We are in the information world , what we have is more and more data being caused in every single second,in most transactions we do there are some amount of data being downloaded to a local database. Organizations are saving ,processing and analyzing these data for better future outcomes.
So, What is Data Mining it is a mathematical method that includes some equations, algorithms and neural networks segmentation. Wherever we have processes and data , to sort out some business related questions We need to analyze these data and processes for that is Data Mining is helpful.
In today’s business era, there is a massive amount of data generation frequently. This Data can use to forecast future business scenarios. It takes the right amount of information analysis, which can be where organizations overlook and increase productivity and efficiency. Data mining can be applicable to e-commerce startups, advertising, marketing effectiveness , healthcare processes , logistics processes and many more businesses.
Data Mining is a continuous process to discover and extract data from a large amount of raw data.In other words Data Mining as Knowledge Discovery in Data (KDD), Knowledge Extraction and Information Harvesting, and Data Scraping.
Why is data mining important for Businesses?
Data Mining is used by businesses to get information strong consumer attention to achieve their goals to retail and financial bases. The benefits of data scraping are Automated Decision-Making, Accurate prediction, and customer insights. Businesses can have an insight into their customer’s interest, like how social media is affecting their business, for generating sales goals, revenues, and customer conversions. It will be helpful to target the right customers and can develop more productive strategies with cost-effective purposes.There are many more applications in Data Mining.
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Benefits of data mining for different organizations
- Manufacturing: Define variations between purchase orders.
- Mail Order: For Mail promotions through a targeted audience.
- Supermarket: Combination of products to sell together with their needs.
- Airlines: Increase sales for frequent flyers with special discounts or offers.
- Department store: Forecast their seasonal products.
- Insurance: To identify risk factors and fraud detection.
- Banks: Increase their leads with direct marketing campaigns
Data Mining Applications and Trends
Data Mining nowadays going popular, WHY because data continues to increase. For example just look at the trendy Social Networking sites Facebook,LinkedIn and Twitter what it is ? It is nothing but a big Data collection that mark out the information of the people or different types of data. What they like and where they are.
In every single day we are simply capturing and discovering data. In this way we are in the way of information economy. So in simple way to extract the data from different data warehouses is Data Mining.
Data Mining applications extensively used in the health industry, e-commerce, direct marketing, telecommunication industry, finance industry, and customer relationship management (CRM). For better understanding, let’s start with examples.
- Customer Segmentation: Use to understand the customer with data clustering.
- Market Basket Analysis: Use for the retail industry to make a strategy for combining the product selling process.
- Risk Management: Insurance companies apply to uncover risks.
- Fraud Detection: Use by the credit card company to research like abnormal spending.
- Demand Prediction: Retail/online industry use to increase sales.
Data mining is accessible in diverse forms as data scraping, web mining, text mining, audio & video mining, pictorial mining, social networks data mining, and relational database. Web mining which includes the rest of all mining forms which used to extract data from a direct website.
Continuous web development is being increased text mining and web mining is being more popular today. As we are living in a competitive world we have to be updated that which strategies are used by our competitors to grow their sales.In this case Web Mining is more useful.
Web Mining: To extricate information from the direct web by using data mining methods. Web Mining contains three techniques,
- Web – content mining: It includes data in the form of text mining, video mining, and audio mining.
- Web – structure mining: It shows navigation through hyperlinks and tags.
- Web – usage mining: For finding analytics through server logs and web page logs.
Steps of Data Mining
Data mining is not so simple task it is time consuming task it has some certain tasks as per below
- Data Collection: The very first step is to gather the data.
- Data Cleaning: It is necessary to remove false data or duplicate data for that Data Cleaning is important,though there is data scrubbing tool may help to do this task.
- Data Analysis: For finding and inspecting patterns Data Analysis is important.
- Evaluation: For prediction of final outcomes evaluation is the final step.
Models of Data Mining
There are different models are associate with Data Mining Process. And three of them are listed below. To create such Predictive models , It is mandatory to do process,filter and study that data perfectly.
- Predictive modeling: It helps to analyse between the past set of data and future behavior.
- Descriptive modeling: It shows the gathered data in a form of demographics and customer behavior with the use of Data Clustering.
- Prescriptive modeling: It is a forward step of Predictive Modeling,it shows outcomes in every performance.
If any business wants to do manual or customize data mining service for their particular business website , many web data mining software and services are available in the market.
The outsource web data mining companies are specialized for manual data mining, data clustering, and data analytics. They have software that will scan the internet to search for relevant details and information with Data Scraping methods. The web data mining software gives you a much better authority on your business affairs and helps to see excellent outcomes in the future.