Data Mining: What is it and Why is it important?

What is data mining?

Data mining is the process of sorting through large data sets to identify patterns and relationships that can aid in the resolution of business problems through data analysis. Data mining techniques and tools enable businesses to forecast future trends and make better business decisions.

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Data mining, which uses advanced analytics techniques to find useful information in data sets, is a critical component of data analytics and one of the core disciplines in data science. Data mining is a step in the knowledge discovery in databases (KDD) process, a data science methodology for gathering, processing, and analyzing data. Data mining and KDD are sometimes interchangeable, but they are more commonly regarded as distinct concepts.

Why is data mining important?

Data mining is an essential component of successful analytics initiatives in businesses. Its output can be used in business intelligence (BI) and advanced analytics applications that look at historical data and real-time analytics applications that look at streaming data as it is being created or collected.

Effective data mining aids in business strategy planning and operations management. That includes customer-facing functions such as marketing, advertising, sales and customer support, plus manufacturing, supply chain management, finance and HR. Data mining supports fraud detection, risk management, cybersecurity planning and many other critical business use cases. It also plays an important role in healthcare, government, scientific research, mathematics, sports, etc.

How does the data mining process work?

Data scientists and other skilled BI and analytics professionals are typically in charge of data mining. However, it can also be done by data-savvy business analysts, executives, and employees who act as citizen data scientists in an organization.

Its main components are machine learning and statistical analysis, as well as data management tasks performed to prepare data for analysis. The use of machine learning algorithms and artificial intelligence (AI) tools have automated more of the process and made massive data sets, such as customer databases, transaction records, and log files from web servers, mobile apps, and sensors, easier to mine.

The data mining process can be divided into 4 major stages:

Data gathering. Data for an analytics application is identified and compiled. The data may be stored in various source systems, a data warehouse, or a data lake, which is becoming an increasingly popular repository in big data environments containing a mix of structured and unstructured data. External data sources may be used as well. Regardless of where the data comes from, a data scientist will frequently move it to a data lake for the remaining steps in the process.
Data preparation. This stage consists of a series of steps that prepare the data for mining. It begins with data exploration, profiling, and pre-processing and then moves on to data cleansing to correct errors and other data quality issues. Unless a data scientist is looking to analyze unfiltered raw data for a specific application, data transformation is also done to make data sets consistent.
Mining the data. After preparing the data, a data scientist selects the appropriate data mining technique and then implements one or more algorithms to perform the mining. Before running against the entire data set in machine learning applications, the algorithms are typically trained on sample data sets to look for the information being sought.
Data analysis and interpretation. Data mining results are used to develop analytical models that can aid in decision-making and other business actions. The data scientist or another data science team member must also communicate the findings to business executives and users, frequently accomplished through data visualization and data storytelling techniques.

Types of data mining techniques

Different techniques can be used to mine data for various data science applications. A common data mining use case enabled by multiple methods is pattern recognition, as is anomaly detection, which aims to identify outlier values in data sets. The following are examples of popular data mining techniques:

Association rule mining. Association rules in data mining are if-then statements that identify relationships between data elements. To evaluate the connections, support and confidence criteria are used; support measures how frequently the related elements appear in a data set, while confidence reflects the number of times an if-then statement is correct.
Classification. This method categorizes the elements in data sets using categories defined during the data mining process. Classification methods include decision trees, Naive Bayes classifiers, k-nearest neighbour, and logistic regression.
Clustering. As part of data mining applications, data elements with similar characteristics are grouped into clusters. K-means clustering, hierarchical clustering, and Gaussian mixture models are a few examples.
Regression. Another method for discovering relationships in data sets is calculating predicted data values based on variables. Examples include linear regression and multivariate regression. Regressions can also be performed using decision trees and other classification methods.
Sequence and path analysis. Data can also be mined to look for patterns in which specific events or values lead to later ones.
Neural networks. A neural network is a collection of algorithms that simulates human brain activity. Deep learning, a more advanced offshoot of machine learning, employs neural networks in complex pattern recognition applications.

