step requires reducing the data lake by applying more filters to ensure it is optimal, accurate, and doesn’t slow down the following steps or mining software.
Mining the data
Now the data is ready, it is time for the mining. This is the core step of Data Mining and consists in applying different algorithms and techniques to convert the data into knowledge.
The most common techniques are:
- Decision trees: laid out as tree ramifications, this technique classifies data based on different decisions and possible decision-making.
- Association rules: this rule-based method (normally if-then statements) identifies relationships inside the data. These are frequently used to understand consumption habits, useful for improving selling strategies or recommendation platforms.
- Clustering: in this case, clusters are made by grouping together data elements that share several characteristics, previously defined.
- Neural networks: this technique imitates the functions of the human brain, which allows computer programs to find patterns. This technique is very useful in problem-solving, machine learning, deep learning, and for implementing Artificial Intelligence.
- Sequential patterns: in this case, the mining is done by looking for relevant patterns in data that are sent and delivered as a sequence.
Interpreting the results
Once the mining is done it is time to create an analytical model and test the results to make sure they are accurate. Then, when the mining is tested and approved, the results must be converted into useful, valid, and understandable knowledge.
Many data scientists choose to deliver the results using systems for data visualization, and even storytelling techniques, to make comprehension easier. That way the results can be used by the organization to implement changes or new strategies to achieve its goals.
Data mining resources: software, tools, and companies
The majority of tools and software include built-in algorithms, data preparation options, predictive modelling, and modelling options to see how the results perform.
Some of the most common software are Rapid Miner, Oracle Data Mining, Rattle, or Knime, mostly offered by companies that also provide data management and consulting. But there is also the option of working with automated data with Python, a free open-source language with a very quick learning curve, which makes the process much more accessible and easy for beginners.
Data Mining applications
To wrap up this article, let’s see some applications of this process:
- Data Mining in healthcare: in medical research, analyzing data is a crucial part of obtaining the best results. Also, it can be useful for interpreting medical imaging results or obtaining a patient’s diagnosis.
The use of data mining in the United States in healthcare can save the healthcare industry up to $450 billion each year (Basel Kayyali, David Knott,& Steve Van Kuiken, 2013).
- Financial services and data mining: Banks and credit card companies can elaborate financial risk models and even detect fraud thanks to this technology.
- Data Mining in marketing and sales: as we mentioned above, this process can provide knowledge about the customer’s behaviour, something very helpful for improving new releases or upselling strategies.
- Education and data mining: to understand the student’s needs for better educational success, is possible to evaluate their performance with this technology.
- Data Mining in entertainment: have you ever wondered how streaming services know exactly what to recommend to each of their users? Using it to analyze what the user is watching and browsing.