Data Mining Explained
In business, data mining refers to the practice of sifting through data to find useful patterns and correlations that may then be used to inform analytical approaches to the resolution of issues. Companies can use methods and technologies for data mining to better predict future trends and make smart business decisions.
Data mining is a subfield of data science that use sophisticated analytical methods to extract actionable insights from data. Knowledge discovery in databases (KDD) is a method used in the field of data science for collecting, cleaning, and analyzing data. Data mining is an important part of KDD. Even though data mining and KDD can sometimes be used interchangeably, they are often seen as two different methods.
The significance of data mining in a nutshell
Successful analytics projects in businesses often include data mining. Its data can be used in traditional BI and advanced analytics programs that look at data from the past, as well as in real-time analytics programs that look at streaming data as it is being made or collected.
When it’s done right, data mining can be a useful tool for many parts of business planning and strategy. Included in this category are tasks like as marketing, advertising, sales, and customer service, as well as those involved in creating a product, managing inventory, handling finances, and staffing human resources. Among the many essential corporate uses for data mining include spotting fraud, managing risks, and preparing for cyberattacks. It’s also crucial in fields like medicine, politics, science, math, sports, and more.
What is the procedure for data mining like?
Data scientists, who are experts in business intelligence and analytics, are usually in charge of mining large amounts of data. However, citizen data scientists in the shape of business analysts, executives, and other employees may also do this out.
Its foundation is made up of data management activities, statistical and machine learning processes, and the analysis of collected data. Machine learning algorithms and artificial intelligence (AI) technologies automate more of the process and make it simpler to mine enormous data sets including customer databases, transaction records, and log files from web servers, mobile applications, and sensors.
In general, there are four phases of data mining:
- Data collection. It locates and compiles information that will be useful to an analytics program. There are a variety of places the data might be stored, including in separate source systems, a data warehouse, or a data lake, the latter of which is becoming more frequent in big data contexts. Additional information may be obtained from other sources. Before moving on with a procedure, data scientists often move information from its original source to a data lake.
- We’re getting ready to do some data prep. Preparing the data for mining is an integral part of this phase. Data cleaning entails correcting mistakes and other data quality concerns, and it follows the steps of data exploration, profiling, and pre-processing. Unless a data scientist is specifically asked to look at raw, unfiltered data, data transformation is done to make sure that different data sets can be compared.
- Exploring the depths of the data. After cleaning and organizing the data, a data scientist will choose a good data mining strategy before putting an algorithm or set of algorithms to work to do the actual mining. Algorithms used in machine learning applications are often trained on smaller subsets of data to find the desired information before being applied to the complete dataset.In machine learning applications, algorithms are often trained on smaller subsets of data to find the information they need before they are put to use on the whole dataset.
- Interpretation of data. The data mining findings are used to construct analytical models that may assist drive decision-making and other corporate activities. The results must also be shared with business leaders and users. This is often done by the data scientist or another member of the data science team using data visualization and data storytelling.
The Various Data Mining Methods
- Data may be mined for a wide variety of data science uses using a variety of methods. Anomaly detection, which looks for outlier values in a set of data, and pattern recognition are two common uses of data mining that are supported by many methods. These are some examples of well-known data mining methods:
- Discovering relationships between variables via computational methods. Association rules are if-then statements that are used in data mining to figure out if one piece of information caused something else to happen. Support and confidence criteria are used to figure out how strong a relationship is. These criteria count how many times an “if-then” statement is true and how often its two dependent items show up in a set of data, respectively.
- Classification. The components of data sets are classified according to criteria established during the data mining process, which is the focus of this method. Examples of classification techniques include decision trees, Naive Bayes classifiers, k-nearest neighbor, and logistic regression.
- Clustering. As part of the data mining process, similar pieces of information are grouped together into clusters. K-means clustering, hierarchical clustering, and the Gaussian mixture model are all types of clustering methods.
