All organizations today are data-driven, which means they rely on data and use it to assess the value of their company and to come up with the right solutions for several problems. Business analysts work with data, and most use the data mining techniques to work more accurately with the data at hand.
Data mining is the process of extracting valuable information like patterns from large data sets. It is also called knowledge discovery in data. The process is done with data warehousing technology, a major business intelligence component. It consolidates data from varying sources into one common repository to help business analysts make a decision.
In the past few decades, companies have undertaken data mining techniques to transform raw and unintelligible data into meaningful and useful inputs. This technique is suited for handling large-scale data, and thus comes in handy for business analysis.
Data mining is an integral part of business analytics. It helps in taking important decisions and gives a clear picture of the situation of a business. It has two major purposes. First, to describe target data sets, and second, to predict outcomes based on the study using the machine learning algorithm. These methods filter the raw body of data and give an output that would be beneficial for the company. They organize and filter data, detect fraud and security breaches, sieve the most useful and interesting information, etc.
Therefore, data mining helps a team of analysts make accurate and near-perfect predictions regarding several aspects of the business, from sales figures to target customers to logistics. They also recognize patterns in a large body of data, making itmore readable and decipherable by eliminating unmatched variables. It also helps in identifying errors and gaps in processes or any improper data entry.
Benefits of Data Mining
Let’s take a look at how undertaking data mining projects helps different business functions.
1. Marketing and Retail
Marketing teams cannot come up with vague strategies and campaigns that have no reference point or a goal to achieve. Data is used to extract insights on customer behavior, reaction and response, and other important factors that help them come up with a strategy that will suit the needs of the business the best.
They create models using the information that data mining presents to them. They find out which campaigns work with their target audience and how they can enrich their content to target more and more people.A general marketing strategy often fails to create an impression; thus, you must try to analyze the previous records before coming up with a new one.
2. Finance and Banking
We are sure you have heard of several credit-card fraudulent cases in the last few decades. Of late, the numbers have diminished mainly due to the use of data mining in the banking sector. Technology is like a set of Infinity Stones, which could wreak havoc in the wrong hands but is also the only way to counter and check crime, in this case, card fraud.
Data mining has been able to detect credit card fraudulent transactions and has alarmed authorities to take appropriate actions. It also provides information about historical customers to the bank and other institutions todetermine their credit score and banking history.
3. Regulating Customer Groups
Data mining is useful in determining customer groups for a new product or service. This again helps in coming up with appropriate marketing strategies. It also explores and understands what keeps the customer coming back for the product and improves retention. It recognizes the preferential needs of profitable customers and improves relationships with them to maximize sales.
4. Manufacturing and Production
Data mining can predict machinery failures with the help of sensory data, and thus prevents the sudden breakdown of machinery that might push back production. This allows the company to indulge in the condition-based maintenance of their machines.
It also spots anomalies in the production system, thereby optimizing manufacturing capacity. The patterns also help the production team identify shortcomings and improve product quality.
5. Prediction of Future Trends
Every future is born out of the past! And, if you can investigate every bit of the past data, your forecasts will be on-point. Data mining considers all the data to come up with visible patterns, which in turn help analysts see the hitherto hidden details.
Disadvantages of Data Mining
Some drawbacks of data mining functionalities include:
Since the process is extremely complicated, the tools used are equally complex. Not all data scientists have knowledge of these tools and techniques.
There is no thumb rule for data mining, and different tools work differently based on the algorithm used. Therefore, if you choose the same tool for every mining, you will probably have the wrong results. Therefore, having impeccable knowledge is very important.
8. Not 100% Accurate
While data mining gives you near-accurate results, it does not guarantee 100% accuracy. Technologies used for data mining are not infallible, and inaccuracies often slip in when the dataset is lacking in diversity.
9. Needs Large Database
The process cannot function without a vast database, and thus is difficult to manage.
