Difference Between Business Intelligence vs Data Mining

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Business Intelligence transforms the data into actionable information. It helps in optimizing organizations’ strategic and tactical business decisions using the applications, infrastructure and tools, and the best practices that facilitate access to the operational facts and figures of an organization. Data Mining is the process of evaluating the unrecognized patterns in the sets of large raw data, as per the different perspectives to categorize the data into useful information resulting in gaining business insights to solve issues beforehand.

Business Intelligence (BI)

In layman’s language, the Business Intelligence will analyze the complex raw data of an organization and transform them into useful information as required by the business. By using this useful information, the business will know what is working, what is not, what is the future, and how can you improve your business.

Below are the process involved in Business Intelligence:

  •     Aggregate the complex raw data of an Organization
  •     Analyze the data
  •     Present the data in a meaningful visualization
  •     Based on these facts businesses will take intelligent decisions for the wellness of the organization

There are many tools available in the market for Business Intelligence and any organization can use this tool to improve their business:

  •     Microstrategy
  •     Tableau
  •     QlikView
  •     Sisense
  •     Oracle Enterprise BI Service
  •     IBM Cognos Intelligence
  •     icCube
  •     Accurate Business Intelligence and Reporting Tool (BIRT)
  •     DOMO
  •     SAP Business Objects

Data Mining

In layman’s language as the word itself explains, it is just the mining of useful information or knowledge. Data mining helps in finding useful information or knowledge from an ocean of data.

There is an ocean of data available in an organization. There is no value for the data until you convert that into valuable information. It is required to analyze this data and convert them into valuable information. Therefore, the Data Mining will help to extract this valuable information from huge sets of data available. The other process involved in Data Mining are:

  •     Cleansing the Data

It will handle corrupt, irrelevant, inaccurate, incomplete data

  •     Integrating the Data

Combine multiple data sources into meaningful information

  •     Selection of Data

Data, which are meaningful for the analysis, will be retrieved from the database

  •     Transformation of Data

Converts data into specific form that is relevant for mining

Data Mining

Will extract data patterns that are required

  •     Evaluating the patterns in Data

Will extract patterns representing information or knowledge depending on interesting measures.

  •     Presentation of information or knowledge

Will present the mined knowledge to the business using different visualizations

The valuable information or knowledge revealed from Data Mining can be used for many purposes, such as:

  •     Management Analysis
  •     Market Analysis
  •     Risk Management
  •     Corporate Analysis
  •     Customer Management
  •     Fraud Detection

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