Data Mining

What’s Data Mining?

Data mining is everywhere in the digital world today. Any and all competent analysts and computer personnel should know a thing or two about it. To put it simply, data mining is the exploration and analysis of large data to discover meaningful patterns and rules. Many people put this topic under the data science field of study as it differs from predictive analytics because it analyses historical data. Data mining, on the other hand, aims to predict future outcomes. Data mining predictions are used to build up machine languages channeled towards modern artificial intelligence applications such as search engine algorithms and recommendation systems.

Where is it used?

Data mining is a widespread technique that is used across various computer-related endeavors. You can find instances of Data Mining being used in Computer Applications, Database Marketing and Targeting, Credit Risk Management, Credit Scoring, Fraud Detection and Prevention, Healthcare Bioinformatics, Spam Filtering, Recommendation Systems, and even Sentiment Analysis.

Steps to Data Mining 

Different people can use data mining in different ways depending on the situation. However, acceptable data mining is an intricate process that involves six major steps:

  • Business understanding

This is the first step, and it involves establishing the goals of the project as targets and then finding out just how data mining can help you achieve these goals. At this stage of data mining, a plan is developed to include timelines, actions, and role assignments.

  • Data understanding

In this step, data is retrieved from available sources from a variety of fields. This stage can use Data Visualization tools to explore the properties of the data collected and ensure it will help achieve the business goals.

  • Data preparation

In this step, you filter out the data and fill in the missing data to ensure it is ready to be mined. Depending on the amount of data analyzed, the sources collected from, and the fields; Data processing in this step can take a dreadfully large amount of time if done manually. Luckily, modern database management systems (DBMS) use distribution systems to greatly improve processing speed by spreading the workload across various computers. With this method, information is more secure because instead of having all an organization’s data in a single data warehouse, you have it across many. Remember always to have failsafe procedures to prevent total data loss.

  • Data Modeling

In this stage, analysts use mathematical models to search for data patterns using sophisticated data tools.

  • Evaluation

At this point, the accumulated information is then evaluated and compared to the initial objectives. This determines whether or not the findings should be used across the organization.

  • Deployment

This is the last stage in the data mining process. Here, the findings are distributed across the daily business operations. An enterprise business intelligence platform can be used to provide a single source of the truth for self-service data discovery.

Benefits of Data Mining

Data mining is a complex process that can greatly increase a business’s productivity if utilized properly. Below are some of the key benefits of Data Mining.

  • Decision-Making

Data Mining lets businesses and corporations analyze data and make automated decisions. These decisions can be both routine and critical, depending on the situation and algorithm applied. This eliminates human delays. It can be used to instantly detect fraudulent transactions, request verification, and even secure personal information in banks and other financial institutions that hold sensitive user data. 

  • Prediction and Forecasting

Data mining speeds up planning processes for organizations by supplying reliable forecasts based on past trends and current conditions. This implemented in daily business helps organizations prepare for decisions before they even come up.

  • Costs

Using data mining efficiently in an organization helps reduce costs by properly allocating resources. Transportation organizations imbed RFID chips in passengers’ checked baggage. They deployed data mining models to identify holes in their process and reduce the number of bags mishandled, thus reducing checking costs.

  • Problems associated with Data Mining

Data mining is a powerful process and is very useful, but one can experience some challenges when handling such large amounts of complex data. A few problems arise when extracting, analyzing, and gaining insight from such large amounts of data daily.

This is easily the most challenging issue one can encounter when data mining. It affects every field where collecting and analyzing data is involved. It’s grouped into four major issues; volume, variety, veracity, and velocity. Dara mining involves balancing out these issues actually to achieve goals. The volume considers the size of data and the issues that arise when attempting to store or process such large quantities of it. Variety deals with the fact that different forms of data are collected and stored. To analyze such, data mining tools must be able to process such variety simultaneously. Velocity deals with the speed of data created and collected. As opposed to volume and variety, velocity involves the challenges associated with the increasing data creation rate. Veracity is the last group of big data challenges, and it covers the fact that all data is not always equally accurate due to messy or incomplete data collection. Analysts much acknowledge this when collecting and processing data.

  • Over-Fitting Models

When the model used in data mining contains many independent variables to generate predictions, it becomes over-fitted. If the values are too few, the model becomes overly simple and virtually useless; however, it becomes a challenge to connect if it contains too many variables. Analysts must find a balance and moderate the balance of each model used.

  • Privacy and Security

With the amounts of data processed, organizations are forced to move their information to cloud servers to prevent data loss. This makes work faster and more secure, but it also opens up some challenges. Cloud servers face significant privacy and security threats. Malicious hackers are always looking to exploit holes in security systems, so organizations must pay top dollar to ensure that their intel is secure.

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