Companies are keen on finding more and more applications for Data Mining and Business Intelligence as the importance of data analytics continues to grow. You can say for business data is undoubtedly a boon that has opened a world of possibilities for it. It is a branch of computational statistics that compares millions of isolated data pieces, mainly used by companies to detect and predict consumer behaviour. It aims to come up with new market opportunities.
Mainly data mining is used by companies who have a strong consumer focus, including retail, financial, communication, and marketing organizations. They do so to drill down” into their transactional data and determine price, customer preferences, product positioning, sales impact, customer satisfaction, and corporate profits. Retailers can point-of-sale records of customer purchases with data mining to develop products and services to appeal to the specific target audience.
Things to know about data mining
Ideally, data mining is an automatic or semi-automatic technical process that helps analyze the vast amount of scattered information to make some sense and transform the same into knowledge. Data mining considers anomalies, patterns, or correlations to aid the plethora of records to analyse the results. As per recent research, it was found that at least 90% of world data is from 2014, and every 1.2 years, its volume doubles.
Why do you need to use data mining?
You can quickly discover patterns and relationships within that data to be valuable to an organization. It is the same thing that data mining does and help companies in better decision making with connections and insights. The best part about data mining is that it minimizes risk, which allows you to learn about fraud, errors, and inconsistencies, which then leads to profit loss and reputation damage. Many industries use data mining in various contexts, but the plan doesn’t change that is to understand customers and their business in a better way.
Some of the applications of data mining:
- Health care: Data mining is said to be holding a fantastic potential to enhance the health systems. It implements data and analytics to learn about the best practices which will improve care and minimize costs. Experts use data mining approaches, including multi-dimensional databases, machine learning, soft computing, data visualization, and statistics. People can use data mining to analyze the volume of patients in almost all categories. Some of the processes are developed, ensuring that the patients get the most excellent care right place and at the right time. Healthcare insurers can also use data mining to detect fraud and abuse.
- Market basket analysis: Ideally, it is a modelling technique based upon a theory. If you buy a particular group of items, you are more likely to buy another group of elements. The method enables the retailer to learn about the purchase behaviour of a customer. The information might help the retailer understand the buyers’ needs and change the layout of the store. It uses different analysis comparisons of results between various stores in various demographic groups.
- Education: Educational Data Mining is a newly emerging field that concerns developing methods and discovering knowledge featuring data originating, leading to educational Environments. Educational data mining aims to learn about the students’ future learning behaviour and learn the effects of academic support. Even an institution can use data mining to make accurate decisions to help the results of the student. After the results, the institution can focus on how to improve the teaching method. They can learn about the learning patterns of the student and then develop other teaching practices.
- Manufacturing Engineering: One of the best assets a manufacturing enterprise would possess is manufacturing. When it comes to discovering patterns in the complex manufacturing process, data mining tools can be handy. Data mining is mainly used in system-level designing to learn about the relationships between product architecture, product portfolio, and customer needs data. You can also use the same to learn about the product development span time, cost, and dependencies besides other activities.
- CRM- it is also known as customer relationship management. It mainly includes acquiring and retaining customers, which helps enhance customers’ loyalty and implement customer-focused strategies. A business needs to collect data and analyze the information to maintain a proper relationship with a customer. Here is where data mining plays a crucial role. The data collected can be useful for analysis with data mining technologies. The solution gets filtered results rather than being confused about where to focus on retaining customers.
- Detecting the fraud: The majority of the people have lost billions of dollars in the wake of ongoing fraud. Ideally, the typical fraud detection method is both complex and time-consuming. Data mining helps in giving meaningful patterns and turning data into information. Knowledge is nothing but any information that is valid and useful. An ideal fraud detection system must protect the data of all individuals. A supervised method is a collection of sample records, and they are both fraudulent or non-fraudulent. The algorithm is made to learn if the record is fraudulent or not as the technique is built using this data.
- Intrusion Detection: Ideally, the intrusion is any action that will compromise the resource’s integrity and confidentiality. It includes user authentication, avoid programming errors and information protection under defensive measures to prevent an intrusion. By integrating a level of focus to anomaly detection, data mining can help improve intrusion detection. Through data mining, an analyst can differentiate been activity and common everyday network activity.
- Lie Detection- It is easy to apprehend the criminal but bringing the truth is quite challenging. Law enforcement tends to use mining methods to analyse the crimes, monitor the communication of potential terrorists. It also includes text mining. The plan aims to learn about meaningful patterns in data that is generally unstructured text. From the previous investigations, the data sample is collected, and a model for lie detection is generated.
- Customer Segmentation: With typical market research, you can easily segment customers. On the flip side, data mining goes in deep and enhances market effectiveness. Data mining helps align the customers into a different segment and can tailor them as per the needs of customers. You can say the market is all about retaining the clients. With data mining, you can find a segment of customers depending on the vulnerability. The business can offer them special offers and improves satisfaction.
- Financial Banking: A large amount of data is supposed to be generated with new transactions with computerized banking’s prominence everywhere. By learning about patterns, causalities, and correlations in business information. Managers do not instantly see the market as the volume data is extensive or is generated too quickly to be understood by the experts. The managers mainly use this information for doing a plethora of tasks, including better segmenting, targeting, acquiring, retaining, and maintaining a profitable client.
- Corporate Surveillance: The process of monitoring a person or group’s behaviour by a corporation is Corporate surveillance. Often the data collected is used for marketing purposes or sold to other companies. Besides that, it is also shared with government agencies. Even the businesses can use the same to tailor their products desirable by their customers. For direct marketing purposes, the data can be used for the targeted advertisements on Google and Yahoo. Here the ads are targeted to the search engine’s customers by understanding their search history and emails.
- Research Analysis: As per historical records, we have seen revolutionary changes in research. When it comes to data cleaning, data pre-processing, and integration of databases, data mining is quite helpful. The analysts can learn about any similar data from the database, which might help get any change in the research. You can know about the co-occurring sequences and the correlation between any activities. Additionally, you can get a clear view of the data with data visualization and visual data mining.
- Criminal Investigation: A process that aims to identify crime characteristics is criminology. Ideally, crime analysis is all about exploring and detecting crimes and their relationships with criminals. Ideally, criminology is an appropriate field for applying data mining techniques. Thanks to the high volume of crime datasets and the complexity of relationships, the term has gained prominence.
- Bio Informatics: Data mining approaches is quite suitable for Bioinformatics, as it is data-rich. Gene finding, protein function inference, ailment diagnosis, disease prognosis, ailment treatment optimization, protein and gene interaction network reconstruction are data mining applications to bioinformatics.
Hence data search, analysis, and management are markets with several employment opportunities as of now. Data mining professionals work with databases to evaluate information and discard any useful and reliable information. Data mining professionals work with databases to assess data and discard any data.
Written By : Dr. Boudhayan Ganguly
Designation : Associate Professor – MIS and Analytics
He has a Doctorate from IIM Lucknow. Before joining IMI Kolkata, he undertook projects such as “Tourism Enterprise Survey in Bihar” (commissioned by the Bihar Government; under the technical assistance of International Financial Corporation) and “Study on Computer-Aided Learning Centres” under Sarva Shiksha Abhiyan. He has also published papers in reputed journals such as Electronic Markets, Tourism Recreation Research and International Journal of Electronic Business.