8 Key Differences Between Data Mining and Data Warehousing


The idiom “data is the new oil” appropriately explains how data is fueling practically every possible system across the planet. Everything from your online buying experience to your pastime, what you stream, and what you share on social media creates and operates on data that millions of people like you generate. To condense information into statistics, in 2020, an average individual created roughly 1.7 MBs of data every second. You get some mind-boggling figures when you multiply it by approximately 8 billion people. 

Not all of this data is erroneous. The majority of this unstructured, meaningless data can be well converted into a more organized (tabular/more comprehensible) format. This structured data, in turn, assists businesses all over the world in making critical decisions that allow them to match their goods and services to the demands of their customers. In simpler terms, good data use implies thriving businesses. 

This raises a vital question. How precisely can this massive amount of data, which the typical human brain cannot grasp, be harnessed? 

Read this article to learn how a massive amount of data is collected, organized, and processed to extract useful information using data warehousing and data mining. You will also understand the Difference between Data Warehousing and Data Mining in a detailed manner. 

What Is Data Warehousing? 

It is a process that permits data to be collected, integrated, and maintained under a cohesive relational model. Data warehousing solutions are a collection of analytical tools that allow this stored data to be evaluated to derive insights and unseen trends that ultimately drive business choices that boost anything from businesses to financial markets to other services. 

Advantages of Data Warehouses 

  • Data warehouses facilitate the tracking and analysis of patterns in vast amounts of data. 
  • Data warehouses may help firms get useful insights into their operations and discover opportunities for improvement by centralizing data from different sources. 
  • Data warehouses may provide a level of confidentiality and safety for company data. 
  • Data warehouses can help decision-makers at all levels of a business, from front-line employees to top executives. 

Disadvantages of Data Warehouses 

  • Data warehouses can be expensive to develop and manage, especially if they need to be updated often. 
  • A data warehouse’s data can not be timely enough to allow real-time decision-making. 
  • Data warehouse implementation can be difficult and operate as they need specialized skills and knowledge. 

What is Data Mining?  

Data mining is the systematic assessment of datasets to discover potentially relevant trends and correlations. The fundamental purpose of Data Mining is to process and obtain information from data collection. 

Data mining also entails building linkages and discovering patterns, anomalies, and correlations to solve problems and generate actionable information. Data mining is a broad and complex process with several components. 

Advantages of Data Mining 

  • It assists businesses in gathering dependable information. 
  • When compared to other data applications, it is a more efficient and cost-effective option. 
  • It enables organizations to make lucrative manufacturing and operational changes. 
  • Makes use of both new and existing systems. 
  • It assists firms in making educated judgments. 
  • Assists in finding the credit risks and fraud. 
  • It enables data scientists to evaluate massive volumes of data swiftly. 

Disadvantages to Data Mining 

  • Scientists must be well trained to utilize the tools efficiently. 
  • When it comes to tools, various ones operate with different sorts of data mining, depending on the algorithms they use. As a result, data analysts must be careful to select the appropriate tools. 
  • As data mining techniques are not faultless, there is always the possibility that the information is inaccurate.  
  • Companies can sell gathered customer data to other firms and groups, causing privacy issues. 
  • Data mining necessitates enormous databases, making the process difficult to manage. 

Difference between Data Mining and Data Warehousing 

Here, we have the difference between data mining and data warehousing: 

Comparison Parameters  Data Mining  Data Warehousing 
Definition  Data mining is the process of obtaining meaningful data from a collected set of recorded data. It is employed for the company’s evaluation and improvisation techniques.  The process of accumulating, sorting, and arranging sets of data in a generally accessible database is known as data warehousing. A data warehouse is used to assist management in making and carrying out decisions. 

Data is continuously evaluated. 


Data is regularly maintained. 
Purpose  The purpose of data mining is to derive useful information from existing data. 


A data warehouse’s major purpose is to offer a central store of information that can be promptly evaluated and searched to create significant insights. 
Managing Authorities    Managed by Data mining analysts are also widely regarded as business intelligence professionals. 


Managed by data warehouse managers and warehouse analysts.   
Functionality  Artificial intelligence, statistics, databases, and machine learning systems are all used by data mining technologies. 


Data warehouses are integrated, topic-oriented, time-varying, and non-volatile. 


Tasks  Pattern recognition logic is used in data mining to find patterns. 


Data warehousing entails data gathering and retention to facilitate analysis. 
Uses  Data mining is utilized in a wide range of disciplines, including research, business, marketing, sales, product development, education, and healthcare. Data mining gives a significant edge over competitors by offering more information and assisting in the development of stronger and more successful strategies.  Data warehouses are used to examine data from many sources. This form of online analytical processing is employed in data mining and business intelligence operations such as budgeting and forecasting. 
Example  Some data mining examples in the current industry are: 

  • Marketing 
  • Retail 
  • Banking 
  • Medicine 
  • Entertainment and more 
Some examples of data warehousing in diverse industries consider it a crucial aspect of their everyday activities. 

  • Investment and Insurance sector 
  • Retail chains 
  • Healthcare 
  • Education and more 


 To sum up the difference between data mining and data warehousing. A data warehouse is viewed as a storehouse for vast volumes of data. Data warehousing is the procedure of gathering data from disparate sources and combining it into a homogeneous data structure that can subsequently be utilized for data analytics. On the other hand, Data mining is the process of applying business intelligence to stored data to uncover underlying tendencies and linkages.



In this article, hopefully, you understood what data warehousing and data mining are, followed by the difference between data mining and data warehousing. 

Data warehousing and data mining are two vital terms in business and management. Thus, both tasks are necessary for driving and maintaining a business. A business can develop critical marketing tactics to eliminate mistakes by closing loopholes. If you’re interested in starting your journey as a proficient Data Scientist, UNext Jigsaw’s Data Science certification course is a match made for you! 

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