Why data mining is important ?

Introduction

Generally, when someone mentions the word ‘mining’, the first thing that comes to mind is workers wearing yellow helmets with flashlights. A person not wholly aware of the field of computers and the internet would inevitably imagine such a scenario where the workers are digging, extracting, and mining for valuable resources. However, when it comes to technical aspects, data mining is a lot different than merely digging deep into the frameworks of the computer to find patterns of 0s and 1s.

On the contrary, some of you might have heard the term ‘data mining in its essence and wondered ‘why data mining is important?’. Though the process seems like it involves a plethora of knowledge and deep understanding, this article will help you with all the aspects of data mining, its importance, and its applications.

What is data mining

The contemporary world has surpassed ancient times in terms of data availability. It might be a gift of the advancements in information technology, that we now have access to unfathomable data and knowledge. This advantage might come in handy, but as the saying goes, good and bad go together.

Not every knowledge, data, and information is helpful to everyone. The sheer volumes of knowledge and data spread throughout the web make it difficult and time-consuming to dig out the most valuable insights.

Businesses today are data-driven. Corporates that consider data their valuable asset are bound to be successful as long as they efficiently and smartly use the data for the business. With the internet, the scope and opportunities for businesses have increased immensely. In the old days, companies typically operated in areas where they had ease of access and knew about the market. Now the digital world has transformed businesses as they can operate from virtually anywhere. Data can help corporates understand the market, consumer behaviour, purchasing power, trends, patterns, etc. and thus can work like never before. Still wondering how? After going through this article, you’ll understand how and why data mining is important.

Data mining is sometimes called Knowledge Discovery in Data (KDD). In its essence, data mining is extracting valuable knowledge/insights from a surfeit of data sets and information. In simpler terms, searching, collecting, filtering, and analyzing data is data mining.

The internet provides ideally unlimited data. Through that, it is possible to collect sheer volumes of data. Data analysis comes into play afterwards, leading to advantageous uses of the data acquired. Governments, large organizations, and much rather all businesses, to an extent, perform data mining to gather information necessary for data analytics and in-depth business insights.

Why is data mining important?

When large sets of data are acquired, the primary motive is to identify the visions and insights of that data. This identification is carried out by data mining. In the current era of information technology, the demands of the data industry are growing at an unprecedented rate. Consequently, increasing the need for data scientists and data analysts.

Through data mining, all the available data is analyzed, filtered, and then converted into meaningful and valuable information. Subsequently, the resultant information turns out to be helpful in terms of making accurate and better decisions for the business. The knowledge acquired through data mining is useful for making intelligent market decisions, making accurate predictions, creating relatable and efficient campaigns, and finding out the trends and the patterns of value.

Fundamentally, customer behaviour and relevant insights enable businesses to target their audience appropriately, resulting in business success based on data mining.

The process of data mining

Data mining requires profound skills and knowledge of the process. The process is interactive while being highly time-consuming and lengthy and is often divided into numerous steps:

Understanding the business and requirement gathering

In the first step, the data analysts initially understand the company’s current standing. The project’s objectives are marked, and the data analysts understand and gather the necessary prerequisites. Afterwards, the scope is clearly defined from the business perspective.

Exploring the Data:

Once the scope is clearly defined, the data mining experts gather information and evaluate them in terms of problem finding and solving. Then, the collected and evaluated information is converted into metadata and data patterns through data mining statistics.

Preparing the Data:

In this step, the acquired data is converted into useful information for the next step. Data mining experts typically use the ETL process to extract, transform, and load. New data attributes are created, and data is presented in a structural format, keeping the data sets’ meaning unchanged.

Modelling the Data:

Modelling plays a pivotal role in the complete processing of data. Through this step, the data is filtered appropriately. Algorithms are employed, and the models are created, tested and evaluated.

Evaluating the Data:

Evaluation and modelling are entirely correlated steps. This process can simply be called the filtering process after modelling. If the outcome of the step is not up to the mark, the data is transferred to the model repeatedly until the outcome is quality proven. Once the outcome is acquired, the requirements are cross-checked with the scope so no point is left untouched.

Deploying the Data:

Once all the above steps are carried out, and a satisfactory final outcome has been achieved, the data mining experts present the data in graphs or spreadsheets. The data is mainly in the form of solutions acceding to the positive essentially of ‘why data mining is important?’.

Data mining tools and techniques

There is a renowned saying, ‘right tools for the right job.’ Since data mining deals with a considerable volume of data, efficiency is lost without the right tools, and the business’s inability to make real-time decisions overwhelms the visions of success. For that, there are numerous data mining tools which include:

Artificial Intelligence

The AI systems carry out analytical functions close to how a human would have done. As a result, they dig out problem-solving strategies and reasoning whilst learning and planning simultaneously.

