7 Data Mining Applications And Examples You Should Know

The fact that big data and analytics change the business and science world is undeniable. But how? What are data mining applications, examples, and benefits?

Data scientists already saw how machine learning and the uses of data mining techniques deliver results.




However, still many people don’t know how this exactly revolutionizes industries and people’s lives.

If you wonder what the benefits and application areas of data mining are, then you’re in the right post.

On this page:

  • What is data mining?
  • 7 key industry applications of data mining in:
    – Business
    – Telecommunications
    – Banking Sector
    – E-commerce
    – Finance
    – Medicine and Healthcare
    – Security (Information and Cybersecurity)
  • Infographics in PDF

What is Data Mining?

Let’s define it.

Simply, data mining is the process of finding patterns, trends, and anomalies within large data sets to take adequate decisions and to predict outcomes.

Now, there is an enormous amount of data available anywhere, anytime. But this data is worthless for the management decisions until it is turned into useful information. Click To Tweet

This is where data mining comes to play. It turns raw unstructured data into useful information.

Through a wide range of techniques and statistical algorithms, data mining is able to help businesses increase revenues, reduce costs, or answer questions that bother many other industries.

So, let’s sum the key data mining characteristics:

  • Discovery of anomalies, patterns, correlations, and trends.
  • Prediction of likely outcomes.
  • Answering specific questions.
  • Thus it is a solid basis for an efficient data-driven decision-making process.
  • Focus on big data sets and databases.

How can you use data mining?

Organizations and businesses use data analytics software tools to transform raw data into actionable insights by applying algorithms and automated processes.

There is a huge range of data mining companies and solutions available on the market.

The software programs help companies discover patterns and trends in big data volumes, convert those into actionable solutions, and predict possible outcomes.

Now, let’s see data mining benefits in real use.

7 Key Data Mining Applications And Examples

1. Data Mining Applications in Business

Data Mining Applications in Business - infographic

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In today’s highly competitive business world, data mining is of a great importance. A new concept of Business Intelligence data mining (BI) is growing now.

BI is widely used by leading companies to stay ahead of their competitors.

Business Intelligence is a software-driven process for analyzing data used for competition analysis, market segmentation, improving customer satisfaction, reducing costs, increasing sales, predicting possible risks, market intelligence, and etc.

Let’s take an example by Starbucks data mining.

Starbucks utilizes data to discover the best locations for their stores. Starbucks locations do very profitably due to data mining and BI.

Starbucks uses location-based data such as street traffic analysis and demographic information to find out where their locations can generate the most revenue.

Examples of business applications and questions that data mining answers:

Possible Question Application
How to better target product? Market segmentation
Which customers to invest in? Profitability
How to increase sales with loyalty programs and promotions? Online retail
How to minimize operational costs? Asset management
Which products are customers likely to buy together? Cross-selling

2. Data Mining Applications in Telecommunications

Data Mining Applications in Telecommunications - an infographic

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The telecommunications industry produces a countless amount of data each minute. Thereby, TELCO companies are an early adopter of data mining techniques and methods.

Data mining is taking care of many activities related to the management of telecommunication companies – marketing/customer profiling, reduced calling fees, fraud detection, reducing customer churn, network infrastructure management, and etc.

For example, call drops are one of the most critical problems for telecom operators when it comes to their network infrastructure management. With the help of data mining and its anomaly detection algorithms, network failures can be predicted and avoided.

In relation to marketing management, besides the general customer data that most businesses typically gather, TELCO companies also collect call detail records.

Thus, they can very accurately describe the calling behavior of each customer. This information is a great source for marketing and forecasting purposes.

Examples of applications and questions that data mining can answer in telecommunication management:

Possible Question Application
When a customer is likely to leave one TELCO company to go to another? Customer retention management
Which customized services to provide to increase customer loyalty? Sales and customer loyalty management
Who are the customers most likely to become the victims of cloning fraud? Fraud detection

3. Applications Of Data Mining In Banking Sector

Applications Of Data Mining In Banking Sector - an infographic

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Today banking systems gather a huge amount of transactional and historical data daily.

The collected data includes customer information, transaction details, credit card details, and etc.

By successfully performing big data analytics, a bank can understand its customers’ habits, identify the key channels of transactions (ATM credit/debit card payments. etc.), segment customers, detect and prevent frauds, assess risk, and analyze customer feedback.

For example, Barclays (a British multinational investment bank) utilizes real-time social media data analysis via its mobile banking application, “Pingit”, to gain immediate customer feedback.

As there is a huge amount of social data transferring when a customer uses its application, Barclays used the data mining services by a social media monitoring company to determine positive and negative feedback and then to create and offer new apps specifically based on this feedback.

Examples of applications and possible issues/questions that data mining is able to answer in the banking sector:

Possible Question Application
Whom customers can be offered short-term loans with high payout rates? Customer profiling
What patterns in credit transactions lead to credit fraud? Fraud detection
What are the characteristics of a high-risk borrower? Risk management
What is the profile of the customers who are using all types of services from your bank? Customer satisfaction management

4. Data Mining Applications in E-commerce

Data Mining Applications in E-commerce - an infographic

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People constantly buy and sell things via the internet. To keep the customers engaged with the website and to improve customer’s experience, businesses use data mining and machine learning algorithms.

For example, when you are searching for a product on the Amazon website, you can see several related recommendations.