Data mining software and tools

Data mining tools are available from a wide range of vendors, usually as part of larger software platforms that include data science and advanced analytics tools. Data mining software’s key features include the following:

Data preparation capabilities.
Built-in algorithms.
Predictive modelling support.
A GUI-based development environment.
Tools for deploying models and scoring their performance.

Alteryx, AWS, Databricks, Dataiku, DataRobot, Google, H2O.ai, IBM, Knime, Microsoft, Oracle, RapidMiner, SAP, SAS Institute, and Tibco Software are among the vendors that provide data mining tools.

DataMelt, Elki, Orange, Rattle, scikit-learn, and Weka are free, open-source technologies that can mine data. Some software vendors also offer open-source options. Knime, for example, combines an open-source analytics platform with commercial software for managing data science applications, whereas Dataiku and H2O.ai provide free versions of their products.

The Advantages of Data Mining

In general, the increased ability to uncover hidden patterns, trends, correlations, and anomalies in data sets results in business benefits. This information can be used to improve business decision-making and strategic planning through a combination of traditional data analysis and predictive analytics.

The following are some specific data mining advantages:

More effective marketing and sales. Data mining assists marketers in better understanding customer behaviour and preferences, allowing them to create targeted marketing and advertising campaigns. Similarly, sales teams can use data mining results to improve lead conversion rates and sell additional products and services to existing customers.
Improved customer service. Companies can use data mining to identify potential customer service issues more quickly and provide contact centre agents with up-to-date information to use in calls and online chats with customers.
Improved supply chain management. Organizations can better spot market trends and forecast product demand, allowing them to manage goods and supply inventories better. Supply chain managers can also use data mining information to optimize warehousing, distribution, and other logistics operations.
Enhanced production uptime. Mining operational data from sensors on manufacturing machines and other industrial equipment aids predictive maintenance applications in identifying potential problems before they occur, thereby reducing unplanned downtime.
Better risk management. Risk managers and business executives can better assess and manage a company’s financial, legal, cybersecurity, and other risks.
Reduced expenses. Data mining contributes to cost savings by increasing operational efficiencies in business processes and decreasing redundancy and waste in corporate spending.

Finally, data mining initiatives can lead to increased revenue and profits and competitive advantages that distinguish companies from their competitors.

Data mining industry examples

Here are some examples of how organizations in various industries use data mining as part of analytics applications:

Retail. Online retailers mine customers’ data and internet clickstream records to help them target marketing campaigns, ads, and promotional offers to specific shoppers. Data mining and predictive modelling are also used to power recommendation engines that suggest potential purchases to website visitors and inventory and supply chain management activities.
Financial Services. Banks and credit card companies use data mining tools to build financial risk models, detect fraudulent transactions, and vet loan and credit applications. Data mining is also important in marketing and identifying potential upselling opportunities with current customers.
Insurance. Insurers use data mining to help them price insurance policies and decide whether to approve policy applications, including risk modelling and management for prospective customers.
Manufacturing. Manufacturers’ data mining applications include efforts to improve uptime and operational efficiency in manufacturing plants, supply chain performance, and product safety.
Entertainment. Data mining is used by streaming services to analyze what users are watching or listening to and to make personalized recommendations based on their viewing and listening habits.
Healthcare. Doctors can use data mining to diagnose medical conditions, treat patients, and analyze X-rays and other medical imaging results. Data mining, machine learning, and other forms of analytics are also heavily used in medical research.

Data mining vs. data analytics and data warehousing

Data mining and data analytics are sometimes used interchangeably. However, it is primarily regarded as a subset of data analytics that automates the analysis of large data sets to discover information that would otherwise go undetected. This data can be used in the data science process and other business intelligence and analytics applications.

Data warehousing aids data mining by serving as a repository for data sets. Historically, historical data has been stored in enterprise data warehouses or smaller data marts designed for individual business units or specific data subsets. Data lakes, which hold historical and streaming data and are based on big data platforms like Hadoop and Spark, NoSQL databases, or cloud object storage services, are now frequently used to serve data mining applications.