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What is Data Mining?

Data mining is the exploration and analysis of large data to discover meaningful patterns and rules. It aims to predict future outcomes but it additionally is also used to build machine learning (ML) models that power artificial intelligence (AI)

What is Data Mining used for?

Data mining can be used for

How does Data Mining affect you?

One of the most evident ways data mining is being used today is through database marketing. Every time you search, click, scroll past anything online you leave a digital footprint. Database marketing collects, sorts and categorizes your activities in order to target specific demographics with certain types of advertisements.

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Data Mining Uses

Uses of Data Mining

  • Data Base Marketing and Targeting
  • Credit Risk Management and Credit Scoring
  • Fraud Detection and Prevention
  • Healthcare Bioinflrmatics
  • Spam Filtering
  • Recommendation systems
  • Sentiment Analysis
  • Qualitative Data Mining (QDM)

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Data Mining: Steps

  1. Business Understanding
    2. Establish the goals you wish to reach by using data mining. What is your plan? arrange a plan with a timeline, actions, and role assignments
  2. Data Understanding
    3. After your data is collected use data visalization tools such as spreadsheets to explore the properties of the data. Ensure that your findings will help you achieve your goal.
  3. Data Preparation
    4. The bulk of the work is concentrated here. Data is cleansed and ready to be 'mined'
  4. Data Modeling
    5. Mathematical models are then used to find patterns in the data using sophisticated data tools.
  5. Evaluation
    6. The findings are evaluated and compared to business objectives to determine if they should be deployed across the organization.
  6. Deployment
    7. In the final stage, the data mining findings are shared across everyday business operations. An enterprise business intelligence platform can be used to provide a single source of the truth for self-service data discovery.

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Challenges with Data Mining

The challenges of big data are prolific and penetrate every field that collects, stores, and analyzes data


  • Big Data:
    • Big data is characterized by four major challenges: volume, variety, veracity, and velocity
    1. Volume
      2. describes the challenge of storing and processing the enormous quantity of data collected by organizations. This enormous amount of data presents two major challenges: first, it is more difficult to find the correct data, and second, it slows down the processing speed of data mining tools.
    2. Vareity
      3. encompasses the many different types of data collected and stored. Data mining tools must be equipped to simultaneously process a wide array of data formats. Failing to focus an analysis on both structured and unstructured data inhibits the value added by data mining.
    3. Veracity
      4. details the increasing speed at which new data is created, collected, and stored. While volume refers to increasing storage requirement and variety refers to the increasing types of data, velocity is the challenge associated with the rapidly increasing rate of data generation.
    4. Velocity
      5. acknowledges that not all data is equally accurate. Data can be messy, incomplete, improperly collected, and even biased. With anything, the quicker data is collected, the more errors will manifest within the data. The challenge of veracity is to balance the quantity of data with its quality.