These recommendations are generated through Machine Learning algorithms.

E-commerce and online retail companies store the data of every click customer makes, every purchase customer makes, every review a customer submits, etc.




E-commerce businesses use this data to understand better their customers, to ensure more positive consumer experiences, and thus to increase sales and opportunities.

Examples of applications and typical questions that data analytics can answer:

Possible Question Application
How your most valuable customers reach your business and e-commerce website? Customer lifetime value (LTV) management
Which product to recommend to particular customers? Recommendation management
Who are the customers most likely to switch to another eCommerce website? Customer retention management
Which products are most often bought with a particular product? Cross-selling

5. Data Mining for Financial Applications

Data Mining for Financial Applications - infographic

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Financial companies and financial departments of businesses take data mining and machine learning very seriously.

Reasons – data science help them to reduce operational costs, increase revenue, reinforce security, enhance user experiences, and forecast the financial markets.

There is a wide range of machine learning algorithms for classification problems that fit great with financial data.

Moreover, the big financial services companies have solid funds that they spend on state-of-the-art software related to data mining such as best BI reporting tools, competitive intelligence tools, and etc.

Insurance area is also a big part of the financial industry. If the insurance companies want to manage their risk successfully and keep their business profitable, they can’t afford to neglect data mining and machine learning.

For example, let’s think about clients database of an insurance company. If one of the company’s clients has a gap in insurance coverage, the data analytics system will automatically notify the company’s sales team.

This will give them the opportunity to bring additional value to their client.

Instead of blindly cold-calling, the salesperson will only call when seeing the client is missing something.

This will make the client feel the company’s care to their needs and also the insurer will get more sales.

Examples of applications and possible questions that data analytics can resolve in a financial company:

Possible Question Application
What will be the short-term changes in the financial market? Risk management
What is the ideal, fair price for a financial product (eg. stocks, bonds, insurance, etc)? Pricing
Which personalized insurance plans to recommend an insurance company to a particular user? Cross-selling and customer retention management
How to assess and control risk within existing consumer portfolios? Risk management
How to decrease fraud losses and increase cybersecurity? Fraud detection and security management

6. Data Mining Application in Medicine and Healthcare

Data Mining Application in Medicine and Healthcare - infographic

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Medicine and healthcare are one of the greatest examples of how data mining can revolutionize an entire industry.

Data science is moving the medical field to a whole new level, from analyzing medical records to drug findings and oncology disease examination.

Healthcare uses data science often for financial purposes such as reducing costs and customer acquisition.

Here is an example of specific data mining applications from IBM Watson – one of the largest data analytics software providers.

Watson for Oncology is a solution that assesses information from a patient’s medical record, evaluates medical evidence, and displays potential treatment options ranked by level of confidence, always providing supporting evidence. The oncologist can then apply their own expertise to identify the most appropriate treatment options.”

Examples of applications and common questions that data mining answers in medicine and healthcare:

Possible Question Application
How to increase the accuracy and efficiency of diagnostics with reading imaging data (such as x-rays, CT scans, etc.)? Diagnostics accuracy
How to increase hospital quality and patient safety? Hospital management
How to reduce the death rate of certain diseases and to predict medical outcomes? Disease forecasting
How patients with different genetic issues react to particular drugs and diseases? Genetics and genomics
How to reduce health care costs and at the same time to satisfy patients? Condition management in health insurance
How to bring drugs to customers quickly and effectively? Drug discovery

7. Data Mining Applications in Security (Information and Cybersecurity)

Data Mining Applications in Security - infographic

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It is not a surprise that as the complexity of IT information and networks has grown, the cybersecurity attacks have risen just as quickly.

And the estimated costs for cybercrime have reached billion dollars.

The attack surface is increased by mobile, cloud, and the other types of internet surfaces. On top of that, attackers know highly effective ways to make quick money using different tools and techniques.

All of these bring to organizations and businesses huge problems and challenges trying to maintain good information security levels. There are many ‘doors’ for an attacker to ‘break’ an enterprise network.

Using data analytics algorithms, companies can find the patterns in the lack of security, prevent cyber threats, detect attacks, and respond to them fast.




Data mining and analytics significantly reduce the time needed to catch and solve a problem, allowing cyber analysts to predict and avoid invasion.

Data analytics tools are used to identify cybersecurity threats such as compromised and weak devices, malware/ransomware attacks, and malicious insider programs.

Examples of applications and questions that data analytics answer in information and cybersecurity:

Possible Question Application
How to detect an unusual volume of network traffic from a network device? Fraud detection and network security management
Which databases have customer-related information and how vulnerable they are to attacks? Fraud detection and customer information security management
How to recognize and prevent malware attack? Fraud detection and network security management
How to identify anomalies and suspicious activities? Fraud detection
How to detect data exfiltration by attackers? Fraud detection

Conclusion

Reaching hundreds of areas, big data and analytics will revolutionize industries and our everyday life. The above list of data mining applications is an overview of those that are delivering high results today.

The fact is, the organizations and businesses that don’t use data mining advantages are going to be left behind soon or later.

Amazon keeps an eye on everything we’ve bought. Google knows everything we need to know. Facebook sees what we like.

These leaders play with an unimaginable amount of data in order to predict what could be the next valuable service for their users.

Their benefits are enormous: worldwide customer reach, brand recognition, high level of profitability, and etc.

What are your examples of data mining applications? Share your thoughts with us.

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