10. Security Concerns
Data mining firms have access to all the data of a company and might sell the data to other organizations and businesses. This is a case of concern as it carries every little detail of the business.
Data Mining Applications
Some of the most popular data mining applications include:
11. Sales and Marketing
A large amount of data sits in a company's database. Consumer demographic and user behavior can be used to optimize marketing campaigns, create customer loyalty programs and cross-sell offers, and yield higher ROI on marketing efforts. Predictive analysis helps teams to estimate yields and accordingly talk to stakeholders.
Educational institutions use data to understand their students and ways that will help them flourish better. Online classes have increased the importance of data in the world.
A lot of factors like student profile, class, and universities, are taken into consideration when coming up with useful results.
13. Fraud Detection
Occurring patterns are not the only noticeable detail in the database. Repetitive anomalies can also lead you somewhere. These anomalies bring fraud to light and help companies detect what might have gotten lost in the vast body of data. It is mostly used in banking sectors and other financial institutions. However, of late, SaaS-based companies have also adopted these techniques. It helps them recognize and eliminate fake accounts.
Types of Data Mining Techniques
While there are several techniques one can use when undertaking data mining projects, here are some popular ones.
14. Classification Analysis
This type of data mining involves retrieving important information relevant to the data and metadata. It classifies and assigns different classes to the data available. It is a segmentation of data into different groups. A data analyst knows the different classes and would apply the appropriate algorithms to decide how data should be classified.
15. Association Rule Learning
It is a method that detects relations between variables in a database. This technique helps you see through obscure patterns that will eventually help to identify variables within the dataset. This type of mining is common to shopping basket data analysis, store layout, catalogue design, etc.
16. Clustering Analysis
Clustering is not very different from classification, but, in this case, clusters are made based on similarities of data items. There are further divisions in clustering methods as there are different types. Some of the methods are:
- Hierarchical agglomerative methods
- Density-based methods
- Partitioning methods
- Grid-based methods
- Model-based methods
A decision tree has a structure similar to that of a tree, as the name suggests. It works like a flow chart, and different parts have a definite role to play in producing the final result.
2. Anomaly Analysis
This technique is used to deal with data that does not fall into any expected behavioral pattern. These miscellaneous data are also called noise or outliers. These are extremely helpful in detecting fraud and intrusion detection.
3. Use Cases
Data mining comes with immense capacity that has transformed the landscape of business strategies. Here are some of the use cases:
- Television and radio
- Service providers
- Crime investigation
All the above sectors use data mining to achieve their individual goals and so far have done great.
How does it Fit into the Data Science and Data Analysis Process?
Both data science and data analysis work with data, and their major concern is to extract the best posiible insights that they can from the database. Data mining helps them in achieving their goals by rendering the best techniques that can be applied to come up with visible patterns from within the data.
Both the fields benefit from data mining and can use their potential to the maximum. Every sector today uses business analytics. And, with data mining, they will reap the maximum benefits. Analysts today are stressing the importance of mining for the right reasons.
The application of business analytics and data science has become integral for business problem solving data mining only enhances and supports it. Knowledge of data mining comes as a bonus when you are looking for work in the field of data analytics. No matter which company you join, your knowledge will not go unnoticed.
It is an important skill that improves the data structure and gives you a chance to accurate the data in the best possible way. It serves as one of the best tools to handle data and thus cannot be overlooked. Do not shy away from learning data mining science and mining. You can enrol yourself in a great program or course that teaches about data science, mining, and how to sharpen your skills. Notably, an upgraded CV is always more attractive to employers than a plain one having mundane degrees only.
Hero Vired offers the three best programs to suit your needs. Learn about data science and more with these three courses.
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Integrated Program in Data Science, Machine Learning, and Artificial Intelligence - This program is especially for candidates who wish to strengthen their knowledge of machine learning, mathematics, and statistics. It will polish your analytical skills and make your data-driven decisions on point. This course is designed in partnership with MIT, and you also earn transferable credits.