How Data Mining can make business models more effective?

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Data Mining

If you’re the entrepreneur of a startup or a large firm, this topic should sound like music to your ears. As a firm operates, they bound it to generate an extensive amount of data, comprising different data.

If data is analyze as it is, it will only make a minimal amount of sense to any firm but if the data are interlink, analyze, and co-relate to,

it will make a lot more sense to any firm and it is a process that does exactly that.

It is the process of analyzing large data sets to find anomalies, correlations, and patterns in the data sets. Many corporate applications use the techniques to suggest new business strategies to organizations using the data in their large databases.

It involves data collection and analysis of data sets to derive meaningful business-related information from them. It  is a technique that is use in Machine Learning programs, Search Engine algorithms, business forecast models, database marketing, etc.

Data Mining Process

The Process involves the following steps. They are:

Data Mining Process

Data Integration

They collect the data from various sources (usually from unique records of a company) and integrate into one data set so that data analysis becomes easier.

Data Selection

We will have a definite objective in mind and therefore will not need all the data that we had integrated into the data set.

Data Selection is the process of discarding the data that we do not require so that we can analyze only the required data.

Data Cleaning

If you’re thinking about how data can get dirty, let me tell you that this is not that type of cleaning. It means to eradicate incorrect, missing,

and inconsistent data in our data set so that we don’t encounter data anomalies when we do data analysis.

Data Transformation

Data Transformation means to organize the cleansed data in meaningful structures so they can be mine them to derive useful information.

The Techniques such as data smoothing, data aggregation, data normalization, use to transform the data.

Data Mining

The companies can do Data Mining to transform the data set to derive informative patterns and correlations in the data set. Data Mining can use to create new business strategies and business forecasts.

Pattern derivation and analysis

The derived patterns and Co-relations from the mined data set are analyze carefully to derive any meaningful information.

The techniques like data clustering, association analysis, etcetera is use in pattern analysis.

Decision making

If it derives some useful information in the pattern analysis phase, it implements soon so that the efficiency of existing business models can improve or new.

The companies can plan effective business strategies. Data Mining can also do business forecasts.

What are the techniques used in Data Mining?

The business can apply Data Mining to data sets to derive some useful information. Different techniques are used to achieve different objectives. Given below are some of the most commonly used the concepts and techniques.

Frequent pattern mining a.k.a. Association analysis

This technique looks for recurring patterns in a data set. It looks for associations and correlations between the different data entities in a data set. This technique is mostly used by e-commerce websites to determine so they buy together which products like toothbrush and toothpaste, badminton racket and cock, etc.

Correlation analysis

Correlation analysis is measure by the mathematical function known as Lift. It shows whether the purchase of one entity enhances the purchase of the other entity or not.

Example: Let us assume there are two products, A & B. The probability of a customer purchasing product A by P(A) and it is always between 0 and 1. The probability of a customer purchasing product B by P(B) and the probability of a customer purchasing both the products by P(A U B).

The mathematical function Lift (A, B) = P(A U B) / P(A) * P(B). If Lift (A, B) >1 then it means that the sale of item A implies the sale of item B. If Lift (A, B) < 1 then it means that the sale of item A negates the sale of item B. If Lift (A, B) =1 then it means that the sale of item A does not affect the sale of item B.

Decision tree induction

In a decision tree, each leaf node tests an attribute and the branch of that tree gives the outcome of the test. Entities are ‘classified’ based on the branch nodes’ outcomes. Decision trees can classify the sales of a product on a month as being good, okay, or poor among other uses.

Cluster analysis

In cluster analysis, similar data are group together and then various algorithms apply to them to get various informative patterns. We can do cluster analysis to get buying trends, sales graphs, business forecasts, etcetera.

Those are some of how Data Mining for Business Analytics is done. The above algorithms give any business, valuable business insights. They can improve the efficiency of existing business models, devising new and effective business strategies, and predicting future sales or business value.

Data Mining Vs Data Analysis, is there a difference? 

Data Analysis is a technique applied on a large data set to test various business models, business hypotheses, product campaigns, etcetera. If you want to determine if your proposed business strategy will be successful or not, you can apply data analysis on the required data set to do so.

It is a much more elaborate process than Data Analysis. We apply it to an extensive set of data to determine informative patterns, correlations, and associations between the different data entities in a data set.

It can provide any business with valuable business insights. We apply a lot more algorithms in Data Mining than in Data Analysis.

Read similar article: Importance of data cleansing for businesses

The myriad tools used in data mining

There are several Tools , Some of them are for free download, while others can only installed in one’s system by paying for it. The list of the software mentioned below is those that are available for free download.

They provide basic data mining facilities while the paid software provides advanced data mining facilities. The list of free data mining software are:



Xplenty has features to gather data from various sources, integrate them into a single data set, cleanse the data, and apply a wide variety of algorithms on them to get interesting patterns, correlations, and associations from the data set.