Association Rule Learning (ARL)

The market basket analysis tool. The ARL aims to find relationships among the variables of the dataset. For instance, Amazon uses this tool to search products purchased frequently together, e.g. a phone and a protective case, a CPU and a monitor, etc.

Clustering

The data is partitioned into subclasses called clusters. Through this process, the data analysts find grouping within the data sets.

Data Analytics

Digital information is evaluated and transformed into helpful business intel.

Data Warehousing

Since information gathering can be time-consuming, businesses store helpful information for later use or to make better decisions accordingly.

Machine Learning

Machine learning, in essence, is somewhat similar to AI. It is mainly a computer programming technique aimed at employing specific statistical probabilities that enable the computer to learn without human intervention.

Oracle Data Mining (ODM)

It is primarily the Oracle Advanced Analytics Database module. The data analysts generate in-depth insights and make accurate predictions through this tool. The tool is useful for understanding consumer behaviour, developing customer profiles, and marking cross-selling opportunities.

R.

It is an open-source language responsible for statistical and graphical computing. Through the tool, the data analysts have a wide range of statistical tools to choose from, alongside various graphical techniques.

Benefits of Data mining

At this point in the article, you have gone through the significant aspects of data mining. You also would have gotten a slight hint of why data mining is essential. However, there is more to the importance of data mining than mere data evaluation, problem-solving and gathering insights. The benefits of data are abundant and are discussed as follows:

Data analysts use data mining to generate models and predict trends and patterns based on historical data. The data mining experts create compelling campaigns and market strategies through the gathered information. Eventually, the entire process results in the rapid growth of the organization.
Governments use data mining to track and analyze transactions and financial data, modelling them into their use.
Similar to marketing companies, the retail industry also benefits from data mining. Data mining data analysts generate predictive models for their goods and services. Moreover, data mining can substantially improve productivity and give detailed customer insights. Furthermore, historical data plays a vital role in discounts and redemption codes, which, when undergoing data mining, become increasingly important to the business.

Banks acquire financial data, updates, and benefits through data mining. For that, the banks typically generate a model based on the customer data and then check out the loan process. All of this is solely because of the application of data mining.
The engineering data acquired through data mining can help the manufacturing industries detect faulty products and devices. When the process of data mining takes place, it results in the organization cutting off the defective items and ensuring the customers get the best goods.
Organizations that regularly carry out data mining can considerably improve their decision-making and planning.

Data mining, if done rightly, can aid the business in finding new and unique revenue streams. Having multiple revenue streams screams success for the company.
It is now a well-established fact that data mining helps in making predictions. However, it can also aid in developing new products and services.
Since data mining can give the business customer insights like behaviour, purchasing power, trends, etc., businesses can therefore interact with their customers through interactive campaigns or can provide products and goods the customers need. Moreover, being a business in touch with the customers allows for more opportunities to discover the problems and devise strategies to ensure they are solved.

Data mining helps a business gain a competitive advantage. Once a business gets that, data mining can also help reduce the cost.

The above-mentioned benefits speak for themselves, which is why data mining is crucial in almost every field of life today. From the healthcare to the retail industry, data mining is improving the success of every data-driven business.

Problems with data mining

Though data mining has numerous advantages, the process is not as easy and ideal as it seems. There are also certain drawbacks or limitations with data mining. These include:

Difficulty or the inability to access data. Sometimes unavailability of data makes the process of data mining difficult.
Sometimes a data analyst might stumble into substantial data sets that need a distributed approach. Such can make data mining a bit tricky.
Data mining can not be done without the right tools and techniques and requires powerful software. Purchasing and maintaining this powerful software can become highly expensive at times. Similarly, the maintenance of hardware and servers associated with data mining also adds to the cost.

Unbalanced, dynamic, and cost-sensitive data can make the process of data mining burdensome to the organization, particularly in newer aspects.
Data mining involves structuring unstructured data. The process can become onerous if there are large volumes of unstructured data.
The massive volume of output from the data mining techniques and tools can be strenuous to deal with.

Conclusion

Data mining involves extracting valuable insights and information from sheer volumes of data. The extracted information is beneficial to businesses, which are data-driven in the current age of IT.

The process of data mining is divided into numerous steps to achieve good outcomes. However, human capacity and efficiency can not fathom the sheer volumes of data on the webpages or databases. Therefore, it is quintessential to use techniques and tools like Artificial Intelligence, Machine learning, R., ODM, Data Warehousing etc., to get the best of data mining.

The insights acquired through data mining aid in understanding customer behaviour, patterns, and trends. Eventually, the business model and strategies are adapted to the information obtained through data mining. Such guarantees the success of a business because of the multiple benefits associated with data mining.

Data mining can become hard at times. Therefore, it is best done by the data analysts or data mining experts who know their way around even if a difficulty were to befall.