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Introduction to Data Mining
We can simply define data mining as a process that involves searching, collecting, filtering, and analyzing data. It is important to understand that this is not the standard or accepted definition. But the above definition caters to the whole process.
A large amount of data can be retrieved from various websites and databases. It can be retrieved in form of data relationships, correlations, and patterns. With the advent of computers, the internet, and large databases, it is possible to collect large amounts of data. The data collected can be analyzed steadily and help identify relationships and find solutions to existing problems.
Governments, private companies, large organizations, and all businesses are after a large volume of data collection for the purposes of business and research development. The data collected can be stored for future use. Storage of information is quite important whenever it is required. It is important to note that finding and searching for information from websites, databases, and other internet sources takes quite some time.
Why do we need Data Mining?
Have a look at the below-mentioned points which explain why data mining is required.
- Data mining is the procedure of capturing large sets of data in order to identify the insights and visions of that data. Nowadays, the demand of data industry is rapidly growing which has also increased the demands for data analysts and data scientists;
- With this technique, we analyze the data and then convert that data into meaningful information. This helps the business to take accurate and better decisions in an organization;
- Data mining helps to develop smart market decision, run accurate campaigns, make predictions, and more;
- With the help of Data mining, we can analyze customer behaviors and their insights. This leads to great success and data-driven business.
Data mining and its process
Data mining is an interactive process. Take a look at the following steps.
1 – Requirement gathering
Data mining projects start with requirement gathering and understanding. Data mining analysts or users define the requirement scope with the vendor business perspective. Once the scope is defined, we move to the next phase.
2 – Data exploration
In this step, Data mining experts gather, evaluate, and explore the requirement or project. Experts understand the problems, challenges, and convert them to metadata. In this step, data mining statistics are used to identify and convert data patterns.
3- Data preparations
Data mining experts convert the data into meaningful information for the modeling step. They use the ETL process – extract, transform, and load. They are also responsible for creating new data attributes. Here various tools are used to present data in a structural format without changing the meaning of data sets.
Data experts put their best tools in place for this step as this plays a vital role in the complete processing of data. All modeling methods are applied to filter the data in an appropriate manner. Modeling and evaluation are correlated steps and are followed at the same time to check the parameters. Once the final modeling is done the final outcome is quality proven.
This is the filtering process after successful modeling. If the outcome is not satisfactory, then it is transferred to the model again. Upon final outcome, the requirement is checked again with the vendor so no point is missed. Data mining experts judge the complete result at the end.
This is the final stage of the complete process. Experts present the data to vendors in the form of spreadsheets or graphs.
Have a look at the below diagram for CRISP DM- Cross Industry-standard process for Data mining.
Data mining services can be used for the following functions
- Research and surveys. Data mining can be used for product research, surveys, market research, and analysis. Information can be gathered that is quite useful in driving new marketing campaigns and promotions.
- Information collection. Through the web scraping process, it is possible to collect information regarding investors, investments, and funds by scraping through related websites and databases.
- Customer opinions. Customer views and suggestions play an important role in the way a company operates. The information can be readily be found on forums, blogs, and other resources where customers freely provide their views.
- Data scanning. Data collected and stored will not be important unless scanned. Scanning is important to identify the patterns and similarities contained in data entries.
- Extraction of information. This is the processing of identifying useful patterns in data that can be used in the decision-making process. This is so because decision making must be based on sound information and facts.
- Pre-processing of data. Usually, the data collected is stored in the data warehouse. This data needs to be pre-processed. By pre-processing, we mean some data that may be deemed unimportant may be removed manually by data mining experts.
- Web data. Web data usually poses many challenges in mining. This is so because of its nature. For instance, web data can be deemed as dynamic, meaning it keeps changing from time to time. Therefore, it means the process of data mining should be repeated at regular intervals.
- Competitor analysis. There is a need to understand how your competitors are fairing on in the business market. You need to know both their weaknesses and strengths. Their methods of marketing and distribution can be mined. How they reduce their overall costs is also quite important.
- Online research. The internet is highly regarded for the vast amounts of information it contains. It is clear that it is the largest source of information. It is possible to gather a lot of information regarding different companies, customers, and business clients. It is possible to detect fraud through online means.
- News. Nowadays, with almost all major newspapers and news sources posting their news online, it is possible to gather information regarding trends and other critical areas. In this way, it is possible to be in a better position of competing in the market.
- Updating data. This is quite important. Data collected will be useless unless it is updated. This is to ensure that the information is relevant so as to make decisions from it.
After the application of the data mining process, it is possible to extract information that has been filtered through the processes of filtering and refining. Usually, the process of data mining is majorly divided into three sections; pre-processing of data, mining data, and then validation of the data. Generally, this process involves the conversion of data into valid information.
ADVANTAGES OF DATA MINING
Check out the below-mentioned Data mining benefits
- With the help of Data mining, marketing companies build data models and predictions based on historical data. They run campaigns, marketing strategies, and pivots when necessary. This leads to success and rapid growth.
- The retail industry is also on the same page with marketing companies. With Data mining, they believe in predictive based models for their goods and services. Retail stores can have better production and customer insights. Discounts and redemption codes are based on historical data.
- Data mining helps banks with financial benefits and updates. They build a model based on customer data and then check out the loan process which is truly based on data mining.
- Manufacturers obtain benefits from Data mining in engineering data and detecting faulty devices and products. This helps them to cut off the defective items from the list and then they can occupy the best services and products in place.
- It helps government bodies to analyze the financial data and transactions to model them to useful information.
- Data mining organizations can improve planning and decision makings.
- New revenue streams are generated with the help of Data mining which results in organization growth.
- Data mining not only helps in predictions but also helps in the development of new services and products.
- Customers see better insights with the organization that grows its customer lists and interactions.
- Once the competitive advantages are made, the cost is also reduced with the help of data mining.
There are many more benefits of Data mining and its useful features. When data mining combines with Analytics and Big data, it is completely changed into a new trend which is the demand of the data-driven market.
It is important to note that it takes time to get valid information from data. Therefore, if you are after making your business grow rapidly, there is a need to make accurate and quick decisions that can take advantage of grabbing the available opportunities in a timely manner.
Data mining is a rapidly growing industry in this technology-oriented world. Everyone nowadays requires their data to be used in an appropriate manner and with the right approach in order to obtain useful and accurate information.
Loginworks Softwares is one of the best data mining outsourcing organizations that employ highly qualified and experienced staff in the market research industry.