Pros and Cons of Data Mining Explained
What are the pros and cons of data mining?
Data mining is sorting data according to your needs. It refers to the process of extracting a large number of consistent data patterns, capable of generating valuable insights.
Data mining came about intending to help to understand a vast amount of data. This could then be used to conclude to contribute to the improvement and growth of companies.
The analysis of data through data mining can provide countless advantages to companies for the optimization of their management and time. However, there can also be some inconvenience when using data mining techniques.
Let’s take a closer look at these pros and cons of data mining to know if it is worth investing.
- Pros of Data Mining
- Customer Relationship Management
- Competitive Advantage
- Attract Customers
- Anomaly Detection
- Cons of Data Mining
- Violates User Privacy
- Incorrect Information
Pros of Data Mining
Better Customer Relationship Management
Being able to ensure proper customer relationship management is one of the critical advantages of data mining.
It helps businesses know what type of customers to approach with different kinds of products. This guarantees the sale of the product and not the pitching of the product.
It also helps companies in choosing different marketing strategies depending upon the demography of their customers.
Forecasting Market Trends
Marketing and retailing depend on the current market trends that are followed by customers.
Data mining allows these industries to find the correct trends through market research, which, in turn, helps them in choosing their marketing strategies.
It also allows them to predict trends and products that customers would be interested in. It helps companies decide what type of products to bring into the market.
Helps Stay Ahead of Competition
With so much new data, well analyzed, your R&D department will be at the forefront of trends and will be able to think about the next product.
One strategy that can be used with data mining is to compare the information gathered from your company with that of your main competitors. With this, you can improve your marketing, develop better products and services, and even strengthen your brand.
Data mining offers many advantages over the possibilities of personalization, consistency with the current and future needs of consumers.
Attract and Retain Customers
Today’s consumers crave for a personalized and interactive experience with the companies they collaborate with.
Thus, the only way to win and retain the customer is to understand their behavior, characteristics, and preferences deeply.
Data mining enables quick and efficient analysis of the main attributes of consumers. Thus, your advertising campaigns can be targeted precisely to the people who are most likely to become consumers.
Anomaly Detection with more accurate analysis
The analysis is much more careful with data mining since it is possible to classify all the information according to the priorities that you previously identified. It is capable of analyzing databases with a massive amount of data.
Data mining can become very useful for various financial institutions. Banks and credit card companies can obtain information on loans and know the creditworthiness of customers.
It also helps credit card companies by providing details about the frauds. These are the most important advantages of data mining as it helps financial institutions reduce their losses.
Cons of Data Mining
Expensive in the Initial Stage
With a large amount of data getting generated every day, it is pretty much evident that it will draw a lot of expenses associated with its storage as well as maintenance. This is one of the main disadvantages of data mining.
To successfully operate data mining, your company needs the appropriate specialists.
Depending on the type of data you want to collect, a lot of work may be required, or sometimes the initial investment to obtain the technologies needed for data collection can be very expensive.
Security of the Critical Data
Companies hold a lot of critical information on their customers and employees as well. There’s always a risk of being hacked, as a massive amount of valuable data gets stored in the data mining systems.
Below mentioned are the key security issues related to data mining:
- Minimal protection setup
- Access controls
- Non-Verified data updating
- Security architect evaluation
- Data anonymization
- Filtering & validating external sources
- Data storage location
- Distributed frameworks for data
Data Mining Violates User Privacy
It is comprehended that data mining uses market-based techniques to gather data on people. Most of the time, private information that companies hold is traded to others or leaked.
Organizations gather information on their consumers in several ways to understand their purchasing behavior and much more.
However, using unethical methods to collect this information and then using it for personal gain violates user privacy.
Lack of Precision or Incorrect Information
The data mining tools analyze data without actually knowing its meaning. They present the results in the form of various visualizations. However, these patterns are not meaningful by themselves, but only after the user has assessed them.
Pre-processing errors lead to inconclusive or incorrect results. In practice, inaccurate data entry often creates problems. Systematic deviations or distortions are further sources of error.
For example, it is impossible to draw representative knowledge from non-representative customer data. Hence, if incorrect information is applied for decision-making, it can cause severe outcomes.
In times of big data, it is not easy to find data that is, in fact, relevant to your purpose. Therefore, the use of data mining is an excellent way to optimize the process of analysis and application of pertinent information.
On the contrary, it also has certain limitations to it, as mentioned above. Though the advantages of data mining outweigh the disadvantages, we recommend you take careful considerations before launching a new data mining project.