Data Mining vs Data Warehouse
Data Warehousing and Data Mining are two important yet often confused concepts. Let first understand the terms.
What is Data Warehousing?
It is a technique to collect and manage a huge volume of data from various sources to get meaningful information or business insights. It uses advanced technologies and queries to allow strategic use of data. Data Warehousing allows the data to be transferred into information and makes it available for the users.
What is Data Mining?
As the term signifies, Data Mining is the process of finding hidden, valid or potentially useful information in large sets of data. Data Mining is simply finding more/previously unavailable relationship between the data. Data Mining is a multi-disciplinary concept that uses ML, AI, Statistics and Database technology to find more relationship among the data.
So, now we know what Data Warehousing and Data Mining means. Let us see the differences
| Data Warehousing | Data Mining |
|---|---|
| A Data Warehouse is a database system designed for analytical work | Data Mining is a technique to find unknown patterns in data and analyze them |
| Data Warehousing collects data from various sources and stores it in a common repository | Data Mining technique compares large amounts of data to find right patterns |
| Data Warehousing is the step before Data Mining and is done by database experts | Data Mining is done by business users with assistance of architects or engineers |
| Data Warehousing is the process of combining all the relevant data together | Data Mining is considered as extracting data from large data sets |
| Data Warehouse can update itself regularly and consistently, so it is ideal for best and latest features | Data Mining detects and identifies the errors in the system |
| Data Warehouse adds value to the operational business systems during integration | Data Mining creates patterns of customer behavior, sales, products etc. so that the organization can make necessary adjustments on the operation and production |
| In Data Warehouse there is a great possibility that the relevant data may not be available for analysis and will easily lead to loss of information | Data Mining are never 100% accurate and may cause serious consequences |
| Data Warehouse involves high maintenance and will impact the revenue of small-medium scale organizations | Data Mining comes with a high risk that the information gathered can be used against a group of people |
| Data Warehouse stores a large amount of historical data which helps to analyze through a time line and make future predictions | Data Mining can be used to get usable knowledge-based information when equipped with pertinent data/information |
| In Data Warehouse data are pulled from various sources, so cleaning the data can be challenging | In Data Mining different algorithms are employed, so organizations need to spend a lot in training and implementation |
| In Data Warehouse users need to input the raw data | In Data Mining users need to create algorithms/queries to search for unknown patterns |
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