Data warehousing refers to a typical procedure of compiling and organising data into a common database. On the other hand, data mining basically refers to the process of extracting useful data from various databases.
Please note that the data mining procedure entirely depends on the data that is compiled within the data warehousing phase. This helps in recognising all the meaningful patterns of the data. In fact, data warehousing is formulated to provide aid to management systems.
This in-depth guide to data warehousing and data mining will give you valuable insights into the features, applications, and more. Please keep reading until the end of the article to learn about the benefits, differences, and other things associated with data mining and warehousing. Let’s find out!
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What is Data Mining?
In the process of data mining, data is carefully extracted and analysed to fetch nothing other than useful information. Here, all the hidden patterns are researched from the dataset, thereby predicting future behaviour. Besides, it can seamlessly indicate and discover unique relationships through the data.
On the other hand, data mining makes the best use of artificial intelligence, statistics, machine learning systems, databases, etc. It is used for figuring out the hidden patterns within the data. Additionally, it also supports all business-related queries that take loads of time to resolve.
What is Data Warehousing?
A data warehouse is nothing but an electronic storage that stores gigantic amounts of business information. It is exquisitely designed for both query and analysis rather than processing transactions.
Data warehousing is a unique technique that helps collect and manage data from various sources. Furthermore, these data provide meaningful and valuable business insights. It is basically a blend of components and technologies that enables strategic usage of data.
In a nutshell, data warehousing is a process of transforming data into information, thereby making it widely available for analysis. Now, let us discuss the important differences between data mining and data warehousing in detail.
Features of Data Warehouses
Here is a comprehensive listing of the most common features of data warehouses:
Numerous heterogeneous sources are compiled together to formulate a data warehouse like social databases, level documents, etc.
- Subject Oriented
It offers the most important data regarding particular subjects like products, suppliers, customers, promotions, etc. It handles data analysis and modelling while assisting organisations in making data-driven decisions
The previous data remains intact whenever new data is added to the data warehouse. Since the operational database and data warehouse are kept separately, changes within the operational database aren’t shown.
Data collected within the data warehouse is seamlessly identified within a particular period.
Features of Data Mining
Some of the unique features of data mining are:
- It is capable of predicting future results
- It can efficiently handle large datasets and databases
- It can seamlessly utilise the automated discovery of patterns
- It has the potential to create actionable insights, etc.
Applications of Data Mining
The applications of data mining include:
- Education Sector
- Market Basket Analysis
- Business Transactions
- Intrusion Detection
- Scientific Analysis
- Finance and Banking Sector
- Insurance and Healthcare
Applications of Data Warehouses:
The applications of data warehouses include:
- Banking Services
- Consumer Goods
- Financial Services
- Retail Sectors
Benefits of Data Mining
Here, we have listed a few potential benefits of data mining:
- Analysing trends within the existing marketplace.
- Detecting typical frauds in phone calls, insurance claims, debit or credit purchases, etc.
- It can make easy predictions within the market before making business decisions.
Benefits of Data Warehousing
The most compelling advantages of data warehousing are:
- Improved performance and productivity
- Consistent and accurate data access
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Difference Between Data Mining and Data Warehousing
Now, we have understood what is data mining and data warehousing in detail. Here we have listed down the difference between data mining and data warehousing:
|This procedure involves analysing data patterns
||It is exquisitely designed for analytical analysis
|Regular data analysis
||Periodical data storage
|Uses pattern recognition logic to identify patterns
||Extracts and stores data to enable easy reporting
|It is carried out by business users with the help of engineers
||It is carried out by engineers
|It helps in extracting data from large data sets
||It pools all the relevant data together
|Statistics, AI, Machine Learning, and Databases are used in data mining technologies
||Integrated, subject-oriented, non-volatile, and time-varying constitute data warehouses
|Pattern recognition logic is used for determining patterns
||It involves extracting and storing data in perfect order to make efficient reporting
|Employs pattern recognition tools to help identify the access patterns
||Extracts are stores data in an orderly format, thereby making reporting faster and easier
|It helps in creating suggestive patterns of key parameters
||When connected with operational business systems like CRM, it deliberately adds value to it
Common Tools and Software Used in Data Warehousing and Data Mining
Let’s look at the common tools and software used in data warehousing and data mining: Some of the popular data warehouse tools are:
- Amazon Redshift
- Microsoft Azure
- Google BigQuery
- Micro Focus Vertica
- Amazon DynamoDB
Some of the popular data mining tools are:
- IBM SPSS Modeler
- Oracle Data Mining
- Apache Mahout
- SAS Enterprise Miner
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Common Data Mining and Data Warehousing Techniques
Let’s look at the common techniques used in data warehousing vs data mining:
The most common techniques of data mining are:
- Data Visualisation
- Data Cleaning
- Machine Learning
- Neural Networks
- Data Warehousing
- Outlier Detection
Common Data Warehousing Techniques
Some of the most common data warehousing techniques are:
- Database Compression
- Columnar Data Storage
- In-Memory Processing
- Massive Parallel Processing (MPP)
Scope of Data Mining & Data Warehouse
The scope of data mining vs data warehousing is different from each other. Data mining involves sorting enormous data sets to identify relationships and patterns that can easily solve business problems through data analysis. The scope and techniques of data mining enable enterprises to predict future trends and make informed business decisions.
On the other hand, the scope of data warehousing lies within any domain that has something to do with analytics. Now, let us discuss the differences between data mining and data warehousing challenges faced.
Challenges of Data Mining & Data Warehousing
Some of the most common challenges of data mining vs data warehouse challenges:
Some of the most common challenges experienced by data mining are:
- Incomplete and noisy data
- Social and security challenges
- Complex data
- Distributed data
- Efficiency and scalability of algorithms
- Incorporating background knowledge
- Improving mining algorithms, etc.
Some of the most common challenges experienced by data warehousing are:
- Manual data processing
- Data quality
- Data Accuracy
- Performance, etc.
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In this article we have covered the major differences between data mining and data warehousing.