Data Mining is the way of finding hidden patterns and valuable information through data analysis. Data mining contains set of phases like Data Modelling, Data Integration process, Data processing, Data Evaluation, and Deployment etc. By using data mining, you can easily segregate a big number of data sets into small patterns, later you can store them in the database easily.
Why Data Mining?
We live in a digital era where data can be transformed in a Nanosecond which is faster than a normal human capability. In the corporate sector, employees work on a large volume of data which is extracted from different sources like Social Network, Media, Newspaper, Book, cloud media storage etc. But sometimes it may create some difficulties for you to summarize the data. Sometimes when you fetch the data from other sources, you cannot predict that how much data will be stored in the database.
As a result, data becomes more complex and takes time in the mining process. Let me tell you the solution to this problem. Try to retrieve data in the form of category whatever type of data you want or we can say use filtration when you retrieve data. Data mining technique gives the good amount of quality. Excel is the best tool for data mining process. Data mining is very useful in the existing mechanism like as Market Analysis, Data Management, Risk management, Corporate Analysis, and Fraud Detection.
Benefits of Data Mining
- Data mining attributes can discover the missing values from the inconsistent data which increases the optimization speed of the optimal result.
- If you work consistently until the deployment of the business objectives that increases the brand loyalty that will really help you in marketing campaigns. Because customers can directly interact with the organization to serve better.
- By the completion of project delivery to the stakeholders/customers, that surely increase the trust on your organization and the work which you delivered. It simply can increase your customer database.
- Data mining techniques and tools help in making the big decision to increase organization’s revenue.
- By using data mining, it can convert complex and inconsistent data into a general structure that customer can understand easily.
The process of Data Mining
There are six common phases of Data Mining process as follows.
1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Data Modelling
5. Data Evaluation
First of all, it’s important to understand the project’s requirement and its objectives by setting up a group of Business Experts, Domain Experts, Business Analysts, and Data Mining Experts. It is very important to understand that what client is looking for. You must understand the business objectives and get the business’s needs. Next, find out the current condition and develop a team of data mining experts to achieve the business profit.
Data Understanding or Data Exploration
Domain and data mining experts collect the data from other sources in several formats. Then, load the collected data into an analytical tool by using data integration and make the strategy to understand the metadata, and unstructured data which we collected. Find out the problems in the data and get familiar with the collected data. As a result, serve the best quality data by using statistics data analytic tool for exploring the high-quality data.
Data mining experts create a data model before sending the data into the modeling process. They collect the high-quality data, cleanse it and save them into different formats. But the question is how the experts cleanse the data?. Because, in the real data world sometimes data is stored inconsistent, noisy and incomplete into the tool.
Let’s understand an example, a name of the customer is stored differently in other tables or in the database. So, this belongs to an inconsistency of data. If the data is not cleaned then the result cannot be good or accurate. Now, domain experts create a raw data into these analytics attributes likes missing value, attribute value, average value and data of interest. By using these tools you can easily prepare the raw data and automatically correct the missing value in the data easily. This is how data is prepared and cleaned in this process of the data mining.
In this phase of data mining, choose the selected data and prepare a dataset by using different mining functions for exact data mining problem. Some of them require data types to access each model to validate the data quality.
In this phase, data mining experts evaluate the final data model in terms of business objectives. If the data model does not meet the expectations, they roll back to the modeling phase to make changes in the attributes until its optimal value are achieved. Later, when they meet the expectations then get the final reviews like, have all the models achieved the business objectives?. As a result, data mining expert makes a plan to send the final evaluation in the deployment process.
In this final statement of data mining, whatever you collect either data behavior or data information throughout the data mining process, you need to make it in a separate format that stakeholder or client can use it when they need it. As a result, the data mining experts export the data into applications or into other databases like spreadsheets. Intelligent Miner tool assists you to explore this process. Data mining results help in the selection of the data, exportation of the data, and the transformation of the data.
In this article, we have learned the complete process of data mining. I also explained the data mining phases in a general form. These six are the important phases to complete a project since business understanding to deployment. Data mining process works independently and iteratively. Last three phases of data mining process support OD (Oracle Database) and ODM (Oracle Data Mining). If you want to serve better for your organizations perspective then choose these processes. I am pretty much sure you will get the 100% successful result from the client or stakeholder. I hope you enjoyed my article.