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In recent years, data mining has become an important process for companies and SMEs. Data mining helps to transform the received data into a structure that can be used later. But what is data extraction and data storage?
In this blog, we take a closer look at data mining and its various benefits in the business world. Additionally, we will discuss the difference between data warehousing and data mining and how data warehousing can benefit business.
What is data mining?
Data mining is not just a buzzword in business, but has common applications in any type of large-scale data or information processing and any computer decision support system. Simply put, it involves the process of extracting and discovering patterns in large data sets using methods at the intersection of machine learning, statistics, and database systems. This is actually an interdisciplinary field of computing and statistics, the overall goal of which is to extract information from a data set and transform the data into an understandable structure for later use.
Data extraction is an integral part of data analysis. It is one of the fundamental disciplines in data science that uses advanced analytical techniques to find valuable information in databases. Ironically, data mining is a misnomer. Because the goal is to extract patterns and knowledge from large amounts of data, not the data itself.
In simple terms, data mining is the process of sorting through large data sets to find patterns and relationships that help solve business problems through data mining . The tools and methods used allow companies to predict future trends and make more informed business decisions.
Is data mining necessary?
At a more detailed level, data mining is an important part of any organization's analytics strategy. The generated data can be used by business intelligence and advanced analytics programs to further analyze historical data. Additionally, it can also be used for real-time analytics applications that analyze streaming data at the same time as it is generated and collected.
However, data mining can help in many areas in developing and managing business strategy. For example, marketing, advertising, sales, customer service, supply chain management, finance and more. In addition, data mining supports other security-focused aspects of the organization, such as fraud detection, risk management, cybersecurity planning, and more. It is also important in health care, government, scientific research, sports, mathematics and many other fields.
How does data mining work?
Typically, data scientists are responsible for data mining. However, qualified business intelligence and analytics professionals can take on this process, including business analysts, managers, and employees who work as civilian data scientists within an organization.
The core components of data mining include machine learning, statistical analysis, and data management operations performed to analyze data. In general, the process of integration of machine learning algorithms and AI tools and the processing and extraction of huge data sets such as customer databases, transaction logs, web server logs, etc. etc. has been further developed.
The data extraction process can be divided into 4 main stages: data collection; Data preparation; data extraction; Data analysis and interpretation .
1. Data collection . This process involves identifying and gathering data related to the application of analytics. The data can be stored in various data sources such as a data warehouse or data lake, but external data sources can also be used. However, regardless of the primary data source, the data scientist can turn to the data lake for the rest of the process.
2. Data preparation . The following process involves performing several steps before extracting data. Similarly, the first step is to explore, uncover and pre-process the data. Finally, the process is followed by data cleaning to correct errors and other data quality issues.
3. Data extraction . After the data is prepared, the data scientist selects an appropriate data mining method and applies one or more algorithms to begin the data mining process. For machine learning applications, algorithms must (typically) be trained on sampled datasets. This is done to get the information you need before running on the database.
4. Data analysis and interpretation . Once created, the results of data mining are used to build analytical models for decision making, including other business activities. The data scientist or another member of the data scientist team should communicate the results to business managers and users. However, this is often done using data visualization and data mining methods.
What are the different methods of data extraction?
Basically, different methods are used to extract data for different data science applications. However, a common use of data mining is pattern recognition, which is provided by various methods. Another common application is anomaly detection, which aims to identify outliers in data sets. However, the most popular data extraction methods are the following types.
1. The association organizes mining . First, association rules are "if-then" statements that describe relationships between data elements in data mining. To find relationships, support and confidence criteria are used to evaluate relationships: support measures measure how often related items occur in the database, while confidence reflects how often the statement is true, and therefore valid.
2. Classification . Secondly, this approach assigns different categories to the elements of the data set as part of the data mining process. Examples of classification methods include decision trees, Naive Bayes classifiers, k-neighbors, and logistic regression.
3. Grouping . Thirdly, this process involves grouping data elements with certain characteristics into groups in data mining applications. Examples are k-means clustering, hierarchical clustering, and mixed Gaussian models.
4. Return . Next, regression is another way to find relationships in data sets by calculating predicted data values based on sets of variables. In fact, decision trees and some other classification methods can be used to perform iterations.
5. Sequence and path analysis . In some cases, data mining can also be performed to find patterns in certain sets of events or values that lead to a sequence.
6. Neural networks . A neural network is a set of algorithms that mimic the activity of the human brain. Neural networks are particularly useful in complex pattern recognition applications involving deep learning, a more advanced branch of machine learning.
What are the characteristics of data mining?
Data mining analysis is definitely done using analytical focus functions. However, these properties may be unique properties of the activation class. Sometimes these can be properties at a higher level than the focus element level.
However, features with different complexity profiles can be used to capture the analytic features you want to include in your data mining analysis. Mainly, each characteristic is a column in the output table, with different types of characteristics related to different ways of changing the input model in a way to calculate the important properties of the analysis focus.
1. Focus task : For example, tasks that are limited to one area of focus. Storage or date are the simplest because their values are expressions of values already included in the main database tables.
2. Total. Typically, multiple properties are the result of aggregation. Because the level of individual processes is difficult to predict, their behavior should be taken into account. However, under normal conditions, the mixing process is carried out at all concentration levels.
3. Collection department : When analyzing stores (especially sales results), it is common to include partial sales of large departments in the analysis. But it can be easily done from the assembled part. However, the daily sales volume in the store is divided by the sales volume of individual departments.
4. Intelligence. Some data mining algorithms require categorical input rather than numerical input. In these cases, the data must be preprocessed so that values within certain numeric ranges are mapped to different values.
5. Display prices. In particular, displaying values is similar to defining numeric attributes in that users can assign new values to individual attribute values.
