Data Mining

Data mining is a process that involves spotting anomalies and identifying patterns and correlations in large data sets to estimate future trends. Companies use this procedure to convert raw data into useful information that can help them increase earnings, minimize risks, and improve customer relationships.

This process depends on computer processing, warehousing, and effective data accumulation. It combines the techniques used in statistics and computer science, helping businesses run accurate campaigns, develop smart marketing decisions, analyze consumer behavior, etc. Resultantly, organizations can achieve their goals more efficiently. As a result, data mining is popular in multiple business areas like product development, sales, marketing, etc.

• The data mining definition refers to a process that involves sorting through large data batches to spot relationships and patterns that can solve business issues, reduce risks, and seize new opportunities. This process involves six steps — business understanding, data understanding, preparation, modeling, evaluation, and deployment.
• There are various benefits of data mining. For example, it helps organizations detect fraud and make operational adjustments.
• Some popular data mining techniques are regression analysis, clustering, classification, anomaly/outlier detection, and association rule learning.
• Businesses can carry out this process to run successful marketing campaigns and analyze their customers' purchase behavior.

The data mining definition refers to a process that involves using computers and automation to turn raw data into useful information. For example, organizations can get more details regarding customers by utilizing software to spot patterns within large data batches. This can help businesses to formulate more effective marketing strategies, decrease expenses, and increase revenue.

All organizations should follow a repeatable and structured approach to ensure that the procedure delivers reliable results every time. In that regard, the process of data mining must involve the following six steps:

#1 - Business Understanding

The first step involves identifying the business objectives and understanding the current business situation, criteria for success, and project parameters. If an organization does not know what data to mine, the project can produce errors, or the results may not answer the right questions.

#2 - Data Understanding

Once an organization determines its main goal, it must accumulate proper data to help fulfill the objective. Typically, the data comes from a wide range of sources, for example, geolocation data, sales records, customer surveys, etc. This step's objective is to ensure the data properly encompasses all the required data batches to address the goal.

#3 - Data Preparation

This stage comprises three phases — extraction, transformation, and loading. Over these three phases, the cleaning and organization of the data take place for further modeling processes.  

#4 - Modeling

The modeling step involves using mathematical models to identify patterns in the data. Multiple data modeling techniques are available for businesses, for example, regression analysis, clustering, classification, etc.

One must note that modeling involves a lot of trial and error. Moreover, one can use multiple models on the same data set to address certain objectives.

#5 - Evaluation

Once the model is complete, businesses must carefully assess it to ensure it meets their objectives. This step is human-driven as the person running the project evaluates the model. At the end of this step, one must decide whether to go ahead with the mining results.

#6 - Deployment

Depending on the output of this process, this step can be straightforward or complex. Deployment can be in the form of a report sharing insights. Alternatively, it can be a visual representation. This step can lead to the implementation of risk-minimizing measures or the generation of a new sales strategy.

Once the data mining process is complete, businesses can make decisions and implement changes based on their findings.

The following are some common techniques of data mining:

#1 - Classification Analysis

This technique involves assigning data points to classes or groups based on a particular problem or question to address, thus helping one to conclude.

#2 - Association Rule Learning

The association rule learning method requires one to track patterns, particularly based on linked variables. It aims to reveal relationships between data points.

#3 - Anomaly/Outlier Detection

Besides tracking patterns, data mining involves uncovering unusual data in a set. In the case of this technique, one seeks data that does not conform to the pattern. One can use it to detect fraud. Moreover, this method can help retailers know about the increase or decrease in specific products' sales.

#4 - Regression Analysis

One uses this technique to estimate a range of numeric values, for example, stock prices and sales, based on a specific data set. As a result, it can reduce out-of-stock instances and increase sales. Moreover, it can help avoid overstocking.

#5 - Clustering

Clustering is similar to classification. It involves filtering out data points sharing common characteristics into subsets. In this case, individuals must note that one does not assign data to any previously defined group. This technique can be useful when defining traits within a data batch. For instance, the segmentation of customers based on their life stage, purchase behavior, etc.

This concept is common in healthcare, retail, and banking businesses. So let us look into the details.

1. Healthcare - It makes the diagnostic process much more accurate by bringing together all patients' physical examination results, medical history, treatment patterns, and medications. Moreover, it helps reduce waste and fraud.
2. Retail - The marketing and retail worlds have a close association. That said, the latter warrants a different listing. Retail stores can utilize the identified patterns to define product associations and determine which items to stock. The concept also helps determine which marketing got the best response.
3. Bank - Banks can use this concept to work with anti-fraud mechanisms and credit ratings to analyze purchasing transactions, customer financial data, and card transactions. The process also enables banks better understand their clients' online preferences and habits. This, in turn, helps them design a new marketing campaign.

Let us look at this data mining example to understand the concept better.

Example #1

With supply chains becoming increasingly complicated, organizations find it challenging to get complete visibility of their trading partners' sustainability risks. To combat this issue, EcoVadis has added data mining and artificial intelligence (AI) enhancements to its predictive intelligence solution, IQ Plus.

Organizations need additional due diligence capabilities and intelligence to act efficiently in line with the evolving risk and regulatory landscape. IQ Plus addresses this requirement by offering organizations instant broad visibility across the supply base.

EcoVadis's chief product officer, Madhur Aggarwal, believes that the enhancements will enable their clients to accelerate their journey and play a crucial role in the sustainability transition. Moreover, the organization's AI and data mining-powered innovation can help clients stay ahead of the industry.

Example #2

Suppose a retailer, XYZ, wants to improve customer relationships using data mining. It can chunk together or cluster customers according to their shopping frequency, basket totals, and estimated grocery spend per week. Then, based on the data, it can offer discounts to increase customer spending. Besides providing consumers an incentive to buy more products, it helps the retailer retain the dollars targeted by its peers.

Let us look at the benefits and limitations of this process.

• This process helps businesses detect fraud and credit risks.
• Businesses can use the techniques of data mining to analyze substantial data quickly.
• Organizations can make operational adjustments and profitable production with the help of this concept.
• Data mining allows scientists to quickly initiate automated estimations of trends and behaviors, discovering hidden patterns.

Another noteworthy benefit of data mining is that it is more cost-effective than other data applications.

The disadvantages of the process are as follows:

• It requires large databases. Hence, managing this process is difficult.
• Organizations can potentially sell customer data extracted from various sources to other businesses, thus raising privacy concerns.
• Using various data mining tools can be challenging owing to their complexity. Hence, one needs proper training to utilize the tools effectively.
• All techniques of data mining are not infallible. One must remember that there's always a chance that the information is not completely accurate. The chance of inaccuracy is high when a data set lacks diversity.

Data mining, data warehousing, and data profiling can be confusing for individuals who are unfamiliar with these terms. For them, it is vital to understand their distinct characteristics to eliminate such confusion. So, the table below highlights their critical differences. Let us look at them.

This has been a guide to what is Data Mining. We explain its techniques, applications, examples, advantages, and comparison with data profiling. You can learn more about it from the following articles –

• Data Analytics
• Factor Analysis
• Predictive Modeling