We are living in a data-driven world, the more we get dependent upon technology the more the data is generated. There is an abundance of readily available knowledge, but a larger volume of this data can create a challenge as it would take a longer time to find the useful data a user searched. Thus to tackle this problem, in this article, we will be discussing data mining, its applications, and its advantages.
What is Data Mining?
Data mining is the process of analyzing large data sets or data warehouses for the extraction of useful information with the help of computers, automation tools, and a wide range of techniques. It is also known as KDD or Knowledge Discovery in Data.
In simple words, data mining is a process in which the set of raw data available is extracted to gather useful information from it.
As a branch of Data science, data mining is a wide concept that includes establishing relationships, finding problems, artificial intelligence and machine learning(developing algorithms to predict the behavior or outcome), etc.
Importance of Data mining
- With the help of data mining a large stack of data from multiple sources can be analyzed easily for patterns and relationships.
- Data mining helps in making predictions, smart decisions.
- Most importantly with the help of data collected from various users the data mining can let you explore the ideas you never thought of .i.e thinking out of box.
Process of Data Mining
Data mining is an interactive process consisting of six steps.
- Understanding the Business Objectives – This is the first and very important step for the start of the process, data scientists and various stakeholders all work together to understand the objectives or scope of the business. Once a clear understanding is made we move to a new phase.
- Collection of Data – In this phase, data mining experts are hired to gather the relevant data from various fields.i.e.social sites, service data, etc and are stored in a data warehouse.
- Preparation of Data – It is a time-consuming step as the data gathered in the previous step is now cleaned which consists of three phases.
- Extraction – The data is extracted from various sources and stored in warehouses.
- Transforming – The data is cleaned.i.e removal of duplicate data, updating the missing values,etc.
- Loading – Now, the data collected from the previous two steps is fed to the database.
- Model Building – Selection of an appropriate model i.e.(clustering,regression analysis) is done based on the analysis of data done before. In this phase various tools, algorithms, statistical and mathematical approaches are applied.
- Evaluation – Once the model is ready and all the values of data are aggregated, it’s time to evaluate the results of the model developed must meet the objectives set in phase 1.
- Deployment – After the model has been evaluated its time for its deployment is done in the form of graphs or spreadsheets.
Applications of Data Mining
There are several applications of data mining that helps businesses or organization to have an advantage over their competitors.
Insurance Companies – With the help of data analytics, insurance companies can solve fraud problems, customer attrition, etc. Data mining lets these companies find a new way to target customers and offer productive prices to the existing customers.
Education – The education sector is the new field in which data mining is used to predict goals of learning. Data mining helps the institutes to get the best results and to know the group of students that need extra attention.
Banking – Data mining in banking helps to understand customer behavior with automated algorithms. It also helps the banks to detect fraud alerts and to keep track of a customer’s purchasing history to provide them with various banking facilities i.e. Credit Cards, loans, etc.
Marketing – Marketing is the best-benefited field from data mining. It helps the organization or institutes to understand better about customer behavior. Data mining helps bring all together on several criteria i.e. age, purchase history, income level, locations, etc. With these several benefits, companies can retain their customers for a long time period by targeting their specific needs and requirements.
Retail – With the help of marketing, data insights the retail businesses such as grocery shops, dairy shops, etc. can know about the customer’s behavior in their locality and can stock the items as per the need.
Manufacturing – For the manufacturer, data mining can help in the early detection of problems, maintenance of products, etc. It can also help to design the product as per the customer needs at system level designing.
Healthcare – With the help of data mining doctors are able to know their patients a lot better and diagnose the problem easily. Data mining helps to enhance health care facilities at a reduced cost. It also helps the organizations in the detection of fraud alerts, waste, and manages cost-effective relationships between the patients and hospitals.
Advantages of Data Mining
The advantages of data mining are as follows-
- Helping the organizations to gather authentic and correct information.
- It can be easily inducted to new plus existing platforms.
- With the help of data mining an organization can create improved plans and decisions.
- It is cost effective.
- It helps in reducing the cost of products by creating a competition against various companies.
- Data mining helps in early detection of frauds, defects in complex designs,etc.
Disadvantages of Data Mining
- Data mining is not always unerring and in certain cases can lead to repercussion.
- A large database is required to go for mining thus making the process hard.
- Selection of the right tool for a certain business is a cumbersome task as each tool has a different algorithm.
- Data mining is hard and complex, thus a proper training about various tools is required.
Finding correct and required data is a mere challenging task when there is a heap of data present and generated continuously. Data mining is growing rapidly, and every organization needs to benefit. Thus, it’s the end of the article.
Kuldeep is the founder and lead author of ArtOfTesting. He is skilled in test automation, performance testing, big data, and CI-CD. He brings his decade of experience to his current role where he is dedicated to educating the QA professionals. You can connect with him on LinkedIn.