Data mining is the process of extracting valuable information from large data sets. It is a relatively new field due to computer hardware and software advances. Data mining can be used to find hidden patterns and relationships in data that can be used to make better business decisions.

“The world is one big data problem”

-Andrew McAfee

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in various ways, such as database marketing, credit risk management, fraud detection, spam Email filtering, or even to discern the sentiment or opinion of users (Twin, 2022) [1].

Data Mining

The data mining process is a series of steps to extract useful information from large data sets. Data mining can be used to find trends and patterns in data, which can then be used to make predictions about future data. The process of data mining can be divided into four main steps:

1. Data Gathering

The relevant data is identified and gathered. The data can be located in multiple places, such as warehouses, lakes, and structured and unstructured data. Whatever the data source may be, it is usually placed in a data lake for the remaining steps.

2. Data Preparation

This step is also called data scrubbing. This process starts with data exploration, profiling, and pre-processing. It then goes through the procedure of cleaning. Cleaning usually deals with correcting minor errors, accounting for outliers, and checking for reasonableness. The end goal of data preparation is to make all the data consistent so that no errors will be raised during the actual mining.

3. Data Mining

This is where the data scientist chooses the appropriate technique for mining. Now that our large data set has been cleaned, it is ready for processing. This step searches for relationships, trends, associations, and sequential patterns. Algorithms are used to search for relevant data. These algorithms are usually trained in sample data beforehand to sniff out the required information.

4. Data Analysis

This step comes after mining and involves analysis of the extracted data. Analytical models are prepared, and data is interpreted so that the business can make decisions and allocate company resources. The final result is shared with the executives with the help of data visualization and storytelling so that the company may finally execute its new strategies per the information provided by the data.

Its core elements include machine learning, statistical analysis, and data management tasks to prepare data for analysis. The use of machine learning algorithms and artificial intelligence (AI) tools have automated more of the process and made it easier to mine massive data sets, such as customer databases, transaction records, and log files from web servers, mobile apps, and sensors (Stedman, 2021) [2].

“The goal is to turn data into information

And information into insight”

-Carly Fiorina

Data mining is not an easy thing to do. Dealing with such large data sets and trying to make sense of the data requires a lot of training and complex mathematics. Algorithms play a huge part in this process. Here are a few of the techniques and algorithms that are commonly used in data mining:

• Associate Rule Mining is a series of ‘If-Then’ statements that identify relationships between data elements. ‘Support’ (how frequently related elements appear in the data set) and ‘Confidence’ (number of times an ‘If-Then’ statement is accurate) are the pillars of this technique.
• Classification is where the data elements are grouped into multiple clusters based on a shared characteristic.
• Regression finds relationships in data sets by calculating predicted data values based on a set of variables.
• Sequence & Path Analysis identifies patterns in which a particular set of events/values lead to a certain outcome.
• Neural Networks are a set of algorithms working in tandem to simulate the activity of a real human brain. These networks work just like our neurons, remembering and identifying complex patterns. This is a highly advanced application of machine learning.

Benefits & Examples

There are many ways that businesses can use data mining to enhance their strategies. One of the greatest benefits of data mining is that it can provide insights into customer behavior, allowing businesses to understand their target market better and develop marketing strategies that are more likely to resonate with consumers.

By understanding who their customers are and what they want, businesses can develop more targeted marketing campaigns and tailor their products and services to meet customer needs better.

Additionally, data mining can identify trends and patterns in sales data, which can help businesses make informed decisions about pricing, product development, and inventory management.

Data mining can also help businesses to detect and prevent fraud by providing visibility into areas where fraudulent activity is most likely to occur. Data mining can be a powerful tool for corporations seeking a competitive edge.

Then we have cost benefits for industries, such as increased efficiency because production can be streamlined with data mining and analysis, and inefficiencies can be eliminated. Faulty or failing products can be easily identified and removed before costing the company.

“There were 5 Exabytes of information created between the dawn of civilization

Through 2003, but that much information is now created every 2 days”

                                                                                                -Eric Schmidt

Taking a deep dive into the information they give you during their time on your website. Data mining can allow you to offer products and services to customers before they even know they want them. The best part is that your returns with data mining increase over time – the more you know about your customers, the easier it is to provide them with exactly the kind of service they want (Stringfellow, 2016) [3].

