Data Mining and Its Applications

Table Of Contents

• What is Data Mining?
• Why is Data Mining Important?
• Complete Overview on Data Analytics
• Top Data Mining Techniques
• Data Mining in Today's World
• Data Mining Applications
• Uses of Data Mining

What is Data Mining?

Data mining is the process of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks, and more.

Data mining is a method of extracting data from multiple sources and organizing it to derive valuable insights.

1. Exploration: 

Any data mining technique first begins by gathering relevant information from the vast pool of data. You start by eliminating useless information and duplicate records. Thus, what you are left with is the correct set of relevant data.

2.Modeling: 

Then you start building the prototypes. You set your goals and expectations from the gathered data. You can build multiple models which may or may not lead to the same result. By developing different models, you can determine the most useful one. This can be a laborious task since you are applying different models to the same set of data.

3.Deployment:

 Finally, you test the model and compare it with your old data set. Once thoroughly tested, you can use the new data set in order to draw inferences, identify patterns and make predictions for your business. You can also compare the outcomes of new data against the old data.

Why is data mining important?

So why is data mining important? You’ve seen the staggering numbers – the volume of data produced is doubling every two years. Unstructured data alone makes up 90 percent of the digital universe. But more information does not necessarily mean more knowledge.

Data mining allows you to:

• Sift through all the chaotic and repetitive noise in your data.
• Understand what is relevant and then make good use of that information to assess likely outcomes.
• Accelerate the pace of making informed decisions.

Data Analytics:

Analytics uses data and math to answer business questions, discover relationships, predict unknown outcomes and automate decisions. This diverse field of computer science is used to find meaningful patterns in data and uncover new knowledge based on applied mathematics, statistics, predictive modeling, and machine learning techniques.

Who is using Data Analytics?

Recent advancements in technology have increased the potential of analytics. More data, better and cheaper storage options, stronger computational power, distributed and shared processing capabilities, and more algorithms make it easier to apply analytics to large problems and derive answers from data – in every industry.

How Data Analytics Works?

Every business is an analytics business. Every process is an analytics process ripe for improvement. And every employee could be an analytics user in some way. No matter what you plan to accomplish with analytics, the first requirement for any analytics project is data. Once you have data, you need to analyze that data. And then you need to deploy the results of your analysis to drive decision making. The faster organizations can move through the analytic life cycle, the quicker they can achieve tangible value from their analytics investments.

Top Data Mining Techniques

Data mining is a highly effective tool and can turn out extremely profitable if used wisely. There are many techniques already thriving in the market. Though no technique can be said as good or bad, it depends on your requirement. Therefore, it is important to choose the right technique to get high-quality results. 

Data Mining in Today's World:

Modern-day companies cannot live in a data lacuna. They have to evolve and keep up with technological evolution and upcoming digital trends to stay ahead of the competition. So, businesses today are prioritizing staying abreast of all the new developments in the field of data science and analytics. Data mining is one such process.

The complex data mining algorithms allow companies to make sense of raw data by segmenting large datasets, identifying patterns, and predicting outcomes. 

Data Mining Applications:

1. Financial Analysis

2. Telecommunication Industry

3. Intrusion Detection

4. Retail Industry

5. Higher Education

6. Energy Industry

7. Spatial Data Mining

8. Biological Data Analysis

9. Other Scientific Applications

10. Manufacturing Engineering

11. Criminal Investigation

12. Counter-Terrorism

How Data Mining Works?

Uses of Data Mining:

Data mining has proved to be a boon for the new-age marketers who have to face competition not only in the physical world but also at an increasing level of the same in the online world.

• Identification of Patterns in Customer Data: The Internet generates a huge amount of customer data on a daily basis. This calls for an increased level of precision in making value for such huge sets of data. Data mining helps in deducing patterns in raw data that is generated by the internet browsing patterns of the customers. Mining discovers this information and puts it in the right perspective for marketers. This helps them to decide their future marketing strategy.
• Website Optimization: As the real essence of data mining lies in deriving useful information from raw data so this also enables the developers to make suitable changes to the websites in order to secure better ranking and visitors. Data mining provides the most vital information that can be used for making these changes.
• Marketing Campaigns: Data mining is highly useful in truly evaluating customer response and interests over certain products and services. Even vague customer responses can be evaluated for determining patterns. Such information comes in handy for designing marketing campaigns.
• Customer Satisfaction: It is by the streamlined information about the customers that marketers are able to make the marketing campaigns more personalized. This not only enhances customer satisfaction but also helps in building customer relationships.

Weblinks and Reference:

https://cannabisbigdata.co/data-insights/what-you-need-to-know-about-data-mining-and-predictive-analytics/

https://www.redalkemi.com/blog/post/why-data-mining-is-still-important-in-2019

https://www.airtract.com/article/9-best-data-mining-and-data-collection-tools

https://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/

https://www.ricardo-barbosa.com/importance-of-data-mining-in-todays-business-world/#:~:text=Data%20Mining%20is%20largely%20used,estates%2C%20telecommunications%20and%20so%20on.