6. Calculation. Any SQL expression can be evaluated to evaluate attributes derived from other attributes. The calculation process is simple and involves adding or dividing two properties, or it can be more complex depending on the task.
What are the benefits of data mining?
Here are some benefits of data mining
- Marketing and/or retail
Interestingly, data mining helps direct marketers by providing valuable and accurate trends in their consumers' purchasing behavior. Marketers can target customers based on these trends. Data mining helps marketers predict customers' favorite products. This further helps in creating an interactive and engaging shopping experience for customers. In addition to marketing departments, retail stores also benefit from data extraction using similar methods.
- Bank and/or credit
Data extraction is useful for financial institutions, especially in loan documents and credit records. This process helps credit card issuers identify potentially fraudulent credit card transactions. While this method is not completely accurate in predicting fraudulent charges, data mining can certainly help credit card issuers reduce their losses.
- Police
The data mining process helps law enforcement identify and apprehend criminal suspects by location, type of crime, habits and other characteristics.
- Researchers
In addition, the process of data extraction helps researchers. In addition, it allows to accelerate the level of data analysis; This will give them more time to work on different projects.
People often confuse data storage and data retrieval as the same processes. Although both are data management and maintenance processes, there is a significant difference between them. Finally, let's take a quick look at how data warehousing differs from data mining.
What is data logging?
A data warehouse is a method of collecting and managing data from various sources to provide meaningful business information. It's a combination of technology and components that enable you to use data strategically. In other words, a data warehouse is a large-scale electronic repository for querying and analysis by an enterprise rather than for transaction processing. It is essentially the process of converting data into information and presenting it to users for analysis .
In the year In 1990, Bill Inmon used the term "data warehouse" for the first time. According to him, a data warehouse is a domain-specific, integrated, time-varying, non-volatile collection of data that helps analysts make informed decisions within the organization. In addition, the database provides comprehensive and consolidated information in a multidimensional view. It also provides interactive analytical processing (OLAP) tools that aid in interactive and efficient data analysis in a multidimensional space. This analysis also leads to generalization and data extraction.
What are the characteristics of data storage?
The main features of the database are listed below:
- Subject- oriented : Initially, the data warehouse is subject-oriented and provides information on a single topic rather than on the ongoing process of the organization. Therefore, these entities can be products, customers, suppliers and many others. Instead of focusing on ongoing operations, data warehouses focus on capturing and analyzing data for decision making.
- Integration . Secondly, the data warehouse is created by combining data from different sources, which increases the efficiency of data analysis.
- Time interval: Thirdly, the data collected in the data warehouse is identified with a specific period and provides information from a historical perspective.
- Non-volatile: So non-volatile means that when new data is added, the old data is not deleted. The data store is kept separate from the production database and the data store does not reflect frequent changes.
What are the benefits of archiving data?
The advantages of data storage are as follows.
111 1 . It provides rich historical data and adds context by outlining key performance trends important for retrospective study.
2018-05-13 121 2 . The data warehouse not only transforms data in various forms into the desired analytical platforms, but also ensures consistency. Ensures that data produced by different departments of the company are of the same quality and standard. As a result, this allows for a more effective analysis resource to be used.
3. Above all, the data warehouse improves efficiency by collecting information in one place and making it easily accessible in the appropriate format.
4. The data warehouse provides not only power and speed, but also competitiveness in key business areas from CRM to HR, sales success and quarterly reporting.
5. Essentially, it helps you improve your BI, which leads to better decisions and a greater return on investment in any business sector. As a result, increase revenue by making better decisions that strengthen your business.
6. Data records ensure the efficiency of data flow, help business to grow. Especially since this business development is an integral part of business expansion.
7. Advances in data storage today have improved corporate security by further enhancing the overall security of corporate information.
8. The data warehouse is designed to handle large amounts of data and complex queries. As such, it is a highly functional core component of any company's data analytics practice.
9. In addition, data warehouses allow companies to work strategically and effectively with other suppliers in their industry.
10. Finally, data storage improves the process of making business decisions, which, in turn, provides a significant competitive advantage for any business.
Are intellectual analysis of data and storage of data different?
The key difference between data storage and intellectual data analysis is that intellectual data analysis is data analysis, and data storage is the process of compiling information or data into a database used for data storage. .
Intellectual analysis of data against data storage:
| Data processing | Data storage |
| This is the process of analyzing data models. | This database system is intended for analytical analysis, not for transactional work. |
| In intellectual analysis of data, you can identify patterns using the logic of pattern recognition. | Data storage includes the process of extracting and archiving data to facilitate the compilation of reports. |
| Here the data is regularly analyzed. | This implies periodic saving of data. |
| The process of intellectual analysis of data is mainly performed by business users with the help of engineers. | Only engineers are involved in archiving data. |
| Basically, this is the process of extracting data from large sets of data. | However, this includes combining everything that matters. |
| The main attention in data storage is given to artificial intelligence, statistics, databases and machine learning systems. | Хранилище данных — this is, first of all, the thematic, integrated, changing in time and energy-independent data storage. |
| Understanding the logical process of recognizing images | While the process includes the extraction and storage of data to make the reporting process more effective. |
| The procedure uses image recognition tools to help identify access models. | Data are extracted and saved in a structured format, which simplifies and accelerates the creation of reports. |
| For example: since intellectual analysis of data helps to create striking models of key indicators, it helps companies to make necessary changes in their activities and production. Интелектуальный анализ данных обеспечать в область копупательского беходить клиенты, товаров, продаж и много друго. | Primer. Data storage increases value when connected to operational business systems, such as customer relationship management systems (CRM). |
Сообщение What is intellectual data analysis and data storage? It first appeared on Tech Research Online.