Weighing the Benefits and Limitations of Data Mining

The idea of Data Mining is growing in popularity in business activities. We are living in a data-driven age and we have been producing more and more data in every area that you might think about. Each time you make a sale, there’s data being transferring into a database, and there’s some sort of data download in most transactions you perform. With the increasing amount of available data, it is necessary to make use of this huge resource.

Data Mining is the practice of collecting real, new, comprehensible, and meaningful information from large databases and making important business decisions with it. Data Mining applies across all industries and fields. Generally speaking, wherever there are processes and wherever there is data, the application of the powerful mathematical techniques that make data mining possible remains applicable to make data usable for the processes which show that data is the solution to all problems.

But there always is a downside to the story. Data mining, no matter how useful it is, poses some real issues in its application. In this article, we will compare the pros and cons of this field and see how the Benefits outweigh the Limitations which ultimately makes the field successful.

Benefits of Data Mining

We can only make sense of the benefits of some fields when we look at their applications in real life. So here we will discuss the benefits of data mining in different professions of daily life.


Manufacturing is the field that runs our world. Without this process, we can’t experience the true beauty of life. Manufacturers can identify faulty equipment and check optimal control parameters by introducing data mining in operational data. Data mining applies to determine the control parameter ranges that lead to quality control in industries. They also help to ensure that similar products are producing according to the demand at all branches of that business.

Moreover, data mining tools have proven to be beneficial in determining patterns in complex manufacturing processes. It is using in system-level designing to building the relationships between product portfolio, its architecture, and the data needs of the customers. Furthermore, it could enjoy forecasting the product development period and cost among the other tasks. Yet, there are a few key components that need consideration before mulling over how to mine data for manufacturing i.e. determining the ‘right data’.


Data mining is one of the most effective techniques that help researchers to excerpt important information from huge sets of data. It can assist researchers by speeding up their analysis of the process. It helps to identify different patterns in the data available. Most of the time while designing some strategies or systems, one might experience some sort of unexpected issues. Then they can use data mining to extract and process-related data to resolve these issues.

Modern researchers in different fields experience an unprecedented complexity of data. Yet, the results provided to the researchers via traditional data analysis techniques offer limited solutions to such difficult conditions.

Finance/ Banking

Data mining provides information about loans and credit reporting to financial institutions like banks and insurance firms. By building a model from the data of regular clients, these financial institutions can assess both good and bad investing. Additionally, data mining helps banks identify fraudulent credit card transactions to protect the owner’s privacy.

The digitalization of the banking system is there to generate a huge amount of data with each new transaction. The data mining technique can help bankers by solving business-related concerns in banking and finance — identifying trends, casualties, and relationships in business information and market-cost that aren’t visible to executives or managers due to large data volume or are discoverable on the screen by experts. The manager may find these data for better focusing, obtaining, retaining, segmenting, and sustain a loyal and lucrative customer.


Like researchers, Data mining helps marketing firms create historical data-based models to predict who will respond to new marketing campaigns and what will be the right medium such as direct mail, online marketing campaigns, etc. Using the results, advertisers can come up with a suitable strategy for selling profitable products to targeted clients.

All businesses use data mining for marketing as it helps to forecast potential risks, boosts sales, reduces costs, and enhances consumer satisfaction. Also, it helps in competition analysis, market segmentation, and audience targeting or customer acquisition.


Above all the aforementioned fields comes education. Education data mining is an advanced emerging field with a focus on developing techniques that explore facts from the data generated from educational environments. Thus applying data mining in the education industry will have long-lasting effects on the growth of our world. In the academic industry, data mining methods will be in use to evaluate student learning behavior, success, and even predict how the student will do during a test.

Data Mining can also be in use to design a better curriculum according to specific needs, as well as encouraging learning science. Moreover, an organization can use data mining to make accurate decisions and forecast the results of the student. Thus, applying data mining in the education industry will have long-lasting effects on the growth of our world.

Limitations of Data Mining


Security is a big issue attached to every data-oriented technology. When huge data is being assembled in data mining systems, the possibility of critical data getting hacked by hackers is always there since this has happened with one of the biggest companies like Ford Motors. Organizations own their employee and customer information including social security number, birthday, payroll, and much more. But how well this knowledge is being taken care of is still questionable………………

Read Full Story at https://autome.me on January 23, 2020.

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