- Regression. Predicting values in a data collection based on a given set of variables is another method for identifying patterns. A few of common types of regression analysis include linear and multivariate. Regressions may be performed using decision trees and other classification techniques.
- Analyzing steps and their relationships. It is also possible to mine data for patterns showing how a given sequence of events or values predicts future occurrences.
- Artificial neural networks. An artificial neural network is a computer program designed to mimic the way the human brain works. In the context of deep learning, a kind of machine learning used for increasingly complicated pattern recognition tasks, neural networks shine.
Tools and applications for data mining
Numerous companies provide data mining applications; these programs are generally integrated into larger software suites that also include additional data science and advanced analytics programs. Data preparation tools, in-built algorithms, predictive modeling assistance, a graphical user interface (GUI) development environment, and tools for deploying models and assessing their performance are only some of the key elements supplied by data mining software.
Some examples of companies that provide data mining software include Alteryx, Amazon Web Services (AWS), Databricks, Dataiku, DataRobot, Google, H2O.ai, IBM, Knime, Microsoft, Oracle, RapidMiner, SAP, SAS Institute, and Tibco Software.
DataMelt, Elki, Orange, Rattle, scikit-learn, and Weka are only few of the free open source tools that may be utilized for data mining. The open source software community isn’t the only one to provide software. Companies like Dataiku and H2O.ai provide free versions of their products, while Knime combines an open source analytics platform with commercial software for managing data science projects.
There are several specific advantages to data mining
The value of data mining to businesses comes from the information that can be found by searching through huge amounts of data for relationships, anomalies, and other hidden patterns that haven’t been seen before. By combining traditional data analysis with predictive analytics, this information could be used to help companies make better decisions and plan their strategies.
- Sales and advertising should be improved. Marketers may use the information they get from data mining to better target their advertising to the tastes of the people they want to reach. Data mining may also be used by sales teams to increase the number of qualified leads they generate and the number of items and services they offer to current clients.
- Elevated quality of service to consumers. Data mining has allowed businesses to spot emerging trends in customer service concerns and equip call center representatives with timely details to share with consumers over the phone and in real-time chats.
- The management of the supply chain has been enhanced. Businesses may now more precisely anticipate product demand in response to shifting market conditions, allowing for more efficient stock management. Data mining may also help supply chain managers enhance logistical functions like storage and shipping.
- A rise in the percentage of time that factories are able to continue operating. Mining operational data from sensors on manufacturing machines and other industrial equipment supports predictive maintenance applications to identify potential problems before they occur, helping to avoid unscheduled downtime.
- Improved capacity to mitigate danger. Risk managers and business executives can better assess financial, legal, cybersecurity and other risks to a company and develop plans for managing them.
- Lower costs. Data mining helps drive cost savings through operational efficiencies in business processes and reduced redundancy and waste in corporate spending.
Ultimately, data mining initiatives can lead to higher revenue and profits, as well as competitive advantages that set companies apart from their business rivals.
Industry examples of data mining
Here’s how organizations in some industries use data mining as part of analytics applications:
- Retail. Online retailers mine customer data and internet clickstream records to help them target marketing campaigns, ads and promotional offers to individual shoppers. Data mining and predictive modeling also power the recommendation engines that suggest possible purchases to website visitors, as well as 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 also plays a key role in marketing and in identifying potential upselling opportunities with existing customers.
- Insurance. Insurers rely on data mining to aid in pricing insurance policies and deciding whether to approve policy applications, including risk modeling and management for prospective customers.
- Manufacturing. Data mining applications for manufacturers include efforts to improve uptime and operational efficiency in production plants, supply chain performance and product safety.
- Entertainment. Streaming services do data mining to analyze what users are watching or listening to and to make personalized recommendations based on people’s viewing and listening habits.
- Healthcare. Data mining helps doctors diagnose medical conditions, treat patients and analyze X-rays and other medical imaging results. Medical research also depends heavily on data mining, machine learning and other forms of analytics.