Data Mining- everything you need to know
By: Sydney Amos
Tags: Python, Web Scraping, Freelancing, Data Mining, Data Scraping

Data mining’s real value comes from the ability to discover gems in the form of relationships and patterns in data, which can be of use when making predictions that can have a meaningful impact on your business.

For instance, if your company determines that a specific marketing campaign resulted in high sales of a particular model of a product in specific areas of the country and not others, you can refocus the campaign so that in the future, you will get maximum returns.

The advantages of using data mining can vary depending on the type of business you have, and its goals. For instance, sales and marketing managers who work in retail might get customer information in different ways if they want to boost conversion rates, compared to those in the airline service industries.

Whatever the industry, data mining that is applied to client behavior and sales patterns in the past can be used to establish models that predict future behaviors and sales. Data mining also has great potential in helping eliminate practices that can harm your business. For instance, your company can use data mining to detect fraudulent activity in financial service transactions and insurance, or improve product safety.

Different areas where data mining is applied

The application of data mining can be used in virtually every industry. Here are some of the areas where data mining can be applied.

Educational institutions can apply data mining processes such as analyzing data sets to predict the future performance and learning behaviors of students. The information derived can help institutions improve their curricula and teaching methods. Retailers can apply data mining in their various fields to help identify which products are most likely to sell at particular times of the year, or what goods their customers are most likely to buy based on their past purchasing habits. Such information can be of great help to merchandisers when planning inventories and store layouts. Healthcare providers can use data mining to determine better ways of delivering care to patients, and also reduce costs. With the application of data mining, healthcare providers can predict how many patients will require healthcare and the kind of services they need. Data mining can be used in life sciences to get insights from volumes of biological data to assist in the development of new drugs and other treatment. Manufacturing companies can use data mining to search for patterns in the process of production to identify flawed methods and bottlenecks, and come up with solutions that will enhance efficiency. Data mining knowledge can also be applied to designing products, to make tweaks based on reviews from their customer experiences. Banks and other financial service institutions can apply data mining in relation to the channel preferences, accounts and transactions of their clients to better suit their needs. Analyzed data can also be gathered from their social media and websites interactions to help improve the loyalty of existing customers, and attract new ones.

Data mining can be applied in multiple industries including retail and healthcare to detect fraud and other abuses quicker than using traditional methods.

Major Component of data mining

Data mining is a process that features several separate components addressing different needs. Here are the components of data mining.

Preprocessing

Before you can apply the algorithms of data mining, you are required to establish a target data set. A data warehouse or mart is a common source for data. In order for you to analyze the data sets, you need to perform preprocessing.

Data cleansing and preparation

Target data sets need to be cleaned and prepared so that ‘noise’ can be removed, missing values can be addressed and outlying data points filtered. This is done to establish segmentation rules, remove errors and perform other functions related to data preparation.

Association rule learning

Also known as market basket analysis, association rule learning comprises of tools that search for relationships among variables within a data set. For instance, they can help determine which products are often bought together in a store.

Clustering

This is a component of data mining used to discover structures and groups in data sets that are similar to each other in some way. This is done without necessarily using known structures in the data.

Classification

The tools used in performing classification generalize known structures for the purposes of applying them to new data points. For instance, when an email application attempts to classify a message as spam or legitimate mail.

Regression

This is a data mining technique used to predict a range of numeric values when provided with a specific data set. Such values include prices, housing values, sales or temperatures among many others.

Summarization

This major data mining component provides a solid representation of a data set. It includes report generation and visualization.

There are many vendors providing data mining software tools. Some of them offer proprietary software, while others deliver products through open source efforts.

If you are looking to try a data mining application in your business, feel free to contact any of our IT experts at freelancer.com.

Challenges and risks involved in data mining

Data mining comes with its fair share of challenges and risks as any other technology would. Since the process involves using potentially personal and sensitive information, privacy and security are among the top concerns.

In addition, the information retrieved from data mining needs to be reliable, accurate and complete. This is because you’ll be using this information to make important decisions in your business. You might also use the information to communicate with your customers, investors, business partners, and regulators. In addition, data mining requires people who are skilled in the field of data science or other related areas. Other challenges and risks involving data mining include legal requirements, ethical concerns and data protection among others.

Conclusion

Despite there being challenges and risks involved in data mining, it has become an important component of IT-based strategies at numerous organizations, all seeking to gain value from the information being gathered. With ongoing advancements in machine learning, predictive analytics, and artificial intelligence, future trends in data mining are set to accelerate.