Data mining processs

The Data Mining Process 

The data mining process involves five stages:

1. Understanding the Goals of Your Data Mining Project 

The first stage of data mining defines how the process will support your business goals. For example, what areas of business do you want to improve through data mining?

  • Do you want to make your product recommendation systems better like Netflixdid?

  • Do you want to understand your customers better through personas and segmentation?

After codifying your data mining goals, you can develop a project timeline, key actions, and assign roles for completing the project. 

2. Understanding Your Data 

In the next stage, you’ll assess your data sources. Data visualization tools like Google Data Studio, Tableau, or Grapher allow you to explore the properties of your data to decide which information will be useful to achieve your goals. Understanding your data also helps you determine which data mining strategies will produce the insights you want.

3. Preparing the Data (ETL)

In the data preparation stage, you'll use ETL (extract, transform, load) strategies to prepare your data for analysis. You can use an automated, cloud-based ETL solution like Xplenty to extract your data from different business applications, cloud-based SaaS platforms, and other sources—then transform the information and optimize it for high-speed analysis. Ultimately, the ETL process cleanses the data, addresses missing information, and makes sure your data mining applications can analyze the information as a whole. 

4. Analyzing, Mining, and Modeling the Data

At the heart of the data mining process, you’ll introduce the prepared data to business intelligence (BI) tools—like Tableau Server, Looker, InsightSquared, Amazon QuickSight, or Microsoft Power BI. These tools will use different machine learning algorithms to mine the data for patterns and forecast future trends. More on this below!

5. Reviewing and Sharing the Findings Across the Organization

In the last stage of data mining, your data team and key decision-makers will study the results to decide: 

  • If the findings are accurate

  • If they support your goals

  • How to act on them

  • How to share the findings with your team

When it comes to sharing your data-mining results, most enterprise-level BI platforms allow you to distribute key findings across your organization quickly and efficiently.

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