Organizations adopted modern techniques and methods for making data-driven business decisions. These methods include Data Warehousing and Data Mining as one of the widely used operations. Implying Big Data Engineering to boost business performance has other essential aspects of understanding and applying, such as Data Mining for analyzing the data patterns and Data Warehousing for stacking and organizing data at shared storage. However, generally, people get confused between both terms. This blog will introduce you to Data Warehousing and Data Mining; you will also understand the key differences between them.
Data Warehousing is a crucial stage for organizing data and storing it in Data Warehouses. It is the preceding stage of Data Mining. Data Engineers integrate data from multiple data sources and orchestrate it into a uniform schema to simplify Data Processing and analysis.
Data Warehouse is also known as the Decision Support System (DSS) because they are always intended for storage and Data Analysis, unlike functional and operational use as Databases.
There are mainly 3 types of Data Warehouses that have different functions and applications. The other Data Warehouses types are listed below:
- Operational Data Store: It is functional and updated frequently. It consists of user data and sometimes employees as well.
- Data Mart: A Data Mart automatically collects data from multiple sources, and it’s a direct sub-stage of the Data Warehouse. It is commonly used by Sales and Marketing teams for storing customer data.
- Enterprise Data Warehouse: It is a unified Database that connects and integrates every company department.
Data Mining involves defining relationships and identifying correlations between datasets to generate valuable insights and make smarter data-driven decisions. It involves handling vast volumes of data and analyzing it to extract hidden patterns. It is an essential part of the company’s regular activities for understanding and improvising customer’s requirements, risk management, evaluating feedback, etc. Data Mining is one of the essential skills for Big Data Engineering as it involves Data Wrangling and Data Modeling.
Data Mining is a systematic process that helps companies gain a better understanding of the business and performance by analyzing data. Also, it enables organizations to detect faults and predict future trends. Business users and owners, along with Engineers, use Data Mining to drill down into data stored in Data Warehouses. Some of the features of Data Mining are listed below:
- It helps in predicting future trends and expected results.
- It allows business users to perform Data Analysis and discover patterns in data efficiently.
- Data Mining allows organizations to make actionable insights and gain valuable information.
- It helps users handle large datasets holding business data with ease.
|Data Warehousing||Data Mining|
|Data Warehousing consists of extracting and storing data from multiple sources. It compiles, unifies, and organizes the data clusters to a shared data storage.||Data Mining is drilling down into the data and identifying the data patterns from the Data Warehouse. It helps companies analyze data and generate insights.|
|It is operated by Data Engineers and is responsible for maintaining the streamlined flow and availability of data to its users.||Business users carry it out with the help of Engineers.|
|Data Warehouse syncs data from multiple sources at regular intervals to keep the data up to date. This ensures that data is stacked periodically, and users can access the information quickly.||Data Mining involves syncing data from the source when requested. To reduce complexity, the data is analyzed in small phases or broken down into simpler parts.|
|Its aims to make data accessible to users for analyzing it. It helps maintain data sequentially so that users can use any piece of data with ease.||It aims to simplify complex data into a more straightforward format to make Data Analysis easy.|
|Companies assign technicians and Data Engineers for unifying and maintaining data from multiple data sources to shared storage and make it available to its users.||Business Analysts and entrepreneurs use Data Mining to simplify Data Analysis and identify patterns.|
|There is always a probability of data loss and permanent erasure. Data Warehousing also consists of the accumulation of irrelevant and useless data.||The Data Mining process should be handled with care because it involves the possibility of data leaks and piracy. Also, the process is not always entirely accurate.|
|Data Warehousing help companies store vast volumes of historical data and analyze trends for making future predictions.||Data Mining allows companies to make smarter business decisions by equipping relevant and easily accessible data.|
- Data Warehousing is managing and orchestrating data into Data Warehouses for storage and Data Analysis according to business requirements. While Data Mining is used to perform Data Analysis on data from Data Warehouses and identify patterns.
- Data Warehousing helps run operational business activities such as integrating Data Warehouse with CRM systems or SaaS applications. In contrast, Data Mining involves analyzing the Sales, Marketing, Finance, etc., data and creating a suggestive pattern for customer behavior, future trends, etc.
- Data Warehousing requires Data Engineers and technicians, but Data Mining needs business users and engineers.
- Data Warehouse works on unifying data, while Data Mining works on exploring and extracting insights from data.
- Data Warehousing can be performed using automated Data Pipeline tools, while Data Mining is mostly the manual process.
- Data Warehousing is complex and time-consuming to maintain, whereas Data Mining depends on user requests, and workload increases as the complicated queries are asked.
In this blog post, you read about the main differences between Data Warehousing and Data Mining. Both the operations are widely used by companies and serve the common purpose to help companies grow. Data Warehousing is the prior stage of Data Mining, and it is essential for reducing the complexity while analyzing data.