Introduction to Data Mining

We can simply define data mining as a process that involves searching, collecting, filtering and analyzing the 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, co-relations, and patterns. With the advent of computers, internet, and large databases it is possible to collect large amounts of data. The data collected may be analyzed steadily and help identify relationships and find solutions to the 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 it may take a long time for finding and searching for information from websites, databases and other internet sources.

Why do we need Data Mining?

Have a look at the below-mentioned points which explain why data mining is required.

  1. 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.
  2. Since 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.
  3. Data mining helps to develop smart market decision, run accurate campaigns, predictions are taken and many more.
  4. 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 project starts with the 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

Here, 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 the data patterns.

3- Data preparations

Data mining experts convert the data into meaningful information for the modelling step. They use 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.

4- Modelling

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. Modelling and evaluation are correlated steps and are followed same time to check the parameters. Once the final modeling is done the final outcome is quality proven.

5- Evaluation

This is the filtering process after the successful modelling. If the outcome is not satisfied 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.

6- Deployment

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 patterns and similarities contained in the data.
  • Extraction of information. This is the processing of identifying the useful patterns in data that can be used in 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 it means some data that may be deemed unimportant may therefore removed manually be 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 its huge information. It is evident that it is the largest source of information. It is possible to gather a lot of information regarding different companies, customers, and your business clients. It is possible to detect frauds 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 the 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 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

  1. With the help of Data mining- Marketing companies build data models and prediction based on historical data. They run campaigns, marketing strategy etc. This leads to success and rapid growth.
  2. 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 are based on historical data.
  3. Data mining suggest banks regarding their 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. In other ways also Data mining serves a lot to the banking industry.
  4. Manufacturing obtains benefits from Data mining in engineering data and detecting the faulty devices and products. This helps them to cut off the defected items from the list and then they can occupy the best services and products in place.
  5. It helps government bodies to analyze the financial data and transaction to model them to useful information.
  6. Data mining organization can improve planning and decision makings.
  7. New revenue streams are generated with the help of Data mining which results in organization growth.
  8. Data mining not only helps in predictions but also helps in the development of new services and products.
  9. Customers see better insights with the organization which increase more customer lists and interactions.
  10. Once the competitive advantages are made it reduces with cost also 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 data-driven market.

Conclusion

It is important to note that time is spent in getting valid information from the 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 trend world. Everyone now-a-days required their data to be used in an appropriate manner and up to right approach in order to obtain useful and accurate information.

Loginworks Softwares is one of the best data mining outsourcing organizations that employ high qualified and experienced staff in the market research industry.