What is Data Mining? An all-inclusive beginner guide
What is Data Mining? An all-inclusive beginner guide2023-07-05T11:07:57+00:00
The process of data mining is used to detect abnormalities or inconsistencies, patterns, and correlations within data sets to anticipate outcomes. People performing data mining apply a number of techniques to generate important and meaningful inferences that help businesses to boost their revenues, reduce costs, address market risks, gain new customers, and strengthen their relationships with their new customers.
What is data mining?
Data mining is the process of filtering out similar data from a large chunk or batches of Big Data using specific software for the purpose. Data mining also involves identifying similarities or patterns between data sets that help businesses gather analytics on user behavior and use those analytics to develop effective marketing strategies.
Another great aspect of data mining is that it can efficiently predict future trends that businesses can leverage and use to make informed decisions. With the global trend of digital transformation sweeping across industries worldwide, data mining has gained traction among marketers seeking to be future-ready and solve business problems.
The Importance of Data Mining in the Modern-Day
Businesses struggle with numbers as they have to deal with large volumes of data, which cannot be interpreted often. Unstructured data, which accounts for 90 percent of the digital information, is useless unless and until you are able to interpret them into meaningful information that can provide vital insights and knowledge to businesses.
With data mining, an analyst will be able to:
Organize chaotic and repetitive data into meaningful formats.
Identify facts and figures that really matter to you and use the decoded information fruitfully to get expected results.
Use meaningful and insightful data to make informed decisions.
Techniques to Mine Data
Analysts apply a broad range of data mining techniques to find relevant data and interpret them as per specific business requirements. Here are the common techniques involved:
Classification is the process that analysts use to retrieve important data and metadata information. This process is applied to categorize data into various classes.
This type of analysis is a data mining process that is used to identify similar type of data. Clustering helps analysts to identify the similarities and differences between data.
Analysts perform regression analysis to identify and analyze how variables relate to each other. This process is used to find the probability of a specific variable in the presence of other variables.
Analysts apply this technique to identify the co-relationship between two or more items. This technique is used to reveal a hidden pattern in a set of data.
This technique is used to look for items in a data set, which do not demonstrate an expected behavior or follow an expected pattern. Analysts apply this technique in diverse domains including fraud detection, intrusion, etc. This technique is also referred to as Outlier Mining or Outlier Analysis.
This technique is used to detect similar trends or patterns in transaction data for a definite time period.
This technique is actually a combination of multiple techniques such as classification, clustering, trend analysis, sequential patterns, etc. This technique is used to predict future events through an analysis of past instances or events in a proper sequence.
What are the Benefits of Data Mining?
With this process, businesses get access to knowledge-based information.
Insights allow businesses to make sensible and profitable operation and production-related decisions.
It is much more cost-effective than other statistical data analysis processes.
It allows for automated trend projections and automated revelation of hidden patterns.
The process can be applied in both existing and new systems.
The process facilitates the analysis of huge volumes of data in very less time.
Companies may sell critical consumer data to other companies to earn money. American Express, for example, had sold its credit card sales-related information to other companies.
Applications may often prove to be difficult to understand and work with. Such software demand thorough training and knowledge.
Choosing the most appropriate tool to meet a specific project objective is a difficult task. This is mainly because of the fact that various tools are based on different algorithms and hence, they function in different ways.
The techniques may not prove to be accurate and so it may lead to undesirable outcomes under specific circumstances.
Top 14 areas that are benefitted by data mining
Data mining has found practical application and widespread use in a number of areas such as:
Data mining can positively transform the healthcare system in times to come. Data analytics can help in the identification of best practices that would enhance the level of care and make processes more cost-effective. Methods such as soft computing, machine learning, data visualization, and statistical analysis are used to project patient-volume in every category and to ensure that every patient gets the right level of care at the right time. By using different techniques, insurers can also detect fraud.
Market Basket Analysis
This modeling method is based on the concept that if a buyer buys a certain set of items, he is most likely to buy another set of items. The technique helps in determining purchase behavior, thereby allowing retailers to modify their stores’ layout as per their buyers’ needs.
Educational data mining is a new field and it is directed towards determining students’ learning behavior and finding the impact of educational developmental programs. Educational institutions can use this process to anticipate students’ results and to make decisions. Institutions can identify their students’ learning patterns and can develop appropriate teaching techniques.
The process can reveal patterns in complicated manufacturing processes and can be used to identify relationships between product portfolio, product architecture, and data on customer needs. It can also be used to anticipate costs and the duration of product development.
Customer Relationship Management
Businesses need information and proper insights to retain customer loyalty and make customer-focused strategies. Using data mining technologies, businesses can identify the areas that they should focus on in order to retain their customers.
The process converts raw data into meaningful information and insights. It helps in revealing meaningful patterns that can facilitate the fraud detection process and supports the creation of a model that can detect whether a record is genuine or fraudulent.
Processes applied to mine data facilitate anomaly detection and this way, it can help in the detection of intrusion. An analyst is able to spot a new activity from day-to-day, common network activity. The process promotes the extraction of data that is more appropriate to address certain scenarios.
Data mining combined with text mining can help in crime investigations as well as in communication-monitoring of suspects. This process can reveal meaningful patterns in unstructured text. A lie detection model can be created using data samples that are obtained from previous investigations. This model can help in the creation of appropriate process to facilitate further investigations.
Data mining gives deeper insights compared to traditional market research. It allows businesses to categorize customers into certain groups and tailor their services as per their needs. The process can help reveal vulnerable customers, thereby allowing businesses to design special offers for them.
With data mining, analysts can address complex problems in the banking and finance industry. The process enables analysts to identify patterns and correlations in market prices and business information, which are often difficult to be identified due to huge data volume. These patterns help managers in developing appropriate strategies for targeting, segmenting, acquiring, and retaining a loyal customer base.
Corporate surveillance statistics are basically used for marketing purposes. This data can be used by businesses to customize their products as per the needs of their customers. The data can be applied in an appropriate manner to create targeted ads on Yahoo and Google on the basis of customers’ search history.
Mining and analysis of data support database integration, data pre-processing and data cleaning. Analysts can identify similar data, which may cause a change in the research. Data visualization may reveal co-occurring sequences, which may allow analysts to find relationships between activities.
The process facilitates crime analysis. It is an appropriate method for crime data analysis owing to the complexity and large volume of data. With this process, it is possible to convert text-based reports into word-processing files. The information supports the crime matching process.
Data mining can be used to extract vital knowledge in the fields of medicine, biology, and neuroscience. This process can be used to find important information about disease diagnosis, gene finding, treatment optimization, protein sub-cellular location calculation, gene interaction network, disease prognosis and diagnosis, etc.
Summing it Up
The purpose of data mining is to explain past events and predict future events. With widespread use in diverse industries such as communications, education, retail, banking, Ecommerce, insurance, and life sciences, data mining has emerged as a leading option for businesses to address key market issues and retain their competitive edge in the industry. If you are running a business and looking for data mining services, we, at ProGlobalBusinessSolutions (PGBS), are always ready to deliver world-class support. Our analysts are adept at the use of advanced data mining technologies and can deliver professional data analytics support, thereby helping you to stay ahead of the competition and make an optimal use of the available data.