It has a robust interface that can implement ETL, ELT, and replication solutions. The interface can also schedule pipelines via a workflow engine.



Rapid Miner is a data mining software that has code in Javascript. One can do tasks like deep learning, text mining, machine learning & predictive analysis via it. Rapidminer can installed on one’s PC or we can access it via any cloud platform.

Rapidminer comes in three versions. Namely:

Rapid Miner Studio: One can do tasks like workflow design, prototyping, and validation in this version.

Rapid Miner Server: This is used to run data models created in Rapid Miner Studio.

Rapid Miner Radoop: It can execute data models in the Hadoop cluster to simplify predictive analysis.



They have coded this data mining platform in the Python coding language. We match it to design machine learning algorithms. The software has components called ‘widgets’ that can-do tasks like data visualization, pre-processing and evaluation of algorithms, and predictive modeling.




Weka is also known as the Waikato Environment. The University of Waikato in New Zealand developed it. It is best suited to perform data analysis and predictive modeling tasks.

It contains algorithms and visualization tools that enable machine learning. Before performing any data mining task, Weka converts all the data in a data set into a ‘flat-file’ format. Weka can also provide SQL database connectivity and process the results of SQL queries.




KNIME is a data mining platform developed by AG. It is best suited for scheduling pipelines via a workflow engine. KNIME has many embedded algorithms that can enable Machine Learning and Data Mining tasks.

KNIME can also perform customer data analysis, financial data analysis, and business forecasting tasks. They have a robust but user-friendly interface. Even the common man can master this platform quickly and perform data analysis and financial data analysis tasks in it.

Importance of Data Mining

The importance of Data Mining is paramount. We use the techniques in a wide variety of domains for different purposes.

Basket Analysis

This form of data analysis is use by e-commerce websites and shopping stores. We analyze customers’ shopping trends, behavior, tastes, and preferences. Basket Analysis technique can check they buy together which products and predict a customer’s future purchases.

Sales Forecasting

This technique is like basket analysis. It analyzes customers’ buying trends, tastes, and preferences to make future sales predictions for any product. Sales Forecasting can predict when customers will buy a certain product and also what products they are likely to buy.

Database Marketing

Every company in the world, be it a startup or a Fortune 500 company, is looking to get valuable leads and expand their business. Database Marketing companies gather customers’ information on different domains and analyze their behavior, buying trends, tastes, and preferences.

They then sell that data (databases) to their business clients so they (clients) can get valuable leads and expand their business. Database Marketing involves analyzing the behavior of a lot of customers so that firms can develop a “generic marketing strategy” which has a high probability of being successful.

Inventory planning

The sale of any product throughout a year varies. During some months they sell it more than during other months. By analyzing the sales pattern of products, stores can manage their inventory of products accordingly. This practice of inventory management increases the operational profit of stores.

Customer Loyalty

Stores can use data mining techniques to see how changes in the price of a product affect customers’ loyalty. They can also calculate loyalty bonuses depending on the customer’s buying trends. This helps stores and brands retain their valuable customers for longer.

Data Mining in Healthcare

Before the invention of data mining, patients’ records is save in printed files. This practice made retrieving the required patient’s data difficult and tedious.

These days, patients’ records is save in mobile apps that make use of data structures like clusters, classification, decision trees, neural networks, and time series to retrieve the need patient’s data quickly.

This feature has made life easy for doctors and patients alike. It has improved the efficiency and speed of healthcare business models and saved the lives of many people.

Uses of Data Mining in Decision Making

It  can help businesses analyze the sales trends of products and get a fair idea of customers’ buying trends, behavior, loyalty, tastes, and preferences.

This analysis can help businesses improve the efficiency of their business models, devise more effective marketing strategies, and devise more appropriate customer loyalty programs.

It can help businesses get insights from raw data and devise actionable strategies based on those insights. It helps businesses make effective and right business decisions.


It is not a technique vital to businesses. Businesses, especially small enterprises, can function without using data mining techniques, but doing so will limit the efficiency of their business models and hamper their growth and expansion.

Firms will also miss out on valuable business insights and revenue opportunities if they do not use data mining techniques. Retention of loyal customers and devising of customer loyalty programs are also more difficult without employing techniques.

So, if you’re an entrepreneur, please use techniques to improve the efficiency of your business model, get valuable business insights, and devise customer loyalty programs. Why do you want to leave money on the table as it is? Pocket-it by employing appropriate techniques!

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Raaghav has done a respectable degree of MSc. in Software Engineering from Sathyabama University. He graduated in 2012 but has surprisingly taken up a career in content writing. His English is rich and grammatically correct. He can write even the most mundane topics interestingly as he has had a passion for the English language since childhood.