Is Data Mining Illegal?

There is a vast amount of data available in the information industry. However, this data is worthless until it is converted into useful information. Therefore, the raw data need to be analyzed, and the useful information extracted.

Data mining is defined as extracting information from massive datasets. In other words, we can say that data mining is a procedure for extracting knowledge from data.

In this article, we will talk more about data mining and find out is data mining illegal or not?

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What is data mining?

Data mining (deep data mining) is a process companies use to turn big raw data into useful information. The less popular term KDD (knowledge discovery in databases) is also used for this technology.

If the term Big Data refers to the whole array of collected data — both processed and not, then data mining is a process of deep immersion in this data to extract key knowledge.

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Using software to search for patterns in large data packages, businesses can build marketing strategies, manage credit risks, detect fraud, filter spam, or even identify user sentiment.

Data mining depends on efficient data collection, storage, and computer processing. Therefore, data mining is considered a separate discipline in data science.

The first data mining systems were designed to process supermarket sales data by several parameters, including their volume by region and product type.

Data mining models are used for several types of tasks:

Forecasting: sales estimation, prediction of server load or downtime.
Risk and probability: selecting suitable customers for targeted mailing, determining the balance point for risky scenarios, assigning probabilities for diagnoses or other results.
Recommendations: identifying products that will be sold together, creating recommendation messages.
Sequence search: analysis of customers’ choice during shopping, prediction of their behavior.
Grouping: dividing customers or events into clusters, analyzing and predicting the common features of these clusters.

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Data mining methodology

The methodology of data analysis can be divided into 5 main stages of work:

1. Search for data sources

The first thing to do is to find the data source in order to import it to the server for storage. It is this point that is of paramount importance. The data source must be reliable and ensure that all the results obtained are correct.

2. Choose the working environment

The second stage is searching and finding a cloud environment for work. The selected environment must meet several characteristics: power — for fast processing of the volume of data, and availability — for fast data connection.

3. Data segmentation and categorization

The third stage is the ordering and structuring of all the received data. Depending on the size of the data, you may need to work with it in parts rather than as a whole.

4. Data mining

The fourth stage is the process of intellectual analysis and information extraction. Again, you can use specialized software for this step or work independently using a compatible programming language such as R, Python, or SQL.

Data mining uses mathematical models to find and extract baseline information for raw data. Although you should not confuse this with data analysis, which uses data and analytical data, often derived from data mining, to build models and forecasts.

5. Sum up the results

The final stage is summing up and counting the results. It is important to visualize the data by converting them into graphs or tables at this stage. Although visualized results are not very useful for future data analysis and analysis, they make understanding and sharing your results easier.

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Where is data mining used?

Data mining is mainly used by industries serving consumers, including retail, finance, and marketing. However, both companies and government agencies can use it to assess a region’s development potential.

Trading

Data mining allows retail chains to analyze shopping baskets to improve advertising, create stocks of goods in warehouses, plan how to put them on display, open new stores, and identify the needs of different categories of customers.

Banks

Data mining allows credit organizations to detect credit card fraud by analyzing such transactions and to offer various types of services to different groups of customers.

Insurance

Insurance companies analyze large amounts of data to identify risks, reduce their losses on obligations, and offer relevant services to customers.

Production

Data analysis allows enterprises to coordinate supply plans with demand forecasts, detect production problems at early stages, and successfully invest in the brand. In addition, manufacturers can predict the depreciation of production assets and plan maintenance and repairs so as not to stop the production line.

An example of the application of data mining in the industry is the prediction of product quality depending on the technological process parameters.

Sociology

Sentiment analysis based on social media data allows you to understand how a certain group of people relates to a particular topic.

Medicine

Data mining systems are also used for making medical diagnoses. They are built based on rules describing combinations of symptoms of various diseases. The rules help to choose the means of treatment.

Is data mining illegal?

Oddly enough, data collection and analysis are legal. However, concerns arise when dealing with the results of such fees.

All the data received must be publicly available, for example, weather data, or obtained by mutual consent. Thus, all participants in surveys or online meetings should be aware of the further fate of their information.

Companies and institutions that do not have permission to use the data may violate privacy laws, both locally and offshore, depending on the data source.

In addition, most countries generally restrict or prohibit the use of data mining to discriminate against individuals based on age, gender, gender, race, or religion.

The future of data mining

The market for data mining systems is growing. This is facilitated by the activities of large corporations: SAS, IBM, Microsoft, Oracle, and others. As a result, by 2027, the volume of the global advanced analytics market will grow by 23.1% and reach $56.2 billion.

Recent trends in data mining include the development of analysis methods with elements of virtual and augmented reality, their integration with database systems, the extraction of biological data for innovations in medicine, web mining (data analysis on the internet), real-time data analysis, as well as measures to protect privacy in data mining. In addition, industry leaders believe that data mining will be used in intelligent applications that will be embedded in corporate data warehouses in the future.

The main problem of detecting patterns in data is the time it takes to sort through information arrays. Unfortunately, known methods either artificially limit such a search or build entire decision trees that reduce the efficiency of the search. Solving this problem remains the main goal of data mining product developers.