In this day and age, businesses are generating and storing a large volume of data to analyze and generate insights to optimize processes, reduce costs, and engage better with customers, among others. What makes these business insights possible is data mining, which is essentially the process of sifting and sorting through data to identify underlying trends and patterns.
Data mining in business analytics has been around for some time now, with the oldest references going back to the 1930s. But it became popular only in the 90s as businesses were trying to make sense of all the data they have been generating.
With data mining, businesses can extract valuable insights, forecast business outcomes, mitigate risks, and identify new opportunities. In this article, we will discuss what is data mining and its importance in business analytics. We also have a list of top data mining tools for your perusal.
Table of Contents:
What is Data Mining?
Data Mining in business is the process of turning raw data into useful information by identifying hidden patterns and trends. Various tools help businesses in parsing large data volumes in batches to pull out important information. This information then helps businesses to fine-tune strategies, increase revenue, reduce cost, effective marketing, enhance customer relationships, mitigate risks, and much more.
As more and more businesses consider big data and analytics to be their prime digital drivers, let’s take a glance at the importance of data mining in business. Data mining helps businesses:
- To gain a competitive advantage
- A better understanding of customers and prospects
- Have a good oversight of business operations
- Identifying new business opportunities
Different organizations from different industries will benefit in different ways by using data mining, depending on their respective priorities. In each scenario, the data mining process helps businesses in understanding how to make better decisions by analyzing their information and then proceeding forward.
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Process Overview of Data Mining in Business Analytics
Most of the businesses using data mining would know and follow a set process that is described below. Here’s a step by step guide to using data mining in business analytics:
Understand Business Objective: The first step is to identify and understand the business objective and the purpose that needs to be solved with Data Mining. Next is to convert the objective into a data mining problem statement and devise a plan. This is important to design an accurate data mining algorithm that fuels the business objective and provides insights and information that are actually required.
Familiarizing with Data: Once the objective is clear, the next step is to collect the data and get familiar with it. Businesses would need to set the process for collecting, organizing, storing, and managing the data. Here it is important to identify any existing issues in the above-mentioned stages of data collection to storage, along with getting readily available insights and observing the subsets.
Preparing the Data: The information alone is not enough for implementing the whole data mining process. It needs to be in a form that is production-ready. It means transforming the computer language data into a form that is understandable and quantifiable by the stakeholders. Along with transformation, data preparation also involves data cleaning.
Data Modeling: This step involves mathematical models that would search for the hidden patterns in the data. As technology is advancing, machine learning techniques are being actively used by businesses to create models.
Evaluating the Model: Once the data model is complete, there is a need to evaluate it on certain important parameters. The steps used in data modeling need to be reviewed to ensure that the result from the model aligns with the business objective.
Deploying the Data Model: Once the model is created, reviewed, and optimized if required, it is deployed to generate the output of the data mining process. Depending on the type of output that is being generated, deployment could be a simple or a complex part of the whole data mining process.
Data Mining Techniques in Business Analytics
When a series of different data points are grouped together based on their characteristics, it is called clustering. The data is divided into subsets enabling informed decision-making. Business analysts can have information about broad demographics and their behaviors. A simple use case of clustering could be when a retail business segregates and clusters customers based on the product they have purchased. This helps them to run targeted ads for customers in each cluster.
Finding correlation or association between data points in a data set is known as an association in data mining. It helps discover unique relationships between variables in a database. This type of data mining technique is mostly used to determine marketing strategies. A good use case of association is how the government employs census data to plan for public services, or how doctors use association to diagnose medical conditions more effectively.
Data cleaning in data mining is to prepare the data before it is mined. It involves the elimination of duplicate data, removal of corrupted data, data organization, and filling up null values. The information drawn from the data after it is cleaned can be harvested for analysis. Working with data that is incorrect nullifies the whole purpose of data mining in business analytics. No matter how sophisticated a data mining model is, businesses will ultimately suffer if the data being used is not cleaned.
A graphical representation of data better illustrates its meaning, especially to the business stakeholders who are not data engineers or analysts. Information drawn out of data could be represented through charts, graphs, diagrams, and more. Data visualization is extensively used in business reporting because of how well they communicate the findings from the data mining process. When the data is presented in such an easy-to-understand manner, businesses are able to make informed decisions faster.
Classification is one such data mining technique that can be applied to any business in any industry. It is also considered a type of clustering but mostly for comparative analysis. It is used to categorize broad groups of target audiences within demographic or other factors. By categorizing, businesses can get comprehensive insights. A common use case of categorization in data mining could be how financial institutions classify consumer profiles depending on various variables to project credit card risks or provide new loans.
It is a data mining technique that detects anomalies in patterns identified in the data set. Outlier detection in data mining is the key to maintaining safe databases. Businesses use it for use cases such as fraud detection, or abnormal account activity that might suggest theft. It flags any unique data points that diverge from the overall data set. Sometimes outliers can be of an instructive nature as well. Another use case could be understanding anomalies in production or distribution lines to identify any blocker or bottleneck so they can be fixed.
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Free Tools for Data Mining in Business Analytics
Here we have listed some of the free data mining tools for your perusal:
DataMelt is used for numeric computation, statistics, mathematics, data analysis, visualization, and more. The platform combines multiple scripting languages such as Ruby, Python, and Java.
ELKI Data Mining Framework
It is an open-source data mining software in Java that focuses on unsupervised methods in cluster analysis and outlier detection.
Orange Data Mining
Orange Data Mining is a diverse toolbox that has the capability to build data analysis workflows visually. It also supports open-source machine learning and data visualization.
Rattle GUI is a free and open-source software package providing a graphical user interface (GUI) for data mining using the R statistical programming language.
In this article, we understood various aspects of data mining in business analytics. We went through the importance of data mining in business analytics, the techniques, and even some free tools which could help you effectively mine the large volumes of data that businesses generate.
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