There are many ways in which data mining can be used to produce outstanding results. Here are a few live examples:

• Music

Due to growing streaming sites and the impact of covid-19, the live music industry has declined significantly. The recent emergence of a startup named Hearby brought some life back into the local music industry. Using AI and machine learning, Hearby scans datasets and maps live acts in real-time across America and Europe.

• Education

The sudden emergence of Covid-19 has left many students and parents with a very difficult educational situation. Many households were not equipped to deal with the increased tech demand required for the new schooling structure. This is where Zoom, Instructure, and other similar companies stepped in. The public data of Instructure’s State of Student Success Survey has pointed out that the digital divide must be bridged for equitable education.

• Finance

Data mining has played a huge part in the modernization of financial services. Nearly all banks, credit card companies, hedge funds, investment banks, etc., are using data mining to build financial models, track fraudulent transactions and vet all loans and credit applications.

• Automation

Another example of Data Mining and Business Intelligence comes from the retail sector. Retailers segment customers into ‘Recency, Frequency, Monetary (RFM) groups, and target marketing and promotions to those different groups. A customer who spends little but often and last did so recently will be handled differently to a customer who spent big but only once, and also some time ago. The former may receive loyalty, upsell and cross-sell offers, whereas the latter may be offered a win-back deal.

• Grocery Stores

Supermarkets are another amazing example of Data Mining and Business Intelligence in action. Famously, supermarket loyalty card programs are usually driven mostly, if not solely, by the desire to gather comprehensive customer data for use in data mining. One notable recent example of this was with the US retailer Target. As part of its Data Mining program, the company developed rules to predict if their shoppers were likely pregnant. By looking at the contents of their customers’ shopping baskets, they could spot customers they thought were likely to be expecting and begin targeting promotions for diapers, cotton wool, and so on. The prediction was so accurate that Target made the news by sending promotional coupons to families who had not even announced they were pregnant.

• Crime

The safety of our families and societies takes precedence over all other things, which is why it is a relief that data mining can help in stopping crime. Multiple agencies have used data mining to spot trends in crime statistics which has helped them to deploy their resources better. Some good example of this work is how police have been given a better deployment plan for their limited manpower. How border control is given instructions on which cars may be potential threats/lawbreakers by taking note of their age, car, and travel history. Intelligence agencies use data mining to identify which threats are credible and which to ignore.

Despite all the help that data mining can provide corporations, the results can only be as beneficial as the one analyzing the data. The process wastes resources if the person responsible for driving action cannot understand how best to utilize the information provided. Even with all the help, it ultimately comes down to the human element.

Organizations that persist in taking a hard pass on data mining are setting themselves up for problems. At best, they will offer competitors clear sailing in the passing lane. Given the richness of public data coupled with increased ease of access, there’s no longer any need to check our gut when making decisions that affect jobs, education, or anything else (Gibbons, 2021) [4].

Conclusion

Data mining can help corporations to develop and enhance their strategies in several ways. Modern businesses need to gather information on their products, marketing, employees, and customers, all of which is data, which is why data mining needs to become commonplace in all industries.

“Learning from data is virtually universally useful.

Master it and you will be welcomed anywhere”

                                                                                                -John Elder

 The opportunities and insights provided by data mining are extremely valuable resources. The future of data mining is limited only by imagination. Organizations that refuse to accept this technological change will soon find themselves out of business as their competitors leave them in the dust.

Data mining is important for the future of all industries because it offers a way to make sense of large data sets and extract valuable information from them. With the ever-increasing amount of data being generated, data mining will only become more important in the years to come.

More products are becoming digital, with more payment transactions and customer interactions. As this happens, more companies are finding that their data, often already stored in a data warehouse waiting to be analyzed, is just as valuable as their products and services. In this context, data mining gives companies a competitive edge by helping to rapidly find business insights hidden in all the data from all those digital business transactions. The benefits are almost endless. Understanding customer behaviors can lead to new products, services, or marketing ideas. Detecting intrusions can prevent the devastating theft of customer data (Morris, 2021) [5].

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References

[1]: Alexandra Twin, 2^nd August 2022, What Is Data Mining? How It Works, Benefits, Techniques, And Examples

[2]: Craig Stedman, September 2021, Data Mining

[3]: Angela Stringfellow, 14^th April 2016, 30 Experts Reveal the Biggest Ways that Data Mining Improves Customer Experience

[4]: Serenity Gibbons, 25^th February 2021, How Businesses Are Using Data To Make A Real Impact

[5]: Andy Morris, 9^th July 2021, What Is Data Mining? How It Works, Techniques & Examples