Enterprises of all industries have access to more data now than ever before. If you think about it, the number of downloads, uploads, posts, searches, messages sent and received, etc. taking place on the internet every minute is leading to massive amounts of data for organizations. Check out the infographic below:
However, not all data makes sense. For organizations to make complete sense of big data and leverage the insights gained from it, various data science solutions and techniques are applied. One such effective technique is data mining.
What is data mining?
Data mining is the process of analyzing big data to discern trends, patterns, or even anomalies in business. Data miners make use of a variety of tools and technologies to undertake the data mining process – cleaning raw data, finding patterns, creating models, and testing those models.
Why do organizations use data mining?
Data mining is a key component of data analytics initiatives in an organization. The information and insights that data mining provides can be further used for business intelligence and advanced analytics applications.
Effective data mining helps in various aspects of planning business strategies and managing operations. Different departments of an organization can use data mining effectively including marketing, sales, finance, HR, manufacturing, and customer support. Data mining can also be used for business use cases such as risk management, fraud detection, and cybersecurity planning.
What are the various data mining techniques?
Different data mining techniques are used for various data science applications as well as implemented during android application development. Some of the popular data mining techniques are mentioned below.
Classification
It is one of the most common data mining techniques. It involves analyzing different attributes associated with different types of data. Once the main characteristics of the data types are identified, organizations can categorize or classify the related data.
Let us take an example of the various occupation levels of employees. The classification data mining technique can help split the variable ‘occupation level’ into different categories including entry-level, mid-level, and senior. With other fields like age and education level, organizations can train their data model to predict the occupation level of an employee. For instance, with an entry for a 22-year old employee, the data model can classify the person as an entry-level position.
Clustering
This is another common technique that helps group records and cases by similarity. In the clustering process, the data set is separated into subgroups by geographical area, age group, etc. These subgroups then become inputs for a different data science solution or technique, meaning the subgroups are used for preparation of data for analysis.
Regression
This technique is used to analyze relationships between variables as part of the predictive modeling process. It can be used to predict sales, profits, weather conditions, and even patient recovery rates for medical practitioners.
Data miners and analysts typically use two types of regression models – Linear, which estimates the relationship between two variables, and Multiple, which explains the relationship among multiple data points.
Association
It is a technique related to statistics. Data miners and analysts apply this technique to analyze statistical data to find interlinked data and data-driven events. Co-relationship between such events can help identify significant marketing information, for instance, the best product recommendations that usually accompany specific product purchases (like recommending to buy a pair of shoes for a newly purchased dress).
Data visualization
Data visualization is a technique that allows analysts to present the analyzed data through charts, dashboards, graphics, heat maps, etc. The visualizations can be static or interactive, however, either way, it helps convey critical insights needed to make key business decisions.
Real-life examples of companies using data mining techniques
While above, we tried to explain the various data mining techniques, in the section below, we will look at the real-life examples of some top companies using the different data mining techniques.
eCommerce: The eCommerce giant, Amazon uses data mining techniques to boost their upselling and cross-selling capacities. The result – one of the most advanced product recommendation systems in the industry.
Education: The company, Explore uses eye-trackers and data classification technique of data mining to identify dyslexia among children.
Healthcare: OptumLabs uses data mining techniques to intelligently analyze diabetes-related data and reinforce the joint diabetes treatment effort of patients.
What are the challenges in data mining?
While it might seem obvious, the fact remains – there is too much data to handle; and it takes a massive amount of money to buy and maintain software, servers, and storage applications to handle big data.
What adds to the impediment to effective data mining is poor quality of data in the form of incomplete data, noisy data (data with meaning additional information), and poor representation in data sampling. It also sometimes becomes immensely difficult to integrate redundant data from multiple sources, for instance, combining structured and unstructured data.
Data privacy and security is another common challenge faced by data miners. For instance, when a retailer enquires about the purchase details, it uncovers information about customers’ choices and propensities without their authorization.
The future of data mining
The fundamental technologies underlying data mining – artificial intelligence, machine learning, data warehousing, neural networks – will continue to become more powerful, less expensive, and easier to use. Therefore, despite the current challenges, the data mining techniques will be put to increasing use by businesses of varied industries in the near future.
If you too are thinking of incorporating data mining techniques into your business or wondering how to get started with it, consult with a trusted data science solutions provider. The partnered firm will not only help you implement data mining and analytics solutions but would also help you to build an end-to-end strategy for maximum return on investment for your business.
Author bio:
Richard Parker is an active content writer, reviewer and lifelong student of data analytics. He is enthusiastic about the real world application of analytics to improve business operations.
