Data mining is used in almost all types of businesses and organisations today. The businesses and organisations that stand to gain the most from data mining include those with a strong consumer focus. For these organisations, data mining is a way to get relevant data to determine customer preferences, the impact of certain campaigns on sales, corporate profits, customer satisfaction, and pricing. Data mining can also be an important tool in product design and development, promotions and business protection. Here are some of the areas where data mining and analysis are being used.


Healthcare is one segment of our society that can and does benefit greatly from technology innovations and advancements. Data mining is being used in healthcare to identify best practices and practices that have the highest impact on patient care. It is also being used to identify areas where healthcare institutions can cut costs to save money or channel this money towards patient care.

Using tools like data visualisation, statistics, machine learning, and others, healthcare institutions are also able to predict patient volumes so they can be prepared in case patient numbers surge. Healthcare institutions can also use data mining to ensure that patients receive the type and level of care they need as and when they need it, and to detect abuse and fraud within the healthcare setting.

Basket Analysis in Ecommerce

Basket analysis is used a lot with online retailers like Amazon and Best Buy. Market basket analysis is a technique that supports the theory that if you buy a certain product, you are likely to buy a related product with it. For example, if you purchase a laptop, you are likely to need a mouse or a thumb drive.

Market basket analysis is widely used to understand customer preferences and to recommend products they might need. This has the dual benefit of enhancing customer satisfaction and increasing revenues for these retailers. 

This technique can also be used in physical stores to determine the arrangement and location of certain products to increase the possibility of customers adding related items to their baskets, even in cases where they did not come to a store to buy the additional items. 

Differential analysis can also be done to compare results between different stores, arrangements, demographics and other factors that determine which goods customers buy.

Intrusion Detection

In computer science, intrusions are defined as any actions that compromise the confidentiality and integrity of a system or resource. An intrusion may include data siphoning, an intruder using a system’s resources as bots, and intruders masking their presence by making themselves a system administrator, and many others. Intrusions are presented through better IT infrastructure design, avoiding programming errors, removing system vulnerabilities, putting measures in place to protect information and proper user authentication.

Data mining is increasingly being used in intrusion detection as it provides data that shows where, when and how an intrusion happened. This helps give analysis an area to focus on so they know what happened and what to do to avoid intrusions in the future. Data mining can also help extract data that is relevant to adjacent problems so they can also be resolved as the main problem is being taken care of.

Data mining in intrusion detection goes hand in hand with cybersecurity. Because all organisations now rely on their IT infrastructure and computer systems to keep running, data mining, analysis and cybersecurity are highly sought-after skills by businesses and large organisations. When you complete a master in computer science degree at Wilfrid Laurier University or a similar institution, you will be armed with these and other highly-sought computer science skills perfectly tailored for today’s industry demands.

Customer Relationship Management (CRM)

Although CRM usually has a large focus on data collection, it is also about acquiring customers, retaining them, improving customer loyalty, and implementing various customer strategies. Data mining techniques play a huge role in helping businesses acquire the data they need for analysis. This data can be used to provide clarity so that, instead of focusing on where to acquire and how to retain customers, businesses can focus on getting filtered results that are key to finding solutions to any customer issues they may have and for the strategies they need to put in place. 

Fraud Detection

Every year, billions of dollars are lost due to fraud and fraudulent actions. Although there are traditional fraud detection systems that have been effective in the past, these systems and the methods they rely on are both complex and time-consuming. Data mining helps businesses see meaningful patterns while also helping turn this data into useful information that businesses can get insights from. 

When used for fraud detection, businesses use data mining to collect data samples that are analysed and then classified as either fraudulent or not. A model is then built using this data and its insights, with algorithms built around it that can identify any record in a large pool of data as being fraudulent or not.

Customer Segmentation

Customer segmentation is key in targeted customer or demographic marketing. Traditional market research can help with meaningful customer segmentation, but data mining goes deeper and helps increase effectiveness. Using data mining models, businesses can more clearly segment their customers and demographics so they can tailor their messaging, products, services and pricing to different segments.

Data mining can also help find customer vulnerabilities and news, which opens up opportunities to reach out to these customers with specialised and personalised offers.

Research Analysis

Research analysis has evolved in the last few years, with data mining helping clean data, preprocess it, and integrate various databases for data organisation. Once data is organised this way, researchers can find any type of data they need as well as the relationship between different sets of data, even in cases where the data resides in different databases. Data visualisation can then be used to provide a clear view and explanation of the data so collected and organised.

While data mining is not new, we have come a long way in the way we collect, analyse and use data. Data mining has helped organisations and businesses alike make the most out of the data they collect, making data even more valuable to those who collect and need it.

Leave a Reply

Your email address will not be published. Required fields are marked *